Hybrid Subset Feature Selection and Importance Framework
subject: Artificial Intelligence; Feature Importance; Feature Selection; feature-selectionpy; Stock Market; supervised learning
description: Feature selection algorithms are used in high-dimensional data to remove noise, reduce model overfitting, training and inference time, and get the importance of features. Features subset selection is choosing the subset with the best performance. This research provides a Hybrid subset feature selection and importance (HSFSI) framework that provides a pipeline with customization for choosing feature selection algorithms. The authors propose a hybrid algorithm in the HSFSI framework to select the best possible subset using an efficient exhaustive search. The framework is tested using the Bombay stock exchange IT index's companies' data collected quarterly for 16 years consisting of 71 financial ratios. The experimental results demonstrate that models created using 12 features chosen by the proposed algorithm outperform models with all features with up to 6% accuracy. The importance-based ranks of all features are generated using the framework calculated using 13 implemented feature selection techniques. All selected feature subsets are cross-validated using prediction models such as support vector machine, logistic regression, KNeighbors classffier, random forest, and deep neural network. The HSFSI framework is available as an open-source Python software package named ''feature-selectionpy'' available at GitHub and Python package index. 2023 IEEE.
creator: Chaudhari C.; Purswani G.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: Chaudhari C., CHRIST (Deemed to Be University), Dept. of Commerce, Bangalore, India; Purswani G., CHRIST (Deemed to Be University), Dept. of Commerce, Bangalore, India

A Comprehensive Review on Fault Data Injection in Smart Grid
subject: Cyber-attack; Fault data injection attack (FDIA); Machine learning; Smart grid
description: Nowadays, power generation at the utility side and transfer to the demand side have been controlled by the smart grid. Day-by-day entire power distribution process has moved in multiple directions and connects more residential and industrial sectors. Due to these phenomena, more monitoring, and security processes have been adopted in smart grid to control fault data injection, cyber-attack, and physical side attackers in smart grids. This research study analyzes the fault data injection in smart grid with respect to the malicious data, signal, and connectivity process. As a part of this research study, a survey has been done on various techniques to control the faults in smart grid. The analysis carried out in this study is very helpful to identify and determine the suitable method to control the fault in smart grid. Along with these, a countermeasure against the FDI is also summarized on the cyber-attack and physical attack. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Prakyath D.; Mallikarjunaswamy S.; Sharmila N.; Rekha V.; Pooja S.
source: Lecture Notes in Networks and Systems, Vol-757 LNNS, pp. 649-660.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981995165-9
format: Online
language: English
type: Conference paper
coverage: Prakyath D., Department of Electrical and Electronics Engineering, SJB Institute of Technology, Karnataka, Bengaluru, India; Mallikarjunaswamy S., Electronics and Communication Engineering, JSS Academy of Technical Education, Karnataka, Bengaluru, India; Sharmila N., Electrical and Electronics Engineering, JSS Science and Technology University, Karnataka, Mysore, India; Rekha V., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Pooja S., Department of Electronics and Communication Engineering, KS Institute of Technology, Bengaluru, India

Review on Image Processing-Based Building Damage Assessment Techniques
subject: Classification; Image enhancement; Image segmentation; Remote sensing; Satellite sensors
description: Quick damage assessment is essential for starting efficient emergency response operations following natural calamities or any other kind of disasters. After a disaster, it is crucial for rescue departments to produce judgments and distribute the resources based on a fast retrieval of precise building damage status. A ground survey is used to implement traditional building assessment, and this is labor-intensive, dangerous, and time-consuming. Studies on building damage extraction over the past few decades have generally concentrated on localizing and evaluating the destructed structures, analyzing the ratio of damaged constructions, and determining the sort of destruction each construction has sustained. Recent research trends are mainly concentrated on the utilization of data collected from multiple sensors for the damage assessments of buildings. Each stage of digital image processing can be carried out in multiple ways and several novel ideas are emerging every single day. This paper reviews the various damage assessment techniques in the different steps of digital image processing. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
creator: Sajitha I.; Sambandam R.K.; John S.P.
source: Lecture Notes in Networks and Systems, Vol-726 LNNS, pp. 863-882.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981993715-8
format: Online
language: English
type: Conference paper
coverage: Sajitha I., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, India; John S.P., Department of Computer Science and Engineering, Jyothi Engineering College, Kerala, Cheruthuruthy, Thrissur, India

Autism Spectrum Disorder: Automated Detection based on rs-fMRI images using CNN
subject: AAL116; ABIDE; ASD; CC200; CC400; CNN; resting-state fMRI
description: Autism spectrum disorder (ASD) impacts approximately 1 in every 160 children globally and is classified as a neurodevelopmental condition. Image classification in neuroscience has advanced primarily due to convolutional neural networks (CNNs) and their capacity to provide better algorithms, more computing resources, and data. This study used a brain scan dataset to test the feasibility of utilizing CNN to detect ASD. Using functional connectivity patterns, the Autism Brain Imaging Exchange (ABIDE) data repository, which includes recordings of rest-state functional magnetic resonance imaging (rs-fMRI), the aim of using it was to distinguish between individuals who have Autism Spectrum Disorder (ASD) and those who are healthy controls. The proposed method effectively classified the two groups. According to the test findings, the suggested model has the ability to accurately detect ASD with a reliability rate of 92.22% when implemented on the ABIDE dataset using the CC200, CC400, and AAL116 brain atlases. The CNN model is computationally more efficient since it uses fewer parameters than other cutting-edge methods. 2023 IEEE.
creator: Lamani M.R.; Julian Benadit P.; Vaithinathan K.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: Lamani M.R., CHRIST (Deemed to Be University) Kengeri, Computer Science and Engineering, Bangalore, 560074, India; Julian Benadit P., CHRIST (Deemed to Be University) Kengeri, Computer Science and Engineering, Bangalore, 560074, India; Vaithinathan K., Karaikal Polytechnic College Computer Engineering, Puducherry, Karaikal, India

A Compatible Hexadecimal Encryption-Booster Algorithm for Augmenting Security in the Advanced Encryption Standard
subject: AES enhancement; content hide; hide content; hide encrypted data; hide encryption content; hide storage utilization
description: Among the most prominent encryption algorithms, Advanced Encryption Standard ranks first. Even so, many familiar characters can be seen when an AES encrypted file is opened. As of today, there have been very few contributions to research on suppressing known characters in AES encrypted files. It is possible to identify encrypted files not only by their name and content, but also by their size. As a result, hackers can identify files at source and target locations by comparing their sizes. In this paper, a methodology is presented to address these two research gaps. As a result of the proposed algorithm, almost all characters are transformed into an unintelligible format not only for humans, but also for computer interpreters. As an additional benefit, the proposed method makes the encrypted file appear smaller and conceals its actual size. The proposed Encryption Booster algorithm is also easily integrated with Advanced Encryption Standard. 2023 IEEE.
creator: Shaik V.; Natarajan K.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: Shaik V., CHRIST Deemed to Be University, Department of Computer Science and Engineering, Karnataka, India; Natarajan K., CHRIST Deemed to Be University, Department of Computer Science and Engineering, Karnataka, India

Prognosis of Diabetes Mellitus Paradigm Predictive Techniques
subject: CART; Dataset; Diabetes; J48; Machine Learning; Matlab; Prediction; Random Forest; Weka
description: Human life is in the era of data, when almost everything is straped on to data wellspring more- over entire esse are digitises telerecorded. That is data is generated every milli second through several means like Agriculture, Bioinformatics, Web, Cybersecurity, Smart city data, classified in- formation, pda data, flexibility evidence, medical facts, Covid related data from official state too central government portals and a number of other sources are available in todays technological con- text. There are various forms of data like structured, semi-structured, and unstructured data, text, graphics are all feasible. Every day, week, month new genre natural-world features to be resolved, machine learning adroitness have emerged as problem resolver. As a result, data management tools and analytical methodologies capable of extricate penetrated realization related specifics felicitous methodical manner ceaselessly whereby world of nature enactment rely urgently needed. The vast majority of research is focused on machine learning prediction algorithms; thus, we focus on these. Our evaluation aims to provide newbies to the field, as well as more seasoned readers, with a thorough understanding of the primary approaches and algorithms developed over the previous two decades, with an emphasis on the most notable and continuing work. We also present a new taxonomy of state of the art Model, which highlights the many conceptual and technical approaches to training with labeled and unlabeled data. Finally, we show how the fundamental assumptions underlying most machine learning methods are linked to the well-known assumptions. Grenze Scientific Society, 2023.
creator: Padmaja K.; Mukhopadhyay D.
source: 14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023, Vol-2023-June, pp. 2410-2415.
publisher: Grenze Scientific Society
date: 2023-01-01
rights: Restricted Access
format: Online
language: English
type: Conference paper
coverage: Padmaja K., CHRIST (Deemed to be University, Department of CSE, Bangalore, India; Mukhopadhyay D., CHRIST (Deemed to be University, Department of CSE, Bangalore, India

Advancements in e-Governance Initiatives: Digitalizing Healthcare in India
subject: Contact tracing applications; e-Governance; EHR; ICT digital health
description: In order to improve the quality of service delivery to the public, to encourage interactive communications between government and citizens or government and business, and to address development challenges in any given society, information and electronic governance is the sophisticated fusion of a wide range of information and communication technologies with non-technological measures and resources. Digital technology advancements over the past ten years have made it possible to quickly advance data gathering, analysis, display, and application for bettering health outcomes. Digital health is the study and practice of all facets of using digital technologies to improve ones health, from conception through implementation. Digital health strategies seek to improve the data that is already accessible and encourage its usage in decision-making. Digital patient records that are updated in real-time are known as electronic health records (EHRs). An electronic health record (EHR) is a detailed account of someones general health. Electronic health records (EHRs) make it easier to make better healthcare decisions, track a patients clinical development, and deliver evidence-based care. This concept paper is based on secondary data that was collected from a variety of national and international periodicals, official records, and public and private websites. This paper presents a review of advancements for scaling digital health within Indias overall preparedness for pandemics and the use of contact tracing applications in measuring response efforts to counter the impact of the pandemic. The paper provides information about the government of Indias EHR implementation and initiatives taken toward the establishment of a system of e-governance. The document also covers the advantages of keeping EHR for improved outreach and health care. Further, this paper discusses in depth the effectiveness of using contact tracing applications in enhancing digital health. The Author(s), under ex…
creator: Das S.; Garg V.; Joseph J.J.
source: Lecture Notes in Networks and Systems, Vol-755 LNNS, pp. 287-296.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981995084-3
format: Online
language: English
type: Conference paper
coverage: Das S., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bengaluru, India; Garg V., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bengaluru, India; Joseph J.J., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bengaluru, India

An Architecture for Risk-Based Authentication System in a Multi-Server Environment
subject: Adaptive Authentication(AA); Machine Learning; Multi-Factor Authentication(MFA); Multi-Server Environment; Risk-Based Authentication(RBA)
description: Identity authentication, a vital part of any application access, is also one way for imposters to gain access to an application using various fingerprint authentication technologies. Therefore, because of the lack of security in the authentication architecture, this paper proposes an architecture for a risk-based authentication system using a machine learning model in a multi-server environment. Since the recent study mainly focuses on the multi-server environment and adaptive authentication independently, very little work has been proposed using a multi-server environment for adaptive authentication. The study aims to estimate risk for the user during the initial login process and when the user's data is extracted enough for prediction in a multi-server environment. 2023 IEEE.
creator: Pramila R.M.; Shukla S.
source: 2023 IEEE International Conference on Public Key Infrastructure and its Applications, PKIA 2023 - Proceedings
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835031055-9
format: Online
language: English
type: Conference paper
coverage: Pramila R.M., Christ (Deemed to Be University), Data Science Department, Lavasa, Pune, India; Shukla S., Christ (Deemed to Be University), Data Science Department, Lavasa, Pune, India

KnowSOntoWSR: Web Service Recommendation System Using Semantically Driven QoS Ontology-Based Knowledge-Centred Paradigm
subject: KnowSOntoWSR; QoS ontology; Twitter semantic similarity; Web service recommendations; XGBoost
description: Web services have significantly expanded and become a key enabling technology for online data, application and resource sharing. Designing new methods for efficient and reliable web service recommendation has been of tremendous importance with the growing usage and prominence of web services. It would be ideal for a system to suggest online services that are in line with consumers preferences without requesting specific query information from them. Quality of Service (QoS) is vital for characterising non-functional aspects of Web services as they become more prevalent and widely used on the World Wide Web. The KnowSOntoWSR framework, which is built on a knowledge-driven and semantically inclined model that adheres to QoS ontology, is proposed in this research. AWS and WebSphere are employed as knowledge tags, and the powerful machine learning classifier XGBoost is applied. The features and recommendations are computed using the Twitter semantic similarity. The proposed framework outperforms the baseline models estimates with an accuracy of 95.94% and average F-measure of 95.93%. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Dhanvardini R.; Deepak G.; Priyadarshini J.S.; Santhanavijayan A.
source: Lecture Notes in Networks and Systems, Vol-616 LNNS, pp. 233-242.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981199718-1
format: Online
language: English
type: Conference paper
coverage: Dhanvardini R., Healthcare Informatics Division, Optum UnitedHealth Groups, Hyderabad, India; Deepak G., Department of Computer Science Engineering, National Institute of Technology, Tiruchirappalli, India; Priyadarshini J.S., Department of Data Science, Manipal Institute of Technology Bengaluru, Bengaluru, India, Department of Data Science, Manipal Academy of Higher Education, Manipal, India, CHRIST (Deemed to Be University), Bangalore, India; Santhanavijayan A., Department of Computer Science Engineering, National Institute of Technology, Tiruchirappalli, India

Smart Intravenous Infusion Monitoring and Alert System using IoT-based Force Sensitive Resistor and Whatabot API
subject: FSR sensor; Healthcare; IoT; IV fluid; NodeMCUESP8266; Whatabot
description: Intravenous fluids with vitamins are given to people who are dehydrated and have an imbalance of electrolytes. When the IV bag is empty, the patient's blood flows through the IV line toward the empty bag because their blood pressure is higher than the IV bag's. This process, called diffusion can lead to pain and loss of blood. If the IV bag is empty and hooked up to the patient, air can get into the bloodstream. The air bubble enters the patient's bloodstream, causing the same catastrophic effects. This paper aims to eliminate the danger by creating Smart IV Bags in light of the rising number of risks in the medical sector brought on by the reverse flow of blood in an IV bag (intravenous bag). The Smart IV bags eliminate the need for constant physical monitoring of the IV bag's state while preventing reverse blood flow. For detection of the IV fluid state, a 0.5 ' diameter force-sensitive resistor is deployed. We integrate the system with a NodeMCU module and using Wi-Fi, it establishes communication between the smart IV bag and the person in charge at the hospital, like a nurse or a caretaker. In this study, the IV Bag is the 'thing' connected to the internet where the FSR readings are analyzed using NodeMCU script to determine if it needs to be emptied and send a WhatsApp message to the caretaker. This design establishes an IoT environment where the IV Bag automatically alerts the caretaker and eliminates the need for constant human intervention. 2023 IEEE.
creator: Sreekumar K.; Francis J.; Prathap B.R.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: Sreekumar K., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Francis J., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Prathap B.R., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India

Role of Machine Learning in the Analysis of Mental Health Data: An Empirical Approach
subject: Machine learning; Mental health; Supervised learning; Unsupervised learning
description: As funding for mental health research has grown, so too has the body of knowledge about how best to address and alleviate issues related to mental health. However, there is still a lack of certainty and clarity on the precise causes of mental diseases. Discovery of new drugs, analysis of radiological data, forecasting of disease outbreaks, and the diagnosis of illnesses are just some of the medical applications of machine learning algorithms. Machine learning algorithms are commonly used to sift through the mountains of medical data. Since their performance has improved to the point where it can be relied upon, they are now used to aid in medical diagnosis. To assess and address the issues with mental health, numerous new approaches and algorithms had been devised. There are still a lot of issues that can be resolved. So the main purpose of this study is to examine the effectiveness of machine learning in mental health problems. For fulfilling this purpose, this study is descriptive in nature. Primary data is collected with the help of interview method in which 50 individuals suffering from mental illness were asked to answers some questions. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Hashmi S.G.; Shahi F.I.; Qidwai K.A.; Naser M.; Shafiuddin M.; Upreti K.
source: Lecture Notes in Networks and Systems, Vol-730 LNNS, pp. 513-522.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981993962-6
format: Online
language: English
type: Conference paper
coverage: Hashmi S.G., Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jizan, Saudi Arabia; Shahi F.I., Deanship of E-Learning and Information Technology, Jazan University, Jazan, Saudi Arabia; Qidwai K.A., Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jizan, Saudi Arabia; Naser M., Deanship of E-Learning and Information Technology, Jazan University, Jazan, Saudi Arabia; Shafiuddin M., AFS Department, Oman College of Management and Technology, Halban, Oman; Upreti K., Department of Computer Science CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India

Machine Learning Model to Detect Chronic Leukemia in Microscopic Blood Smear Images
subject: Chronic lymphocytic Leukemia; Chronic Myeloid Leukemia; image processing; Machine Learning; Random Forest; Watershed Segmentation
description: Chronic leukemia is a slow-progressing form of disease, If not diagnosed on time can progress and increase the risk of life-threatening complications. It is essential to develop a fully automated system to recognize and categorize type of leukemia for proper evaluation and treatment. This paper aims to provide a machine learning model to identify and classify chronic lymphocytic leukemia, chronic myeloid Leukemia and healthy cells. Digital microscopic blood smear images were automatically cropped into single nucleus and segmented using watershed algorithm. Grey level co-occurrence matrix (GLCM) and geometrical features were extracted from the segmented nucleus images and random forest algorithm is used to classify chronic leukemia and healthy cells. This prognosis aids pathologists and physicians in identifying leukemic patients early and selecting the most effective course of action. 2023 IEEE.
creator: Patil A.P.; Hiremath M.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: Patil A.P., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Hiremath M., CMR Institute of Technology, Department of Computer Application, Bengaluru, India

Analytical Methods of Machine Learning Model for E-Commerce Sales Analysis and Prediction
subject: Boosting; Explained Variance; Extreme Gradient Boosting; Machine Learning; Sales Prediction
description: In the commercial market, E-commerce sales show a significant trend and have attracted many consumers. Ecommerce sales forecasting has a significant role in an organization's growth and aids in improved operation. Many studies have been conducted in the past using statistical, fundamental, and data mining techniques for better analysis and prediction of sales. However, the current scenario calls for a better study that combines the available information to propose different machine-learning techniques. The sole motive of the study is to analyze and determine different machine learning models to predict accurate results. The research observed that the Extreme Gradient Boosting model outperformed all other models and brought a good result. It produced an RMSE value of 0.0004 and Explained Variance score of 0.99. Decision Tree algorithm also shows an exemplary result. 2023 IEEE.
creator: Anushka Xavier K.; Manjunath C.; Manohar M.; Gurudas V.R.; Jayapandian N.; Balamurugan M.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: Anushka Xavier K., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India; Manjunath C., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India; Manohar M., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India; Gurudas V.R., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India; Jayapandian N., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India; Balamurugan M., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India

Efficient Integration of Photovoltaic Cells with Multiport Converter for Enhanced Energy Harvesting
subject: Battery; Efficiency; Multiport converter; PV Cell
description: This research work presents a novel approach for the efficient integration of photovoltaic (PV) cells with a multiport converter to enhance energy harvesting in renewable energy systems. The proposed system combines the advantages of PV technology with the flexibility and scalability of multiport converters, enabling improved power extraction and utilization from solar energy sources. The integration is achieved by employing a multi-input multi-output (MIMO) control strategy, which optimally distributes power among multiple energy storage systems and loads. A comprehensive modeling and analysis of the PV cell characteristics and the multiport converter are conducted to identify the optimal operating conditions. Furthermore, a power management algorithm is developed to dynamically regulate the power flow and maximize the energy harvesting efficiency. The proposed approach demonstrates superior performance compared to traditional single-input single-output converters, achieving higher energy yields and enabling effective integration of PV cells in diverse applications. Simulation results validate the effectiveness of the proposed approach, showcasing its potential to significantly enhance energy harvesting from photovoltaic sources and contribute to the development of sustainable and reliable renewable energy systems. 2023 IEEE.
creator: Parimalasundar E.; Jayakumar S.; Chandrika V.S.; Muthukaruppasamy S.; Devaraju T.; Suresh K.
source: Proceedings of the 5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023, pp. 1590-1595.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835032142-5
format: Online
language: English
type: Conference paper
coverage: Parimalasundar E., Mohan Babu University, Erst while Sree Vidyanikethan Engineering College, Electrical and Electronics Engineering, Tirupati, India; Jayakumar S., Sri Sairam College of Engineering, Electronics and Communication Engineering, Bengaluru, India; Chandrika V.S., Kpr Institute of Engineering and Technology, Electrical and Electronics Engineering, Coimbatore, India; Muthukaruppasamy S., Velammal Institute of Technology, Electrical and Electronics Engineering, Chennai, India; Devaraju T., Mohan Babu University, Erst while Sree Vidyanikethan Engineering College, Electrical and Electronics Engineering, Tirupati, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bangalore, India

Student's Performance Prediction Using Modified PSO
subject: Bag-of-features; Machine Learning Algorithm; Optimization techniques; Student Performance
description: In the present-day education system, evaluating students' performance is very important. The prediction of student performance is helpful to students and instructors in maintaining a student's progress. Presently, institutions are implementing Continuous Evaluation Systems. This system is beneficial for improving the students' performance and helps increase their regularity in their studies. This work proposed a new version of the particle swarm optimization algorithm to classify students into different categories based on their performance with the help of various factors. The proposed model modified the strategy to increase the optimization algorithm's efficiency. This model deals with the bag of features with the modified optimization algorithm to recognize students' performance based on 33 features. These features are used for training, validation, and testing of the student's performance and generate a classification report which includes F-measure, recall, precision, and accuracy. Based on the classification report, the proposed model is compared with the existing methods, and the better performance of the proposed model is ascertained. 2023 IEEE.
creator: Kumari R.; Kumar S.
source: Proceedings - 2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023, pp. 1015-1020.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835031006-1
format: Online
language: English
type: Conference paper
coverage: Kumari R., Icfai Business School (IBS) Bangalore, Off-Campus Center of Icfai Foundation for Higher Education (IFHE) University, Bangalore, India; Kumar S., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India

Enhanced Design and Performance Analysis of a Seven-Level Multilevel Inverter for High-Power Applications
subject: Efficiency; IGBT; Inverter; PWM; THD
description: The structure and performance analysis of a seven-level multilevel inverter is discussed in this study. Due to their capacity to get around the drawbacks of traditional two-level inverters, like high voltage stress on power devices and harmonic distortion, multilevel inverters have attracted a lot of attention lately. Multiple voltage levels can be produced by the seven-level multilevel inverter which is being proposed because it uses a sequential arrangement of power sources and capacitors. The design methodology involves selecting appropriate power devices and capacitance values to achieve the desired voltage levels while minimizing losses and ensuring reliable operation. Total harmonic distortion (THD), inverter efficiency, and voltage stress on power devices are all considered as part of the performance analysis. In comparison to conventional two-level inverters, simulation results indicate that the proposed seven-level multilevel inverter offers lower THD, increased efficiency, and reduced voltage stress. This research contributes to the advancement of multilevel inverter technology and its potential applications in various power conversion systems. 2023 IEEE.
creator: Parimalasundar E.; Jayakumar S.; Nagajothi P.; Chandrika V.S.; Devaraju T.; Suresh K.
source: Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023, pp. 1862-1868.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835032579-9
format: Online
language: English
type: Conference paper
coverage: Parimalasundar E., Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Jayakumar S., Sri Sairam College of Engineering, Electronics and Communication Engineering, Bengaluru, India; Nagajothi P., S.A. Engineering College, Electrical and Electronics Engineering, Chennai, India; Chandrika V.S., Kpr Institute of Engineering and Technology, Electrical and Electronics Engineering, Coimbatore, India; Devaraju T., Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bangalore, India

Facial Emotion Detection Using Deep Learning: A Survey
subject: Convolutional Neural Network; Deep Learning; Facial Emotion
description: The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. 2023 IEEE.
creator: Ansari S.; Kulkarni P.; Rajesh T.; Gurudas V.R.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: Ansari S., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Kulkarni P., Dayanand Sagar University, Department of Computer Science and Engineering, Bangalore, India; Rajesh T., Dayanand Sagar University, Department of Computer Science and Engineering, Bangalore, India; Gurudas V.R., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India

Marketing Research and Market-Focused Production as an Effective Business Tool in Power Sector
subject: Market research; Marketing; Power Sector; Production; Strategy
description: Businesses must devote part of their resources to conducting market and marketing research to make good decisions, which will help expand any business and utilize resources effectively. Understanding the intended clients is essential to successfully operating and expanding a firm. For marketers to comprehend consumer value about the product being supplied and therefore add value to their consumers, it is crucial to have this understanding. Organizations can better influence customers to buy niche goods or corporate services after thoroughly understanding their objectives, requirements, and values. In this situation, it is required to restructure the physical system and the related control and planning systems to provide production the tools it needs to become more competitive and customer-focused, acting as a positive and active production process instead of a reactive one. One of the finest techniques for understanding consumers is market research. It provides basic information that a company may utilize to inform its marketing strategy, facilitating and enhancing sales and marketing. This paper reviews the impact of effective market and marketing research and market-focused manufacturing in the power sector. 2023 IEEE.
creator: Roy A.; Shete A.S.; Tiwari A.; Paliwal M.; Singh S.; Vatsa M.
source: 2023 IEEE Renewable Energy and Sustainable E-Mobility Conference, RESEM 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835031132-7
format: Online
language: English
type: Conference paper
coverage: Roy A., University of Lucknow, Institute of Management Sciences, Lucknow, India; Shete A.S., Krishna Vishwa Vidyapeeth, Krishna Institute of Pharmacy, Karad, India; Tiwari A., Christ (Deemed to Be University), School of Business and Management, Greater Noida, India; Paliwal M., Sri Balaji University, Balaji Institute of International Business(BIIB), Pune, India; Singh S., Sharda University, Sharda School of Business Studies, Greater Noida, India; Vatsa M., Akhildev Ipr and Research Services, Research and Development(R&D), Greater Noida, India

Brain Tumor Classification: A Comparison Study CNN, VGG 16 and ResNet50 Model
subject: Accuracy; Brain tumor; CNN; Custom CNN; ResNet50; VGG 16
description: Brain tumors pose a severe threat to global health and may be lethal. Early detection and classification of brain tumors are essential for successful therapy and better patient outcomes. The good news is that advances in deep learning techniques have shown tremendous promise in medical image analysis, particularly in the detection and classification of brain tumors. Convolutional Neural Networks (CNN), a class of deep learning models, are used to process and analyze visual input, notably images, and movies. They excel in computer vision tasks like object detection, image segmentation, and categorization. Popular and efficient image analysis methods include CNNs. VGG 16 and ResNet 50 are two examples of deep convolutional neural network architectures used for image categorization applications. A number of image identification problems have been successfully solved using the 16 layer VGG 16. ResNet50, a well known 50 layer architecture, employs residual connections to get over the vanishing gradient issue and permits the training of deeper networks. A proprietary CNN model, VGG 16, and ResNet50 were compared in studies to see how well they performed on a dataset. The VGG 16, ResNet50, and the tailored CNN model were the most precise models. As a consequence, VGG 16 accurately detects brain cancers in the dataset that was supplied. Overall, this study highlights the value of deep learning techniques for medical image processing and their potential to improve the accuracy and efficacy of brain tumor diagnosis and treatment. 2023 IEEE.
creator: Sharma A.; Zehra A.; Das A.; Rastogi K.; Agarwal M.; Mascarenhas S.; Jayapriya J.; Vinay M.; Deepa S.
source: 2023 International Conference on Data Science and Network Security, ICDSNS 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030159-5
format: Online
language: English
type: Conference paper
coverage: Sharma A., Christ University, Department of Computer Science, Bengaluru, India; Zehra A., Christ University, Department of Computer Science, Bengaluru, India; Das A., Christ University, Department of Computer Science, Bengaluru, India; Rastogi K., Christ University, Department of Computer Science, Bengaluru, India; Agarwal M., Christ University, Department of Computer Science, Bengaluru, India; Mascarenhas S., Christ University, Department of Computer Science, Bengaluru, India; Jayapriya J., Christ University, Department of Computer Science, Bengaluru, India; Vinay M., Christ University, Department of Computer Science, Bengaluru, India; Deepa S., Christ University, Department of Computer Science, Bengaluru, India

Data Analysis and Machine Learning Observation on Production Losses in the Food Processing Industry
subject: Dairy Products; Data Analysis; Machine Learning; Production Loss; Total Productive Maintenance
description: Food wastage and capturing lineage from production to consumption is a bigger concern. Yielding, storage and transportation areas have evolved to a great extent associated to manufacturing and automation which lead to technical advancements in food processing industry. In such situation, losses are generally observed in the crop production which are sometimes minimal and ignored. However, in some cases these losses are huge and are becoming a threat to the both producers and consumers. Here we considered data related to dairy products and analysed the production losses especially while processing them in the treating unit. Literature on parameters and associated data analysis in the form of graphical representation are provided in the appropriate sections of the paper. Linear regression and correlation were envisaged in view of incorporating machine learning techniques understanding production losses. Karl Pearson's correlation provides an observation related to association of parameters which are desired to be less coupled in terms of employing proposed newer methodology. 2023 IEEE.
creator: Kumar K.P.; Murthy H.; Pillai V.J.; Prathap B.R.; Moses M.; Urs Y.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: Kumar K.P., CHRIST Deemed to Be University, Department of Computer Science and Engineering, Bengaluru, Karnataka, India; Murthy H., CHRIST Deemed to Be University, Department of Electronics and Communication Engineering, Bengaluru, Karnataka, India; Pillai V.J., CHRIST Deemed to Be University, Department of Electronics and Communication Engineering, Bengaluru, Karnataka, India; Prathap B.R., CHRIST Deemed to Be University, Department of Computer Science and Engineering, Bengaluru, Karnataka, India; Moses M., CHRIST Deemed to Be University, Department of Computer Science and Engineering, Bengaluru, Karnataka, India; Urs Y., CHRIST Deemed to Be University, Department of Civil Engineering, Bengaluru, Karnataka, India