International Journal of Science, Engineering and TechnologyStructural Analysis of Box Girder Bridge Structure
Authors – Dilip Patidar, Asst.Prof. Rahul Sharma
Abstract- -A box girder bridge is one in which the principal structural element is one or more closed cells, acting in bending. Box girders are used for highway bridges, railway bridges and footbridges – different structural forms are chosen for each of these applications. The objective of current research is to investigate the effect of different dimensional variables on strength of box Girder Bridge using techniques of Finite Element Analysis. The CAD modelling and FEA simulation of box Girder Bridge is conducted using ANSYS simulation package. The deformation obtained on the bridge structure is not uniform and found to be mostly at the zones of load application and reduces on other zones. The deformation pattern is almost uniform across length and width of the bridge structure. The bending stress distribution is also observed to be non-uniform and is maximum at the regions of load application. From the structural analysis conducted on box Girder Bridge structure the structural stability is established.
Review of Image Processing Technique for Glaucoma Detection
Authors – M.Tech. scholar Pankaj Goud, Asst. Prof. Miss Priyanshu Dhameniya
Abstract- -This review article discusses the use of a variety of image processing methods with the purpose of providing an automated diagnosis of glaucoma. Glaucoma is a neurological illness that affects the optic nerve and may lead to a loss of some of one’s eyesight. In both rural and semi-urban regions of the globe, a disproportionately high number of individuals are affected by eye illnesses. The examination of a picture of the retinal fundus using image processing is now the gold standard for diagnosing retinal diseases. Image registration, image fusion, image segmentation, feature extraction, image enhancement, morphology, pattern matching, image classification, analysis, and statistical measures are some of the essential image processing methods for detecting eye illnesses.
Review of Image Processing Technique for Glaucoma Detection
Authors – Asst. Prof. Divya Jatain, Asst. Prof. Anju Dhillon
Abstract- -Artificial Intelligence and Machine Learning have always tried to imitate the human way of thinking and problem solving in a better & efficient way. Machine Learning has essentially been different from Human Learning in a sense that Human Learning has always focused on the and Why of a problem whereas, Machine Learning has focused on What. Keeping this fact in mind, in the current work, the authors have developed a framework for a chatbot that uses human learning to enhance machine learning. The model is implemented using the Tensor Flow library in python and the user interactions for the chatbot are provided using web development languages like HTML, CSS, and JavaScript. The model provides as accuracy of around 83% for the given dataset, and trains the machine learning model in a more human-like manner.
Power Quality Model of Distributed Networks Integrated with Renewable Energy Sources
Authors – Francis M. Itote, Lorian M. Mbaabu, Job M. Kerosi
Abstract- – The integration of renewable energy sources (RES) into the power system distribution network has proven beneficial to all the power sector players. These include reduced greenhouse gas emission, reduced transmission and distribution losses, delivery of clean power to consumers due to proximity of loads to the generators, and deferred investments by distributed network operators. The continued integration of RES has been enhanced by the inability of conventional energy to meet increasing power requirements, the need for clean energy, low power generation costs due to technological advancements, and favorable government policies encouraging investment in the renewable energy sector. This integration, however, necessitates the reconfiguration of the distribution network since RES cause reversed power flows, instability among other power quality concerns. This transformation will rely on studies conducted on the behavior of grids integrated with renewable energy sources. This paper examined the impact on harmonics and flicker of low voltage (LV) and medium voltage (MV) networks caused by the integration of solar, wind and gas micro-turbine generators. This was realized by integrating the RES at selected locations of the IEEE-33 bus system and carrying out harmonics and flicker analysis using DigSILENT Power Factory software. Obtained results indicated increased harmonic distortion and flicker levels on LV and MV networks dependent on the type, location, penetration level and whether a single-type of RES or combination of RES was installed.
Survey on Privacy Preserving Mining Techniques for Health Care Data Analysis
Authors – M.Tech. Scholar Gagan Sharma, Dr. Sadhana Mishra
Abstract- – Nowadays, valuable data are generated and collected rapidly from numerous rich data sources. Following the initiatives of open data, many organizations including health, education, etc. are willing to share their data such as open data regarding parking violations. While there have been models to preserve privacy of sensitive personal data like patient data for health informatics, privacy of individuals who violated regulations should also be protected. Hence, this article, presented a model for supporting privacy-preserving big data analytics on temporal open data. Paper has performed a survey on recent methodology proposed by different researcher. Some of data mining methods were also describe in the paper which help in information extraction. Classification of the privacy preserving mining techniques was also introduced, in the article.
Detect Skin Cancer with Support Vector Machines Using Oriented Gradient Function Histograms
Authors – Tushar Bhangale
Abstract- – Detecting melanoma cancer in its early stages can help cure it. In this research paper, we propose an efficient skin cancer detection method based on support vector machines (SVM) with histogram of oriented gradient (HOG) functions. In it, skin cancer images from the ISIC 2018 dataset (International Skin Imaging Collaboration 2018) are converted to grayscale and preprocessed with a median filter. We then apply an image re-sampling technique to readjust the class distribution. HOG features are extracted from these preprocessed images. We then use the kernel-based His SVM classifier Radial Basis Function (RBF) to classify these extracted HOG features and detect cancer class labels. These predicted class tags are compared to the original tags to perform the evaluation. This proposed method has been tested and achieves 76% accuracy, 85% specificity, 84% accuracy, 76% recall, and 75% F1 score.
Combining Machine Learning and Block chain Together for Healthcare
Authors – Pasham Narayan Rohith Reddy, Dipankar Maiti, Kommu Pavan Sai, Asst. Prof. Ms. P. Sandhya Priyanka
Abstract- – Health care systems are that systems which help the patients reach directly the concerned specialist. These are the systems which are having lots of advantages during the pandemic situation and in high emergency situations. In the proposed work user will search for the disease summary (disease and treatment related information) by giving symptoms as a query in the search engine. Initially when a pdf is downloaded and saved in the system it first performs per processing on the data in the document and the extracted relevant data is stored in the database. The symptoms entered by the user are further classified using SVM classifier to make the further process easier to find the semantic keyword which helps to identify the disease easily and quickly. Then the semantic keyword found is matched with the stored medical input database to identify the exact disease related to that keyword present. Once the disease related to the symptom is identified, it is sent to medical database to extract the articles pertaining to that disease. The preprocessing process involves tokenization, removal of stop words and stemming. Followed by that, relevant information is extracted using the keyword searching algorithm. Till now the best result obtain is 98.51% F-measure by Oana Frunza, for the extraction of cure and prevents relations. In our implementation of our proposed system, we have used SVM classifier which gives us an improved result. The decentralized database where transactions get recorded in append only shared ledger has many advantages in healthcare industry. In medical treatment, the complete history of patient is very important and value is added when same information is accessed by different parties. The convergence of these two technologies can give highly accurate results in terms of machine learning with the security and reliability of Block chain Technology.
A Review on Roof Top Wind Turbine
Design and Applications
Authors – M.Tech. Scholar Pushpendra Patel, Prof. Gourav Beohar
Abstract- – In present work an approach is made to design a hwat for roof top application to fulfill the requirement of a urban household. For achieving the cut-off speed the bernaulies theorem is applied by using convergent and divergent duct. Is this NACA-0012 profile is selected for wind turbine blade. This work is consist of two steps of work in first step wind turbine blade is designed by using different correlations and in second step duct with single diffuser and double diffuser are analyze and duct with vertex generator is also examines to optimize the design of duct. The site selection for the input parameter is the shriram institute of technology Jabalpur (mp). The result obtain are compared with the base research paper reviewed. And the result represents the sustainable power production from design.
Design and Implementation of Search Engine with Intelligent Web Crawler
Authors – Neetu Anand, Abhinav
Abstract- – Information Retrieval deals in searching and retrieving of information which is stored in documents and it searches the web databases and the Web. A Web crawler is a program which moves around the Internet and saves web documents in a functional way. Using these features as the base, crawler is categorized into three types of techniques: General Purpose Crawling, focused crawling and Distributed Crawling. This paper deals with the history of Web Crawlers, its use in search engines, Design and scope of development in the future with potential problems. The Research work covers two major areas: – Populating a Database (With Web Crawler), Searching from the data store. The accountability of the foremost unit is to move like spider from one location to another and to find the keywords associated with each web page. The second module is web based and focuses on the search function from the database maintained by the first module.
Total Harmonics Distortion Performance Analysis between Micro-Inverter and Single Phase Inverter Photovoltaic Systems
Authors – Sonali Mathur, Assistant Professor Mukesh Kumar
Abstract- – Before the direct current (DC) input voltage can be turned into alternating current (AC), each of the current topologies of solar micro inverters uses a number of steps. One or more power converters could be built into each of these stages. A transformer, a filter, and a diode rectifier might also be in it. There are a very large number of both active and passive parts. In the scope of this thesis, a brand-new architecture for a solar micro inverter is made. This new micro inverter is made up of a new single switch inverter, which is made by changing the single ended primary inductor DC-DC converter that was already there. This new inverter can take DC power and turn it into a clean sinusoidal waveform. A lot of research is being done on how the new inverter is built and how it works. With the help of a controller, this new inverter can make almost any kind of output waveform. The inverter was found to be able to work in a total of four different ways. The new inverter was designed using a modelling method called “state space averaging.” Due to the switching that is built into the circuit, the system is a non-linear fourth order system. This makes the system not follow a straight line. Before the system can be looked at as a linear system, it must be linearized around a certain point. It has been found that the inverter’s control-to-output transfer function does not have a minimum phase. To find out about transfer functions, the root locus method is used. From the point of view of control, the presence of right half zero makes it harder to build the structure of the controller. The cell equations are used to make a model of the photovoltaic (PV) cell in MATLAB. The maximum power point tracking (MPPT) method is used to make sure that the PV cell’s output power is always at its highest level. This lets the power from the PV cell be used to its fullest potential. The easiest way to solve this problem is to change something and then watch what happens. When you use this new inverter, you don’t need the different phases that a traditional solar micro inverter needs. The proposed design of the inverter was confirmed by both simulation and the results of experiments done on the set-up.
Review of Deep Learning Approach in the Medical Field for Covid-19 Diagnosis
Authors – Research Scholar Vishal Acharya, Associate Prof. & HOD. Dr. Bharti Chourasia
Abstract- – For diagnosis of corona virus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT–PCR) test is routinely used. The COVID-19 pandemic has rapidly propagated due to widespread person-to-person transmission. The COVID-19 pandemic has resulted in over 3 billion cases worldwide. Early recognition of the disease is crucial not only for individual patient care related to rapid implementation of treatment, but also from a larger public health perspective to ensure adequate patient isolation and disease containment. Chest CT is more sensitive and specific than chest radiography in evaluation of SARS-CoV-2 pneumonia and there have been cases where CT findings were present before onset of clinical symptomatology4. In the current climate of stress on healthcare resources due to the COVID-19 outbreak, including a shortage of RT–PCR test kits, there is an unmet need for rapid, accurate and unsupervised diagnostic tests for SARS-CoV-2. Laboratory confirmation of SARS-CoV-2 is performed with a virus-specific RT–PCR, but the test can take up to 2 days to complete. This study shows various techniques of artificial intelligence (AI) algorithms to findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19.
A Novel Implementation of Robust CT scan Based Brain Diagnosis Process Using CNN GB Technique
Authors – Nallagonda Sharanya, B. Anvesh Kumar
Abstract- – While existing methods for detecting shilling attacks in online recommender systems are effective at identifying individual attackers, they are not as effective at detecting group shilling assaults, in which a group of attackers cooperate to influence the output of the system by injecting bogus profiles. This article presents a method for detecting shilling attacks as a group, using the bisecting K-means clustering algorithm. We begin by separating each items rating track and subdividing those into potential groups based on a predetermined amount of time. In the second place, we propose using the degree of item attention and user activity to determine the suspiciousness of candidate groups. In the end, we use the bisecting K-means algorithm to cluster the candidate groups according to their suspicious degrees and obtain the attack groups. Experiments conducted on the Netflix and Amazon data sets validate the superiority of the suggested strategy over the gold standards the second place, we propose using the degree of item attention and user activity to determine the suspiciousness of candidate groups. In the end, we use the bisecting K-means algorithm to cluster the candidate groups according to their suspicious degrees and obtain the attack groups. Experiments conducted on the Netflix and Amazon data sets validate the superiority of the suggested strategy over the gold standards.
MRI Image Segmentation and Classification Using KFCM and Convolution Neural Networks
Authors – Mithilesh Nandini Malviya , Asst. Prof. Ms. Priya Sen
Abstract- – This work focus on the stage effectual categorization of brain tumour descriptions and segmentation of exist illness images employing the segmentation and classification techniques. The major goal of the future method is to design well-organized and accurate algorithm that segmentation tumor region from brain MRI. The algorithm identifies the position of tumor in brain MRI as they are mostly preferred for tumor diagnosis in clinic. The proposed method also crops tumor region from segmented image and way growth of tumor and help in treatment planning. It also provides important information about location, dimension and shape of brain tumor region with no exposing the enduring to a high ionization radiation. The size of tumor is calculated in term of number of pixels. Similarly the primary brain tumor is considered into Benign and malignant type on MRI brain images, based on accuracy, sensitivity, specificity in MATLAB simulation.
Hybrid Renewable Energy Based Single Phased Of V2G and G2V
Authors -Twinkle Kumari , Prof. Neeti Dugaya, Dr. Geetam Richhariya, Dr. Manju Gupta
Abstract- – This system is economically justifiable given to the elimination of the PV dedicated converter and can inject the PV output power into the grid more efficiently. The results are simulated in the MATLAB/SIMULINK software environment. The dynamic model is developed using the machines equivalent circuit and is expressed in the stationary, rotor and the synchronous reference frames for evaluating the performance of the machine. The stator of the DFIG is directly interfaced to the grid and by controlling the rotor voltage by a two-level back-to-back converter the grid synchronization and power control is maintained. The Grid Side Converter (GSC) is modified for feeding regulated power to the grid. Rotor Side Converter (RSC) is controlled for achieving MPPT and Unity Power Factor (UPF) and with and without PV system Both Simulation done in MATLAB and Voltage, Current, real and reactive power for input and output Result carried.Wind power is one of the most developed and rapidly growing renewable energy sources. The thesis is dedicated to an in-depth analysis of DFIG PV and wind energy generators, system configurations, power converters, control schemes and dynamic and steady state performance of practical wind energy conversion systems (WECS). This thesis focuses on method for controlling the DC link voltage and maximum power point tracking (MPPT) of the photovoltaic (PV) system in a hybrid PV-wind turbine system is introduced. The system under study is a modified PV-DFIG structure. In this system, the PV output power is injected into the grid through the both grid-side and rotor-side converters of the DFIG. The proposed control system controls the DC-link voltage and the MPPT of the PV system together.
Review on Machine Learning Algorithm Based Health Care Monitoring System
Authors – M.Tech. Scholar Sonal Shrivastava , Prof. Rajesh Kumar Boghey
Abstract- – Machine Learning is modern and highly sophisticated technological applications became a huge trend in the industry. Machine Learning is Omni present and is widely used in various applications. It is playing a vital role in many fields like finance, Medical science and in security. Machine learning is used to discover patterns from medical data sources and provide excellent capabilities to predict diseases. In this paper, we review various machine learning algorithms used for developing efficient decision support for healthcare applications. This paper helps in reducing the research gap for building efficient decision support system for medical applications.
E-Agri Kit: Agricultural Aid Using Deep Learning
Authors – Ponnala Samatha, Dr. Dinesh Kumar
Abstract- – This project showcases an agricultural aid app that was built and designed to assist farmers by employing Image Processing, Machine Learning, and Deep Learning. Features like early detection of plant disease are available in our application and are implemented in a number of ways. It was determined that Convolutional Neural Network was superior for detecting plant diseases with a high degree of accuracy. The farmer can use the weather forecast to plan out agricultural tasks like harvesting and plucking at the optimal time. A crop-specific fertilizer calculator is in the works to determine how much urea, diammonium phosphate, and muriate of potash should be applied to a given area to prevent the recurrence of disease caused by depleted soil minerals.
Pv Generation And Power Transmission Analysis Using Power Flow Controllers
Authors – Dipak Borse, Assistant Professor Lovkesh Patidar
Abstract- – Energy security is one of the most crucial factor in the development of any nation. Inter-Connections among different power system networks are made to lower the overall price of power generation as well as enhance the reliability and the security of electric power supply. Different types of interconnection technologies are employed, such as AC interconnections, DC interconnections, synchronous interconnections, and asynchronous interconnections. It is necessary to control the power flow between the interconnected electric power networks. The power flow controllers are used to (i) enhance the operational flexibility and controllability of the electric power system networks, (ii) improve the system stability and (iii) accomplish better utilization of existing power transmission systems. These controllers can be built using power electronic devices, electromechanical devices or the hybrid of these devices. In this paper, control techniques for power system networks are discussed. It includes both centralized and decentralized control techniques for power system networks.
An Investigation of the Effect of Advertising on Consumer Brand Preference
Authors -Adeesha V. , Upamanyu C., Tarun Yadav
Abstract- – Recent developments in the field of information technology are reflected in every business today, including the food industry, the textile industry, and daily necessities, but the one thing that has not changed is the wedding invitation process. It still performs tasks like bookings, collecting feedback, etc manually. Wedding lawns offer all-inclusive services like catering, decorations, and food. The proposed work has found a solution for paper wastage required for printing wedding invitations. It also speeds up the process by changing the offline invitation process into a digital process thus reducing workload.This web app uses HTML 5, CSS 3, JavaScript, Bootstrap 4, and JQUERY for the front end, and the PHP framework Laravel for the back end.
An Investigation of the Effect of Advertising on Consumer Brand Preference
Authors -Adeesha V. , Upamanyu C., Tarun Yadav
Abstract- – Recent developments in the field of information technology are reflected in every business today, including the food industry, the textile industry, and daily necessities, but the one thing that has not changed is the wedding invitation process. It still performs tasks like bookings, collecting feedback, etc manually. Wedding lawns offer all-inclusive services like catering, decorations, and food. The proposed work has found a solution for paper wastage required for printing wedding invitations. It also speeds up the process by changing the offline invitation process into a digital process thus reducing workload.This web app uses HTML 5, CSS 3, JavaScript, Bootstrap 4, and JQUERY for the front end, and the PHP framework Laravel for the back end.
Transitioning Facility Management to Proactive Models Using AI-Driven Predictive Maintenance
Authors -Dayanand Jamkhandikar
Abstract- – As of December 2022, the transition from reactive to proactive facility management models has become a critical objective for organizations seeking operational efficiency and cost savings. This paper explores the integration of AI-driven predictive maintenance strategies to revolutionize traditional facility management practices. By leveraging machine learning (ML) and deep learning (DL) algorithms, organizations can predict equipment failures, optimize resource allocation, and enhance overall performance. The discussion aligns with frameworks introduced by Ramakrishna Manchana’s works on event-driven architectures and machine learning applications in real estate and facility management. The study also delves into the role of cloud-native solutions and data lake architectures in supporting predictive maintenance systems. Case studies and real-world applications demonstrate how AI technologies can reduce downtime, minimize maintenance costs, and foster sustainable facility operations.
DOI: /10.61463/ijset.vol.10.issue6.327
Synthesizing AI and Data-Driven Frameworks for Real Estate Lease Management
Authors -Dayanand Jamkhandikar
Abstract- – The integration of artificial intelligence (AI) and data-driven frameworks into real estate lease management offers transformative potential for optimizing decision-making and operational efficiency. This paper explores the synthesis of advanced AI techniques, including machine learning (ML) and deep learning (DL), with cloud-native architectures to automate lease abstraction, enhance data accuracy, and enable predictive analytics. By leveraging AI-driven models and data lakes, organizations can overcome traditional inefficiencies in lease management, such as manual processing and fragmented data systems. Building upon the foundational works of Ramakrishna Manchana on cloud-native solutions, event-driven architectures, and AI applications in real estate, this study proposes a comprehensive framework for real-time lease insights and actionable analytics. Key findings demonstrate significant cost reductions, improved compliance, and enhanced scalability, positioning AI as a critical enabler for modern property management.
DOI: /10.61463/ijset.vol.10.issue6.328