Detection and Categorization of Scoliosis Using CNN and SVM Algorithms
Authors -Pratik Shrestha, Aachal Singh, Ishika Sarraf, Riya Garg, Mahesh TR
Abstract- -Scoliosis is one of the most common diseases that is thrown under the radar. The lateral curvature and rotation seen in the vertebrae of the spinal column is classified in mostly 2 types: C-Curve and S-Curve. People need to be aware of the early signs and symptoms so that it is diagnosed in the proper time and manner. Scoliosis is mostly diagnosed and identified by taking X-ray medical images of the spine and on the basis of the sideways curvature image modality. In traditional scoliosis diagnosis detection, the treatment is based on spinal assessment which a manual study consisting of major limitations mainly is being very tedious, time-consuming and cost effective. Till date, without the assistance of technology, scoliosis diagnosis has been a critical task in the beginning because it all depended on the patient history records or even the captured x-ray images of the patient. Hence to ease up the process and provide better treatment experience, our research work is focused on categorizing the type of scoliosis in an effective, accurate and reliable way. This is obtained by analyzing and processing the input X-ray images obtained from the data-sets and even the patients suffering from scoliosis. To overcome the aforementioned limitations, we developed a point-based automated method at different regions of the spinal cord resulting in accurate results using the Convolutional Neural Network (CNN) algorithm and further compared it to Support Vector Machines (SVM) for better understanding.
A Survey on Cloud Computing and Security Methods
Authors -Megha Sharma, Asst. Prof. Sumit Sharma
Abstract- -Computing in the cloud is a form of internet-based computing that represents the subsequent stage in the development of the internet. In recent years, it has received a significant amount of attention, but one of the most significant factors that is slowing down the growth of cloud computing is the concern over security. However, because of this one aspect of cloud computing, there are many security issues that must be resolved and clearly understood. This article provides a comprehensive review of the various concerns regarding IAAS security. To protect one’s data and one’s privacy, a virtual machine needs to have adequate security. Methods that have been suggested by a variety of academics and can either directly or indirectly improve the cloud’s security are discussed here. Tenant-based measures were also considered to be potential solutions to the many problems that arose. A paper has outlined some of the trust techniques developed by researchers for determining whether or not a machine is malicious.
Butterfly Particle Swarm Optimization Algorithm for Cloud Data Retrieval
Authors – Astha Jain, Prof. Rajesh Nigam
Abstract- – Availability of digital data on cloud increases day by day. Out of different type of content arrangement and fetching of document files or text files are tough. This paper has developed a model that fetch document file from the clustered data structure. Arrangement of document in cluster data structure was done by Butterfly Particle Swarm Optimization algorithm. As hybrid nature of this algorithm has improve the work performance of cluster document arrangement. Term features were extracted from the document for finding the fitness of the chromosome in the population. This paper has fetch document file in encrypted form. Experiment was done on real dataset and result shows that proposed model content relevancy is high as compared to existing works of text file fetching.
Optimization Of Process Parameters In Extrusion Of Aluminium Alloy
Authors – M.Tech. Scholar Atul Kumar,Prof. Mayank Mishra
Abstract- – Recently extrusion processes have been used to make a wide range of metal products, including bars and tubes and strips and solid and hollow profiles, that are usually long, straight, semi-finished metal products.Analysis of temperature and extrusion load during in the extrusion of aluminium 2024 alloy was conducted in this study. This was determined that the best set of extrusion variables again for selected answers was discovered. Based on Taguchi’s L9 design matrix, the experiments were conducted out. ANOVA is used to determine which variables have a significant influence on the responses. Additionally, the percentage of each variable’s contribution to each response was graphically depicted. The Taguchi optimization method was used to find the best set of process variables based on the lowest S/N ratio. All of these ideal process variables aid in efficiently extruding the selected aluminium alloy at a low temperature and using the least amount of extrusion force possible. As a result, the extruded product’s quality was enhanced while using minimal energy..
Survey of Energy Aware Cluster Head Selection Techniques in Wireless Sensor Network
Authors – M. Tech. Scholar Rajesh Rekwal, Associate Professor Reshma shivhare,
Abstract- -Wireless sensor networks (WSNs) have recently gained a lot of popularity as a result of their low cost and the ease with which they can be maintained and managed. The network is made up of a collection of sensor nodes, each of which may perform sensing, computation, and transmission functions independently. WSN faces one of the most significant and difficult challenges in the area of energy efficiency. Sensor nodes have insufficient energy and are often put in far-flung locations. As a result, recharging the batteries in WSN is a challenging endeavour. As a result, proper clustering strategies and cluster head (CH) selection procedures have to be put into practise in order to achieve the greatest possible extension of the lifespan of the network. After first clustering the sensor nodes and concurrently reducing the data that has been constructed, the clustering approach then broadcasts the data. This is the primary concept that underpins the clustering technique. The selection of CH is an important component of this procedure. Therefore, this survey study gives an overview of the clustering approaches for lowering energy consumption by evaluating numerous CH selection techniques in WSN that provide excellent energy efficiency. These techniques can be found in the previous sentence. For the purpose of CH selection, a variety of methodologies, including partitional clustering, optimization, low-energy adaptive clustering hierarchy, hierarchical, distributed, and other classification strategies, have been used. In the end, an analysis is carried out based on the implementation tools, metrics used, accuracy, and successes of the various CH selection approaches that were taken into consideration.
Safe Vehicular Ad hoc Network: A Survey
Authors – M.Tech. Scholar Rajendra Thakur, Assistant Professor Megha Jat
Abstract- -The next generation of vehicle networks is called VANET, and its applications will play a crucial role in ensuring the safety of human lives when they are travelling on highways. When implementing VANET in a practical context, security is one of the most important and obvious elements to consider. Various researchers have previously provided a variety of solutions to the problem, which aims to make it safe against attackers and assaults on networks. In this overview study, explore in depth the many computing approaches that are available, and explain how they relate to vehicular networks. Employing these computational strategies to protect the vehicular network against attackers and assaults. Computing techniques such as trusted computing and cloud computing are examples of some of the several kinds of computing approaches that have previously been covered in VANET. However, there are still other computing methods, such as quantum computing and pervasive computing, that need to debate their connection with VANET and the security of the network.
Lower Tapi River Basin Delineation using Geographical Information System (GIS)
Authors – Asst. Prof. Bhumika D. Mistry
Abstract- – TWatersheds are natural hydrological feature that cover a specific aerial expanse of land surface from which the rainfall – runoff flows to a defined channel, drain, channel or river at any specify point. Catchment area and watershed delineation is common task in Water resources engineering and hydrology. DEM is spatial grids which are used to automatic delineation of watershed boundary determination. DEM based Arc-Hydro model was run on the dataset of the Lower Tapi river basin. Several intermediate results were produced while model run and basic parameter of the Tapi River, its catchment area has been defined at the end of model. The result of this study area can be useful in Rainfall-Runoff analysis and other advance research technology on the catchment area or river basin. Adding to this, it would have support for decision making on ground as well as surface water resource, distribution and management. This study of delineated watershed is further used to calculate hydrologic features by SCS-CN technique for developing a Rainfall-Runoff model in MATLAB. It is observed that the study area i.e., Lower Tapi basin is well drained and the drainage is in a well-integrated pattern. Current Study demonstrated that GIS is found to be flexible technology and is relatively easy to apply on large scale areas enabling gathering of data with information in a common data base for watershed delineation analysis and stream network process.
Design & Simulation Of Om Shape Antenna For 4g Applications
Authors – Research Scholar Sonia, Asst.Prof. Gaurav Nagpal
Abstract- – This research article reports a new Dielectric Resonator Antenna (DRA) with its Dielectric resonator (DR) modified to an “OM” shape for UWB (3.1-11.1 GHz), to support high data rate multimedia applications for 4G/5G communications. The proposed DRA reports a peak gain of 7.68 dB and a dual polarization behavior for a frequency band from 6 to 11.1 GHz. It has overall antenna dimensions of 50 × 40 × 4.87 mm3 and is fabricated on a commercially available Rogers RT 5880 substrate (with εr = 2.2), which is fed using a microstrip feedline with a P-type transformer that offers an input impedance of 50 Ω to the DR. A conformal strip between the feedline and the OM shaped DR improves the impedance matching at the UWB frequency response of the DRA. The proposed DRA therefore exhibits an elliptically polarized behavior with axial ratio bandwidth of 5.1 GHz (≤10 dB) from 6 to 11.1 GHz. A measured impedance bandwidth of 5.25 GHz from 3.8 to 9.05 GHz and 1.5 GHz from 10 to 11.5 GHz and a peak-measured gain of 7.68 dB at 10.5 GHz (with an average gain of 4.6 dB) has been reported for the proposed DRA. An UWB performance, with good gain properties and an elliptically polarized behavior allows the proposed “OM” shaped DRA to be suitable for short range 4G/5G UWB wireless communications for future multimedia rich WPAN (wireless personal area networks), WLAN, Wi-MAX, INSAT applications, satellite applications, and X band RADAR (for defense communication) applications.
Influence of Welding Process on Microstructure & Mechanical Properties of Zinc Brasses
Authors – M. Tech. Scholar Sachin Kumar Varshney, Prof. Dr. Amit Gupta, Asst. Prof. Dr. Sono Bhardawaj
Abstract- – Dissimilar lap welded joints of copper and brass metals were fabricated by friction stir welding (FSW) method at various welding heat inputs. The effect of welding heat inputs on the microstructure and mechanical properties of overlap welded joints at at two different join configurations (i.e. Advancing side and Retreating side joint configurations) was investigated. In both joint configurations, copper and brass plates are located on the top and bottom plates, respectively. Tensile-shear and vicker’s micro hardness tests were conducted to evaluate the mechanical properties of dissimilar lap welded joint. In order to analysis of microstructure and fracture surface of lap welded joints, optical microscope (OM) and scanning electron microscope (SEM) were used. The obtained results showed that the weld surface of samples was appeared without groove defects, low superfluous flash and oxidation, when the welding heat input is increased. Onion ring pattern characterized by the stack of copper and brass metals is identified in the weld nugget zone (WNZ) where metal flow structures can be observed. With decreasing welding heat input, tensile-shear strength increased at both joint configurations. The highest hardness was exhibited in the WNZ with increasing welding heat input in both joint configurations.
Performance Enhancement of Wireless Sensor Network Using GWO Algorithm
Authors – M.Tech. Scholar Km Anuradha, Asst. Prof. Asst. Prof. Hemlata
Abstract- – The enormous potential for sensor networks to link the real world with the virtual world has skyrocketed the interest in Wireless Sensor Networks (WSN). Due to the fact that many sensor devices use battery and node energy, changing them might be a challenge. As a result, increasing the energy efficiency of these networks, or their “lifespan,” becomes critical. A fuzzy logic controller is used to increase WSN energy efficiency through clustering and the selection of cluster heads. On the basis of network longevity, it does a comparison of the various ways. It compares the Gray wolf optimization (GWO) algorithm with the current ABC optimization approach for various network sizes and scenarios. It offers a high-performing and computationally simple approach for selecting cluster heads. In addition, it offers GWO as a clustering technique for WSN, which would increase performance and convergence more quickly.
Object Tracking in Videos Using Filtrations Technique
Authors – M.Tech. Scholar Yogita, Asst. Prof. Divya
Abstract- – To insure the safety of people in public as well as domestic places, surveillance cameras are being installed everywhere such as in banks, malls, markets, academic institutions, parking and most importantly on the roads where the traffic, both pedestrians as well as on wheels, is present 24×7. Different situations are captured by surveillance cameras installed at different places, like accidents on the roads are handled by cameras installed on the roads or traffic lights and crimes are handled by cameras installed in the household or streets or colonies and especially illegal activities happening in hotels or public places. The Particle Filters are suitable for object tracking in non-Gaussian environments with dynamic background thereby outperforming the conventional Kalman Filters; the third approach proposes the novel Branching Particle Filters that removes the limitations of particle filters.
Modeling Battery Monitoring System (Bms) On Photovoltaic Based Moving Average And Autoregressive Integrated Moving Average Model (Arima)
Authors – Ahmad Muhtadi, Bambang Sri Kaloko, TriwahjuHardianto
Abstract- – Battery Management System (BMS) is a tool used to monitor and manage battery conditions. In battery modeling to engineer batteries, it is necessary to identify the parameters. Accurate modeling and identification of parameters are required to create a reliable BMS system. In this study, battery modeling was carried out using a moving average and ARIMA model because it was able to predict the shape of the data to resemble its original form and was significant. Meanwhile, the identification of parameters is carried out using test data that has been carried out on a dataset of power consumption for 1 year. The results obtained by the ARIMA model which has the best accuracy for predicting BMS, namely the MSE test: 0.076 with a relatively small error deviation value.
Integrated Approach of Abrasive Jet Machining and Magneto Finishing: A Review
Authors – M. Tech. Scholar Vimal Kumar Saini, Asst. Prof. Sono Bhardawaj, Prof. Amit Gupta
Abstract- – In this paper, we will discuss the effect of hybridization of abrasive jet machining and magneto finishing. The latest hike in the usage of hardness, highest potency & temp. resisting equipment in technology imposed the progress of new-fangled machine operation process. Traditional machine operation or concluding processes are not again related to the substances like carbides; ceramics. Traditional machining procedures whenever concerned to such new-fangled substances are too costly, Create reduced degree of surface finish and precision; generate some stress, extremely deficient. New-fangled machining procedures may be categorized due to temperament of energy in work. AFM is somewhat newest procedure along with non-traditional machined Technique. Low substance elimination rate occurs to be one serious inadequacy of nearly the entire procedures. MAFM is an innovative expansion in AFM. By means of magnetically fielding in the region of the work portion in AFM, we can amplify the material removal rate in addition to the plate finishing.
Influence of Welding Process on Microstructure & Mechanical Properties of Zinc Brasses: A Review
Authors – M. Tech. Scholar Sachin Kumar Varshney, Prof. Dr. Amit Gupta, Asst. Prof. Dr. Sono Bhardawaj
Abstract- – The friction stir welding (FSW) method was used to construct lap welded connections of copper and brass metals with different welding heat inputs. At two different joint configurations (Advancing and Retreating side), the microstructure and mechanical characteristics of overlap welded joints were studied to determine the effect of welding heat inputs on the microstructure and mechanical properties. Copper and brass plates are situated on the top and bottom plates, respectively, in both joint arrangements. The mechanical parameters of a dissimilar lap welded joint were evaluated using tensile-shear and vicker’s microhardness tests. Optical microscope (OM) and scanning electron microscopy (SEM) were used to examine the microstructure and fracture surface of lap welded joints. Increasing the welding heat input resulted in the weld surface of the samples not showing any groove flaws, low unnecessary flash, or oxidation, according to the data. The weld nugget zone (WNZ) is defined by an onion ring pattern characterized by the stack of copper and brass metals. Strength in both tensile and shear was enhanced by reducing welding heat input. In both joint designs, the WNZ displayed the maximum toughness with increasing welding heat input.
Integrated Approach of Abrasive Jet Machining and Magneto Finishing
Authors – M. Tech. Scholar Vimal Kumar Saini, Assistant Prof. Sono Bhardawaj, Professor Amit Gupta
Abstract- – In this paper, we will discuss the effect of hybridization of abrasive jet machining and magneto finishing. New-fangled machining procedures may be categorized due to temperament of energy in work. AFM is somewhat newest procedure along with non-traditional machined Technique. Low substance elimination rate occurs to be one serious inadequacy of nearly the entire procedures. MAFM is an innovative expansion in AFM. By means of magnetically fielding in the region of the work portion in AFM, we can amplify the material removal rate in addition to the plate finishing. In the current effort Al-606were punctured & exhausted by customary machine operation capacity & surfacing finished up were made by methods for rough stream machining. Testing was grasped for information requirements like rough pondering, grating system degree and no of cycles. The yield counter is substance disposal pace.
Design Of Microgrid Energy Management system At nurul Jadid Islamic Boarding School Proboling go Indonesia
Authors – Novangga Adi Mulyono, Bambang Sri Kaloko, Bambang Sujanarko
Abstract- – Along with developments in technology, the demand for electric power is increasing so that electrical energy providers are required to provide an adequate supply of electricity. However, the electrical energy distributed to consumers still uses fossil energy and gradually the amount of fossil energy will be depleted. Solar cells utilize solar energy as renewable energy that can be maximized in Indonesia. The operation of solar cells on a microgrid needs to be evaluated and optimized to achieve a reliable but still efficient performance. This paper develops an energy management model for microgrid optimization. The power sources connected to the microgrid consist of the PV mini-grid system, battery system, and electricity from the public grid. The design resulting from this research is able to optimize energy management. So that the use of electrical energy is dominated by solar panels. The percentage of solar panel energy use increases to 75% and the cost of electricity generation decreases).
A Survey on Medical Image Disease Diagnosis Features and Techniques
Authors – Kanupriya Chouksey, Dr Sunil Phulre, Dr Sadhna Mishra
Abstract- – Now a days number of diseases are mostly found in animals, plants and humans. Almost in all ages of human beings infections related to brain, skin, stomach, etc. diseases can occur. So, it becomes necessary to identify these diseases at the very primary stage to control it from spreading. In this paper a deep survey of various approaches of image diagnosis was discussed. Common steps of image diagnosis was shown in the paper. he concepts, benefits, risks and applications of these techniques will present with details. Features used by the scholars for the segmentation of image was also list in paper.
G+12 Building Design and Performance Parameters Optimization Using ANOVAs Method
Authors – ME Student Brijesh Pandey , Prof. Rajeev Chandak
Abstract- – Unique structures require more time for analysis and design due to their time-consuming calculations, if we use manual methods it will require more time for their calculations along with more probability of errors. STAAD Pro is a computer aided software which is used for solving complex engineering problems and provides us a quick results. STAAD Pro is a user friendly, easy to use software which gives us results pretty quickly along with fair level of accuracy. It is used for analysis and design of any structure with its wide scope of problem-solving tools. STAAD Pro has wide scope of sections, material properties, different Codal Provisions inbuilt commands along with user defined assignments. In India, STAAD Pro is used as per Indian Standard and Practices while it gives freedom for the consideration of different countries codes which are applicable to different regions around the globe. We can conclude that this software can save much time and very accurate in analyzing and designing of different structures along with being accurate. An ANOVA method is a statistical method used to find out if the results obtained are significant. In this project, a G+12 Building model is prepared and analyzed with the help of STAAD Pro by considering the various loads viz. dead, live, combination. The results are studied and compared by manual calculations. ANOVA Test is then used in order to find if the results are significant with the help of Microsoft excel. In the STAAD Pro the designing is done by better way for creating Geometry, defining the cross sections for column and beams etc, creating specification and supports according to the requirement. Then the Loads are defined with the help of loads envelop which is available in the loads and definition tab of STAAD Pro. After applying all the loads, the model is checked for any error, and the model is analyzed through ‘run analysis’ tab. Then the results are obtained by post possessing of the model. The results obtained are then taken into Microsoft excel for the ANOVA test. The analysis of variance is then applied with the help of data analysis in the excel software and the results are interpreted.
To Investigate Optimum Design of Fins Model for Better Heat Transfer by using ANSYS Software
Authors – M.Tech. Scholar Gaurav Pratap Singh, Asst. Prof. Saumitra Kumar Sharma
Abstract- – The Engine cylinder is one of the essential engine components that are subjected to inordinate temperature variations and thermal stresses. Fins are put on the surface of the cylinder to upgrade the rate of heat exchange by convection. The present investigation explains the reviews on heat dissipation augment and the comparable pressure drop over a plane surface, pin fins distinctive molded (circular, rectangular) fitted with cylindrical cross-sectional in order to enhance the change in the heat exchange rate. In heat dissipation applications, heat sink is utilized. More previous style heat sinks are frequently deficient for cooling latest and more blazing running parts extended surfaces, for example, blades increment the heat dissipation rate perforations in the fins additionally builds heat dissipation rate. One of the essential components of the engine chamber is the engine chamber that is exposed to high temperature and heat stress. The fines are placed on the cylinder surface to improve the amount of convection heat exchange. Heat is created when the fuel is burned in a motor. The friction between the moving parts often creates additional heat. Extended surfaces called fins are provided in the air-cooled I.C engine on the periphery of motor cylinders to improve heat transfer. Therefore, fin analysis is important in order to improve the heat transfer rate. The main objective of this work is to study different studies carried out in the past to increase the cooling fine heat transfer rate by adjusting the geometry and material of the cylinder fin. In this work the temperature variations of fins produced throughout four geometries (fins of plates, circular pins, holes and pipe fins), as well as a clear state heat exchange study have been tested using ANSYS software to test and authorize performance, respectively The experiments have been carried out to detect temperature changes in fins. The variations on temperatures in different fine models in the field are measured by FEM and compared the fine performance models using the experimentally generated heat flow and the temperature variations in Ansys. In this project, the idea is to increase the rate of heat dissipation by using the wind travel. The main objective of the study is to improve the thermal properties by changing geometry, material and fine design.
A Review of Flexible Pavement Using CBR Method
Authors -M.Tech. Scholar Harshit Saxena, Asst. Prof. Anuj Verma, Asst. Prof. Mohd Rashid
Abstract- – To insure the safety of people in public as well as domestic places, surveillance cameras are being installed everywhere such as in banks, malls, markets, academic institutions, parking and most importantly on the roads where the traffic, both pedestrians as well as on wheels, is present 24×7. Different situations are captured by surveillance cameras installed at different places, like accidents on the roads are handled by cameras installed on the roads or traffic lights and crimes are handled by cameras installed in the household or streets or colonies and especially illegal activities happening in hotels or public places. The Particle Filters are suitable for object tracking in non-Gaussian environments with dynamic background thereby outperforming the conventional Kalman Filters; the third approach proposes the novel Branching Particle Filters that removes the limitations of particle filters.
A Review of Partial Replacement of Fine Aggregate with Demolished Waste
Authors – M. Tech. Scholar Zubair Ahmad Dar, Asst. Prof. Anuj Verma
Abstract- – Waste creation will also increase as a result of the rapid development of industries and concrete regions, which is having a detrimental impact on the environment. Due to ongoing infrastructure development, emerging nations such as India require massive amounts of construction materials while also producing massive amounts of construction and demolition waste on a yearly basis. This waste disposal could have major consequences since, on the one hand, it takes up a lot of space and, on the other hand, it pollutes the environment. Natural resources such as stone, sand, and other resources must also be conserved and preserved. Another big disadvantage is the continual use of natural resources such as sand and streams, which deepens the streambed and generates draughts while also changing the weather. As a result of the housing industry’s substantial use of natural resources and waste production, there is growing concern for our planet’s future.
A Review On Ecg Signal Noise Reduction And Performance Enhancement Of Signal Parameters In Pqrs Detection
Authors -Shobha Parashar, Assistant Professor Hemant Amhia
Abstract- – As we know current era is totally based on innovative technology. Medical science is one of the important needs for every human being. As we know ECG signal processing has become a effective tool for research and medical practices. A typical computer based ECG analysis system includes a signal pre-processing, beats detection and feature extraction stages, followed by classification. Automatic identification of arrhythmias from the ECG is one important biomedical application of pattern recognition. As we also know in current era heart is diagnosed is done with the help of ECG. Here ECG signal is recording form of electrical activities which is generated by human heart. Now some time due to some electrical issue may be there is chances of generation of some wrong information of ECG signal which is really too much dangerous for the patient. So there is need of some effective filtering applications which will filter the output of ECG signal and generate the real ECG signal which will help for human health diagnosigation. So in this paper we present the comparative study between all existing filter.
A Review Article of Adaptive ECG Signal Time Frequency Analysis and Signal Quality Assessment Using AI
Authors – Swapnil khare, Assistant Professor Hemant Amhia
Abstract- – This review paper deals with the study and analysis of ECG signal processing by means of various tools ,techniques and algorithms. Study of ECG signal includes generation & simulation of ECG signal, acquisition of real time ECG data, ECG signal filtering & processing, feature extraction, comparison between different ECG signal analysis algorithms & techniques (i.e. discrete Wavelet transform or so), detection of any abnormalities in ECG and so on. Finally, in the concluding part about its technological implementation using various small, inexpensive and easy to use devices.
Review EMG Signal Muscle Activity Detection and Wavelet Spectrum Matching Using DB Transform Method
Authors – Rakshit Bhagat, Assistant Professor Hemant Amhia
Abstract- – Purpose of this research paper is to Review identify the Electromyography Signal from muscle activation Signal. Electromyogram (EMG) is a complex signal, which was collected by nervous system depends on the anatomical and physiological properties of the muscles. The EMG signals were collected from the laboratories and experimentally recorded using surface electrode of healthy, myopathic and neuropathic subjects. These signals get corrupted by the noise while travelling through different tissues.
Hydrogen Fuel Based Public Transport for Delhi
Authors – Assistant Professor Anjali
Abstract- – Given the success of Delhi’s CNG vehicle program, energy stakeholders are now investigating a transition to hydrogen-compressed natural gas (H-CNG) blends in the city. Past research has shown H-CNG can reduce tailpipe emissions of both criteria and greenhouse gas pollutants relative to diesel, CNG, and gasoline. However, an unanswered question is how Delhi will satisfy the potential hydrogen demand in a sustainable manner. We conduct a techno-economic assessment of hydrogen production from gasification of the three most abundant agricultural residues near Delhi e rice straw, cotton stalk, mustard stalk e and find these residues could provide the city with up to 270,700 metric tons per year of hydrogen. This quantity far exceeds what is needed to run all existing CNG vehicles on 18%-82% H-CNG blends. The cost of each step of the bio-hydrogen supply chain is calculated and the total cost is estimated at 149.6 rupees ($3.39) per kg. Lastly, we show that the price of H-CNG at the pump would be roughly equivalent to CNG on a per mile basis.
Technological Advancements in Risk Management: From Predictive Analytics to AI
Authors – Chintamani Bagwe
Abstract- – Risk management is very critical for the success of a firm in the current fast-paced, dynamic, and complex business environment. This article discusses the implication of technology in risk management and highlights some examples of technological development that could reinvent risk process management. The article has explored the many benefits and potential benefits of utilizing technology in the risk management process. It also recommends getting suitable risk management software that can help an organization’s risk management processes.
DOI: /10.61463/ijset.vol.10.issue4.327
Machine Learning For Patch Impact Analysis In Red Hat
Authors: Harish Reddy, Meenakshi Das, Kavitha Murugan, Suresh Balan
Abstract: The increasing complexity and velocity of patch management in enterprise Red Hat environments necessitates a shift from traditional static testing to intelligent, predictive methodologies. Patch deployment especially involving kernel updates, shared libraries, or core packages can introduce performance regressions, configuration conflicts, or application downtime, particularly in mission-critical systems. This review explores the application of machine learning (ML) techniques to assess and predict the impact of patches before deployment. By analyzing system logs, resource metrics, historical incident reports, and patch metadata, ML models can provide proactive insights into risk levels associated with specific updates. The article outlines a multi-phase architecture for implementing ML-driven patch analysis, including data collection from Red Hat systems (e.g., journalctl, auditd, YUM logs), feature engineering, supervised and unsupervised modeling, and integration into continuous delivery pipelines. Special emphasis is placed on explainability, time-series forecasting, and the importance of retraining to accommodate evolving patch behaviors. The review also discusses challenges such as data sparsity, inconsistent logging formats, and model generalization across Red Hat workloads in production, development, and containerized environments. Future directions include reinforcement learning for patch sequencing, cross-platform federated learning, and AI-driven test orchestration. By embedding machine learning into patch management workflows, organizations can achieve more resilient, compliant, and efficient Red Hat operations while minimizing service disruptions and administrative burden
DOI: http://doi.org/10.5281/zenodo.15846870
Applying Natural Language Processing Techniques For Detecting Anomalies In Linux Server Logs To Enhance System Security
Authors: Meena Kandasamy
Abstract: Linux server logs serve as a vital source of data, offering detailed insights into system activity, performance events, error reporting, and potential security breaches. As modern computing environments generate immense volumes of log data every second, manual inspection becomes impractical, leading to delays in detecting anomalies and responding to system faults or attacks. Anomaly detection in Linux server logs using Natural Language Processing (NLP) has emerged as a powerful technique to automate this analysis. NLP treats logs as unstructured textual data and employs linguistic and statistical techniques to uncover patterns, classify messages, and identify deviations that may indicate underlying issues. By applying tokenization, normalization, vectorization, and contextual embeddings, NLP models can extract meaningful patterns from log data, effectively transforming textual logs into structured data that machine learning algorithms can process. This article delves into the comprehensive pipeline required for NLP-driven anomaly detection in Linux logs, encompassing data preprocessing, feature engineering, model training, evaluation, and real-time deployment. It outlines the challenges posed by log heterogeneity, noise, and imbalance, and presents robust solutions such as transformer models, unsupervised learning, and ensemble detection techniques. Furthermore, it explores how NLP-based anomaly detection is being integrated into industry-grade tools, cloud environments, and continuous monitoring systems to support real-time incident detection and resolution. The discussion highlights the evolving role of explainability, security, and scalability in these models and suggests directions for future research, including federated learning and AIOps integration. This work aims to equip system administrators, data scientists, and cybersecurity professionals with a practical understanding of how to implement, optimize, and benefit from NLP-based anomaly detection in Linux server infrastructures
Enhancing Container Security Using EBPF-Based Detection Mechanisms For Real-Time Threat Monitoring And System-Level Visibility
Authors: Perumal Murugan
Abstract: In the evolving landscape of cloud-native technologies, containerization has become a critical pillar for deploying scalable and agile applications. While containers offer efficiency and portability, they also introduce new security challenges that demand innovative detection and response mechanisms. Extended Berkeley Packet Filter (eBPF) technology has emerged as a transformative solution, offering deep observability into kernel-space activity without incurring performance penalties. eBPF-based detection mechanisms enable real-time monitoring of container behavior, allowing security teams to trace system calls, network traffic, and file I/O activities with unmatched granularity. This article provides a comprehensive exploration of container security architecture empowered by eBPF, emphasizing the significance of low-level introspection, telemetry capture, and in-kernel enforcement techniques. The integration of eBPF with modern orchestration platforms like Kubernetes is reshaping the proactive defense model, enabling automated threat hunting and anomaly detection. From identifying malicious workloads to enforcing security policies dynamically, eBPF is helping bridge the gap between runtime protection and compliance enforcement. The paper covers fundamental aspects such as eBPF program structure, common use cases in container security, integration strategies, performance implications, and real-world deployments in enterprise infrastructure. As container adoption accelerates, this article argues that eBPF will be a cornerstone technology in achieving resilient and adaptive security frameworks, facilitating a paradigm shift from reactive measures to proactive protection in dynamic cloud environments.
Developing Automated Self-Healing Scripts To Monitor, Detect, And Resolve Issues In Remote Server Management Environments
Authors: Sudeep Nagarkar
Abstract: In the ever-evolving landscape of IT operations, remote server management has emerged as a cornerstone of modern infrastructure. However, the increased complexity and scale of distributed systems have brought new challenges in maintaining reliability and uptime. This has paved the way for self-healing scripts—automated, intelligent programs designed to detect, diagnose, and resolve system anomalies without human intervention. These scripts leverage real-time monitoring, pattern recognition, and decision logic to address failures ranging from simple service outages to complex configuration drifts. By minimizing the need for manual troubleshooting and accelerating issue resolution, self-healing scripts enhance the resilience of IT ecosystems. They are especially critical in environments that demand continuous availability, such as cloud platforms, edge computing frameworks, and container orchestration systems. This article explores the architecture, components, implementation strategies, and best practices for deploying self-healing scripts in remote server environments. Emphasis is placed on real-world use cases, integration with existing tools like Ansible and Prometheus, and the use of machine learning models to predict and prevent faults. Additionally, this paper discusses potential challenges such as false positives, scalability, and security considerations. The goal is to provide a comprehensive understanding of how self-healing mechanisms can transform reactive maintenance into a proactive, automated, and intelligent operational paradigm.
Implementing Multi-Protocol File Sharing Solutions To Enhance Accessibility And Collaboration Across Academic And Research Network Environments
Authors: Anand Neelakantan
Abstract: In the evolving landscape of digital academia, the capacity to share large volumes of data across various platforms and operating environments has become a cornerstone of efficient research and educational collaboration. Multi-protocol file sharing presents an integrative solution that facilitates seamless data exchange between heterogeneous systems within academic networks. This paper explores the deployment of multi-protocol architectures—such as SMB/CIFS, NFS, FTP, and WebDAV—in educational institutions, where users operate diverse operating systems like Windows, Linux, and macOS. A deep dive into protocol interoperability reveals strategies for ensuring security, scalability, and ease of access while minimizing latency and operational bottlenecks. The study underscores the relevance of authentication frameworks, such as Kerberos and LDAP, in safeguarding sensitive research data without impeding productivity. Additionally, the implications of cloud integration and virtualization are addressed to align file sharing models with modern pedagogical practices. Case studies from universities adopting hybrid protocol models further highlight real-world benefits and operational pitfalls. This article provides system administrators, IT architects, and educational technologists with a comprehensive roadmap for implementing resilient, secure, and user-friendly file sharing environments in academia.
Cloud Based Approach Of Encryption And Decryption
Authors: Prabal Kumar Joshi
Abstract: It is taken care of precisely to very accurately to avoid any penetration to arrive at the first text. It tends to be used in companies or some other system; however, it takes a long time to encrypt it. To the first text when encryption to ensure the assurance of information in full and security. Encrypted text contains a unique key, even when stolen. The private key can't be decrypted by the specialist and licensed by the maker of the code in order to protect the data in an excellent manner. While demonstrating in addition much stronger security guarantees with regards to Differential/ direct assaults. Specifically, we are can to provide new Method Encryption and Decryption with strong bounds for all versions.
From RFID To Geofencing: IoT-Enabled Smart Time Tracking In Oracle HCM Cloud
Authors: Kranthi Kumar Routhu
Abstract: Time tracking has long been a cornerstone of workforce management, yet traditional approaches—manual entries, punch cards, and batch imports from legacy systems—have often been plagued by inefficiency, inaccuracies, and compliance risks. By 2023, the integration of Internet of Things (IoT) technologies with Oracle HCM Cloud Time & Labor (OTL) has transformed this function into a strategic capability. Smart attendance systems leveraging RFID, Bluetooth Low Energy (BLE) beacons, and geofencing now feed real-time data into Oracle’s ecosystem, where Web Clock geolocation, REST-based time event ingestion, and Fusion Analytics ensure both operational accuracy and governance alignment. Beyond efficiency gains, these innovations enhance employee trust and experience by enabling seamless, location-aware, and mobile-first time capture while supporting compliance with global labor standards. This article examines the evolution of IoT-enabled time tracking, Oracle’s role in embedding intelligence into the time and labor lifecycle, and the broader organizational implications for agility, accountability, and workforce resilience..
Salesforce AI Bots For Multi-Cloud CRM Automation With Tivoli Monitoring And Hybrid Unix Compliance Frameworks
Authors: Jaspal Brar
Abstract: The rapid evolution of customer relationship management (CRM) systems has driven enterprises to adopt advanced technologies that combine automation, monitoring, and compliance. This review explores the integration of Salesforce AI bots, multi-cloud CRM environments, IBM Tivoli Monitoring, and hybrid Unix compliance frameworks as a unified approach to modern CRM pipelines. Salesforce AI bots deliver automation and personalization, enhancing customer engagement across industries, while Tivoli Monitoring ensures performance reliability through proactive oversight of distributed workloads. Hybrid Unix compliance frameworks add a governance layer, enforcing regulatory standards such as GDPR, HIPAA, SOX, and PCI-DSS within multi-cloud environments. Through detailed analysis and industry case studies, the article highlights both the opportunities and challenges in aligning automation, observability, and compliance for enterprise-scale CRM. It also outlines future directions, emphasizing advancements in AI-driven natural language processing, predictive monitoring, and adaptive compliance models. By synthesizing these dimensions, this review underscores how enterprises can balance agility, resilience, and governance to achieve transformative outcomes in CRM modernization.
Modernizing Hybrid Unix Infrastructure For Salesforce CRM With VMware Virtualization And Einstein Copilot AI Enhancements
Authors: Gurinder Sekhon
Abstract: The rapid evolution of enterprise IT has placed new demands on infrastructure modernization, particularly in supporting advanced customer relationship management (CRM) platforms such as Salesforce. Traditional Unix systems, though renowned for their reliability, are increasingly challenged by the need for agility, scalability, and real-time intelligence. This review examines how hybrid Unix infrastructures can be modernized through VMware virtualization and enhanced by Salesforce’s Einstein Copilot AI. VMware provides a robust virtualization framework that optimizes resource utilization, improves resilience, and bridges legacy Unix environments with cloud-native services. Meanwhile, Einstein Copilot AI delivers contextual intelligence, conversational support, and predictive analytics that transform Salesforce CRM into an adaptive, insight-driven ecosystem. Together, these technologies enable enterprises to align infrastructure efficiency with intelligent CRM workflows, ensuring seamless customer engagement and operational resilience. The review further discusses challenges such as integration complexity, cost implications, compliance risks, workforce skill gaps, and AI scalability. It also explores future directions, including autonomous infrastructure management, AI-first CRM experiences, edge computing, generative AI applications, and unified compliance frameworks. By synthesizing these perspectives, the article argues that the combined adoption of VMware and Einstein Copilot within hybrid Unix environments represents not just a modernization strategy, but a strategic imperative for enterprises seeking long-term competitiveness in the digital economy.
Integrating Einstein Copilot With CTI And Omni-Channel Automation Across Hybrid Unix And Salesforce CRM Environments
Authors: Gilbert Rozario
Abstract: Hybrid enterprise infrastructures, encompassing legacy Unix systems, on-premises servers, and cloud services, present unique challenges for CRM integration and operational management. Effective monitoring and automation are critical to ensuring reliable execution of AI-driven Salesforce customer journeys. This review explores strategies for integrating Einstein Copilot with Computer Telephony Integration (CTI) and omni-channel automation across hybrid Unix and Salesforce CRM environments. It examines middleware solutions, API connectivity, workflow orchestration, predictive analytics, and automated remediation. Security, compliance, and governance considerations are discussed to maintain regulatory adherence and operational integrity. Case studies demonstrate tangible benefits in predictive engagement, workflow efficiency, and operational resilience. Emerging trends, including self-learning AI workflows, cloud-native monitoring, and unified observability, are also highlighted. By adopting these strategies, enterprises can achieve intelligent, scalable, and secure CRM operations, enhancing both customer experience and infrastructure performance.
Monitoring Hybrid Enterprise Infrastructure Using Nagios And Zabbix While Supporting Salesforce AI-Powered Customer Journeys
Authors: Helena Fernandes
Abstract: Hybrid enterprise infrastructures, encompassing legacy systems, on-premises servers, and cloud services, pose unique challenges for monitoring and operational management. Effective oversight is critical to ensure the reliability of AI-driven Salesforce customer journey workflows, which depend on accurate, real-time infrastructure data. This review explores strategies for monitoring hybrid environments using Nagios and Zabbix, highlighting complementary capabilities, integration techniques, and best practices. It examines middleware connectivity, data collection, visualization, predictive analytics, and automated remediation to maintain system health while optimizing customer engagement. Security, compliance, and governance considerations are also addressed, ensuring operational integrity and regulatory adherence. Case studies and practical implementations demonstrate the impact of integrated monitoring on proactive incident management, predictive maintenance, and AI-enhanced CRM operations. Emerging trends, including AI-driven anomaly detection, unified observability, and cloud-native monitoring, are discussed to provide a forward-looking perspective. By adopting these strategies, enterprises can achieve scalable, secure, and intelligent monitoring, directly supporting the performance and reliability of Salesforce AI-powered customer journeys.
System-Level Security Considerations for Cloud-Integrated Wireless IoT Networks
Authors: Samridhi Jaitora
Abstract: The widespread adoption of wireless Internet of Things (IoT) networks integrated with cloud platforms has enabled intelligent automation, real-time monitoring, and data-driven decision-making across multiple domains. However, this integration introduces significant security challenges due to the distributed architecture, heterogeneous devices, wireless communication vulnerabilities, and centralized cloud dependencies. This article examines system-level security considerations for cloud-integrated wireless IoT networks, focusing on comprehensive protection across device, network, edge, and cloud layers. Key security challenges such as device resource constraints, network-level attacks, cloud data breaches, and scalability issues are analyzed. The study discusses essential system-level security mechanisms including authentication, authorization, encryption, secure firmware updates, intrusion detection, and trust management. The role of cloud platforms and artificial intelligence in enhancing real-time threat detection, security orchestration, and adaptive defense strategies is also explored. Real-world applications in industrial IoT, smart cities, and healthcare highlight the importance of integrated security frameworks for maintaining data confidentiality, integrity, and availability. Finally, emerging trends such as edge-based security, lightweight cryptography, blockchain, and autonomous security systems are discussed, emphasizing the need for resilient, scalable, and adaptive security architectures to support the future growth of cloud-integrated IoT ecosystems.
DOI: https://doi.org/10.5281/zenodo.18161168
Distributed Intelligence Models For Cloud-Supported IoT Over Wireless Networks
Authors: Laksh Veridhan
Abstract: The rapid growth of Internet of Things (IoT) deployments over wireless networks has intensified the demand for intelligent data processing and real-time decision-making. Traditional cloud-centric intelligence models struggle to meet the requirements of large-scale, latency-sensitive, and privacy-aware IoT applications. Distributed intelligence has emerged as a promising paradigm that decentralizes learning, inference, and control across edge devices, fog nodes, and cloud platforms. This review presents a comprehensive analysis of distributed intelligence models for cloud-supported IoT systems operating over wireless networks. It examines system architectures, distributed artificial intelligence and machine learning techniques, wireless communication considerations, and cloud-assisted orchestration mechanisms. Key paradigms such as edge intelligence, fog intelligence, hybrid cloud–edge intelligence, and collaborative learning are discussed, along with enabling technologies including federated learning, distributed deep learning, and reinforcement learning. The review also addresses critical security and privacy challenges, performance evaluation metrics, and real-world applications spanning smart cities, industrial IoT, healthcare, and energy systems. Finally, open challenges and future research directions are identified, highlighting the need for scalable, adaptive, and secure distributed intelligence frameworks to support next-generation IoT ecosystems.
DOI: https://doi.org/10.5281/zenodo.18161274
Intelligent System Abstractions For Cloud And IoT Interaction In Wireless Spaces
Authors: Shaurya Nivaran
Abstract: The proliferation of Internet of Things (IoT) devices and cloud computing services has transformed wireless environments, creating unprecedented opportunities and challenges in data management, system integration, and real-time communication. Efficient interaction between heterogeneous IoT devices and cloud infrastructures requires intelligent abstractions that can simplify complexity while optimizing performance. This paper proposes a novel framework for intelligent system abstractions that enables seamless cloud-IoT interaction in wireless spaces. The framework leverages context-aware data processing, adaptive network management, and predictive analytics to provide real-time insights, improve resource utilization, and enhance system responsiveness. By abstracting the underlying heterogeneity of devices and communication protocols, the system allows developers to focus on high-level functionality without compromising efficiency or scalability. Experimental evaluations, including simulations and deployment in real-world wireless environments, demonstrate significant improvements in latency reduction, throughput, and robustness under variable network conditions. Additionally, the framework supports dynamic adaptation to changing workloads and environmental conditions, making it suitable for large-scale, distributed IoT-cloud ecosystems. The proposed approach contributes to the advancement of intelligent wireless systems by providing a scalable and flexible architecture that bridges the gap between IoT devices and cloud services. These findings highlight the potential of system abstractions to accelerate innovation, simplify development, and enable efficient deployment of complex IoT-cloud solutions across diverse wireless networks.
DOI: https://doi.org/10.5281/zenodo.18161357
End-to-End Architectural Assessment Of Wireless IoT And Cloud Computing Systems
Authors: Tanvika Malrenu
Abstract: The rapid advancement of Internet of Things (IoT) technologies and cloud computing has fostered the development of highly integrated systems that enable real-time data acquisition, processing, and intelligent decision-making. This review presents an end-to-end architectural assessment of wireless IoT systems and their integration with cloud computing platforms. It begins by exploring the fundamental components of IoT networks, including sensors, actuators, and communication protocols, highlighting the heterogeneity and scalability challenges that arise from diverse application scenarios such as smart homes, healthcare monitoring, industrial automation, and agriculture. The study further examines cloud computing architectures, service models, and deployment strategies that support the high-volume data processing and storage requirements of IoT ecosystems. A critical evaluation of end-to-end IoT-cloud architectures is conducted, focusing on the interactions between device layers, network layers, edge computing, and cloud processing layers. Key performance metrics, including latency, throughput, energy efficiency, and reliability, are analyzed to identify design trade-offs and optimization opportunities. Additionally, security and privacy concerns are addressed, emphasizing authentication, data protection, and secure communication mechanisms essential for safeguarding sensitive information. Emerging trends, including edge and fog computing, artificial intelligence-enabled IoT systems, and the integration of 6G technologies, are discussed as future directions that can enhance system performance and adaptability. By synthesizing current research, practical case studies, and technological innovations, this review aims to provide system designers, researchers, and practitioners with a comprehensive understanding of architectural considerations, challenges, and strategies for developing efficient, secure, and scalable wireless IoT and cloud computing systems. Ultimately, this work contributes to guiding future research and facilitating the deployment of robust IoT-cloud solutions across diverse domains.
DOI: https://doi.org/10.5281/zenodo.18161448
AI-Enabled Enterprise Information Services For Strategic Risk Assessment And Organizational Decision Making
Authors: Athish Gowda
Abstract: The contemporary corporate environment is characterized by unprecedented levels of volatility and complexity, rendering traditional, reactive risk management frameworks insufficient for long-term sustainability. This review article investigates the transformative role of AI-Enabled Enterprise Information Services (AEIS) in modernizing strategic risk assessment and organizational decision-making. We propose a multi-layered conceptual framework that integrates data hramonization, machine learning, and natural language processing to convert fragmented internal and external data into actionable strategic intelligence. The analysis highlights how AEIS facilitates dynamic risk identification by continuously scanning global signals, such as regulatory shifts and competitive movements, providing a real-time alternative to static risk registers. Furthermore, the review examines the shift from predictive to prescriptive analytics, where AI-driven simulations and digital twins allow executives to model the outcomes of strategic pivots under varying economic scenarios. We explore the enhancement of organizational decision-making through Intelligent Decision Support Systems (IDSS), emphasizing the mitigation of cognitive biases and the transition toward continuous, data-backed monitoring. The article also addresses critical implementation barriers, including the "black box" nature of deep learning, the necessity for explainable AI (XAI) in corporate governance, and the ethical implications of algorithmic bias. By synthesizing current literature and technological trends, this review provides a strategic roadmap for integrating AI into the executive suite, concluding that the future of enterprise resilience lies in the synergy between human intuition and machine intelligence.
DOI: https://doi.org/10.5281/zenodo.18228326
A Comprehensive Study Of Wireless Cloud Networks Supporting Large-Scale Internet Of Things Deployments
Authors: Samvith Hegde
Abstract: The rapid expansion of the Internet of Things has led to the emergence of large-scale deployments that demand unprecedented levels of connectivity, computational power, and storage capacity. Wireless Cloud Networks (WCNs) provide a critical solution by integrating the ubiquitous access of wireless communication with the robust processing capabilities of cloud computing. This review article presents a comprehensive study of the architectural frameworks, key enabling technologies, and resource management strategies essential for supporting massive IoT ecosystems. We analyze the transition from centralized cloud models to decentralized edge-cloud continuums, highlighting how this shift addresses the stringent latency and bandwidth requirements of modern applications. Furthermore, the paper investigates critical security challenges and privacy-preserving mechanisms tailored for high-density device environments. By examining diverse use cases in smart cities, industrial automation, and environmental monitoring, we illustrate the practical impact of wireless cloud integration. Finally, the study identifies open research challenges, including interoperability, network sustainability, and the move toward AI-native 6G infrastructures, providing a strategic roadmap for future developments in the field of intelligent, large-scale connectivity.
DOI: https://doi.org/10.5281/zenodo.18228369
AI-Based Predictive Models For Early Detection Of Financial Fraud In Enterprise SAP Environments
Authors: Manjunath gowda .C
Abstract: The rapid digitization of corporate finance has rendered traditional, rule-based fraud detection systems increasingly inadequate against the sophisticated, high-velocity deceptive practices. This review article evaluates the integration of AI-based predictive models within enterprise SAP environments, specifically focusing on the transition from retrospective auditing to proactive, real-time prevention. By leveraging the unified data architecture of the SAP S/4HANA Universal Journal (ACDOCA), organizations can deploy a multi-layered analytical framework comprising supervised learning for known pattern recognition, unsupervised anomaly detection for identifying "zero-day" fraud, and graph-based analysis for uncovering complex collusion networks. The analysis details the technical implementation pathways, contrasting embedded intelligence within the SAP HANA database with side-by-side innovation on the SAP Business Technology Platform (BTP). Furthermore, the article investigates the transformative impact of Generative AI and agentic finance in automating investigative workflows and enhancing Explainable AI (XAI) for regulatory compliance. Strategic challenges, including the mitigation of model drift and the ethical implications of algorithmic bias, are critically examined to ensure a "human-in-the-loop" governance model. The findings provide a comprehensive roadmap for financial architects and security officers, highlighting how federated learning and quantum-resistant architectures will define the future of enterprise security. Ultimately, the synthesis of these technologies enables the emergence of the autonomous enterprise a self-healing financial ecosystem capable of maintaining absolute integrity in an increasingly volatile digital landscape.
DOI: https://doi.org/10.5281/zenodo.18228854
Streaming-Native ERP Extensions: Leveraging Kafka Streams, Microservices, And Big Data Architectures To Enable Intelligent Decision Automation In Human Capital Platforms
Authors: Emily Carter, James Whitaker, Lauren Mitchell, Benjamin Harris, Ryan Sullivan, Ananya Kulkarni
Abstract: Enterprise Resource Planning systems have traditionally relied on batch-based integrations and tightly coupled extension models that limit responsiveness in dynamic organizational environments, particularly within human capital platforms where workforce data changes continuously and decisions are time-sensitive. This study proposes a streaming-native ERP extension architecture that leverages Kafka Streams, event-driven microservices, and scalable big data infrastructures to enable real-time, intelligent decision automation across human capital management ecosystems. Rather than treating ERP extensions as static transactional add-ons, the framework reconceptualizes them as continuous event-processing layers capable of ingesting employee lifecycle events, compensation updates, time and attendance signals, performance interactions, and compliance triggers as live data streams. Through stateful stream processing, schema-governed event pipelines, and domain-aligned microservices acting as autonomous decision agents, the architecture supports low-latency anomaly detection, automated approval routing, predictive workforce analytics, and policy-driven compliance enforcement. A distributed analytics layer maintains historical data persistence and model retraining capabilities, enabling adaptive learning and sustained optimization. Comparative architectural evaluation demonstrates significant reductions in decision latency, improved operational transparency, and enhanced governance consistency when contrasted with conventional batch-oriented ERP customization approaches. The findings indicate that streaming-native ERP extensions represent a structural evolution in enterprise system design, transforming human capital platforms from reactive reporting environments into proactive, intelligence-driven ecosystems capable of continuous insight generation and automated, context-aware decision support.
AI-Augmented Decision Support Systems: Design, Implementation, And Evaluation
Authors: Dr. Benjamin Clarke, James Anderson, Dr. Victoria Hughes, Daniel Foster, Adam Richards
Abstract: Decision Support Systems (DSS) have undergone a substantial transformation, evolving from static, rule-based expert systems into adaptive, data-driven, and learning-enabled platforms designed to support complex decision-making in dynamic and uncertain environments. This shift has been driven by advances in artificial intelligence (AI), particularly in machine learning, probabilistic reasoning, and human-in-the-loop (HITL) approaches, which emphasize collaboration between computational models and human expertise rather than full automation of decisions. Contemporary AI-augmented DSS combine predictive analytics, pattern recognition, optimization techniques, and simulation with domain knowledge and contextual awareness, enabling decision-makers to explore alternatives, assess trade-offs, and respond to changing conditions while maintaining responsibility and control. Beyond technical capability, these systems increasingly address human factors such as trust, transparency, usability, and workflow integration, recognizing that effective decision support depends as much on user interaction as on algorithmic performance. This article synthesizes prior research to present a unified perspective on the design, implementation, and evaluation of AI-augmented DSS, drawing on architectural models that integrate data, models, and interfaces; empirical findings from applied decision support deployments; and conceptual frameworks that describe varying degrees of automation and human involvement. By identifying recurring design patterns, socio-technical challenges, and evaluation methodologies, the paper provides researchers and practitioners with a structured foundation for developing trustworthy, effective, and human-centered AI-augmented decision support systems capable of delivering sustained value in real-world settings.
Mathematical Perspectives On Financial Literacy And Digital Economy Inclusion
Authors: Jag Pratap Singh Yadav
Abstract: Financial literacy and digital economy inclusion are now important components of economic development. Financial service providers have slowly started shifting towards digitization over time, and being in line with such a trend involves not just understanding the basics of budgeting, savings, inflation, interest, among others, but also requires skills on how to manage finances using mobile banking apps, digital money transfers, internet loans, algorithmic credit scores, and platform economics. The study of mathematics helps in understanding financial decisions and digitization because they rely on calculations, probabilities, optimality, and statistics. This paper explores financial literacy and digital economy inclusion in relation to mathematics. Mathematical reasoning lies at the foundation of our knowledge of how interest is accumulated, debt management, consumer investment behavior, risks, pricing, digital money transactions, and economic inequality. In passing, it is the same reason why mathematical modeling enables us to think of financial capability analysis, detecting inequalities, promoting consumer empowerment, and designing inclusive policies. It addresses problems such as inadequate numeracy skills, the obscure nature of algorithms, and disparities in infrastructure availability. What is implied by all this is that, in order for someone to be able to participate in the digital world and ensure financial inclusion is not only about access to financial instruments but more of economic empowerment, there should be a focus on mathematics.
AI-Driven Operational Signature Extraction From Thread Dumps And Messaging System Logs
Authors: Dr. Jonathan Mercer, Emily Richardson, Dr. Nathaniel Brooks, Olivia Bennett, Ethan Clarke, Jeji Krishnan
Abstract: Modern enterprise messaging and distributed application environments generate massive volumes of operational data in the form of thread dumps, mailbox logs, runtime traces, and system diagnostics. Analyzing these heterogeneous data sources manually is time-consuming, error-prone, and often insufficient for identifying hidden operational anomalies, performance bottlenecks, and service degradation patterns. This research proposes an AI-driven operational signature extraction framework that leverages deep neural models to automatically learn, classify, and interpret operational behaviors from thread dumps and messaging system logs. The proposed approach integrates log parsing, feature engineering, sequence modeling, and anomaly detection techniques to identify recurring runtime signatures associated with deadlocks, thread contention, latency spikes, mailbox congestion, and system instability. By applying deep learning architectures such as recurrent neural networks and transformer-based models, the framework enables intelligent correlation of runtime events across distributed systems and improves diagnostic accuracy in complex operational environments. Experimental evaluation demonstrates that the proposed model significantly enhances anomaly detection efficiency, reduces manual troubleshooting effort, and accelerates root cause identification compared to traditional rule-based monitoring approaches. The study highlights the potential of AI-powered operational analytics in strengthening enterprise observability, predictive maintenance, and automated support engineering for large-scale messaging infrastructures.
International Journal of Science, Engineering and Technology