Prior Cardiovascular Disease Detection using Machine Learning Algorithms in Fog Computing
subject: Decision tree; Fog computing; Heart disease; K- Nearest Neighbor; Logistic regression; Random Forest; Support Vector Machine
description: The term latent disease refers to an infection that does not show symptoms but remains forever. In this paper, proposed a novel methodology for addressing latent diseases in machine learning by integrating fog computing techniques. Here there is a link between HIV to heart disease, that is when a person progresses to the next stage of HIV, a plague infection develops, causing cholesterol deposits to form. Plaque development causes the inside of the arteries to constrict over time, which may stimulate the release of numerous heat shock proteins and immune complexes into the bloodstream, potentially leading to heart disease. Heart disease has long been considered as a significant life-threatening illness in humans. Heart disease is driven by a range of factors including unhealthy eating, lack of physical exercise, gaining overweight, tobacco, as well as other hazardous lifestyle choices. Five different classifiers are used to perform the precision; they are Support vector machine, K-nearest neighbor, decision tree, and random forest, after we have used the classifier, the recommended ideal will split disease into groups which is created based on their threat issues. This will be beneficial to doctors assisting doctors in analyzing the risk factors associated with their patients. 2023 IEEE.
creator: Suresh K.; Subramani R.; Rio N.; Donald C.; Sinha S.; Soman S.
source: 2023 IEEE 3rd Mysore Sub Section International Conference, MysuruCon 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835034035-8
format: Online
language: English
type: Conference paper
coverage: Suresh K., CHRIST (Deemed to Be University), Department of Computer Science, Bangalore, India; Subramani R., CHRIST (Deemed to Be University), Department of Mathematics, Bangalore, India; Rio N., CHRIST (Deemed to Be University), Department of Computer Science, Bangalore, India; Donald C., CHRIST (Deemed to Be University), Department of Computer Science, Bangalore, India; Sinha S., CHRIST (Deemed to Be University), Department of Computer Science, Bangalore, India; Soman S., GITAM University, Department of Computer Science, Bangalore, India

Enhancing Customer Experience and Sales Performance in a Retail Store Using Association Rule Mining and Market Basket Analysis
subject: Apriori algorithm; Association rule mining; Eclat algorithm; FP-Growth algorithm; Market Basket Analysis; Sales performance
description: The retail business grows steadily year after year andemploys an abounding amounts of people globally, especially with the soaring popularity of online shopping. The competitive character of this fast-paced sector has been increasingly evident in recent years. Customers desire to blend the advantages of old purchasing habits with the ease of use of new technology. Retailers must thus guarantee that product quality is maintained when it comes to satisfying customer demands and requirements. This research paper demonstrates the potential value of advanced data analytics techniques in improving customer experience and sales performance in a retail store. Apriori, FP-Growth, and Eclat algorithms are applied in the real time transactional data to discover sociations and patterns in transactional data. Support, confidence and lift ratio parameters are used and apriori algorithm puts out several candidate item sets of increasing lengths and prunes those that fail to offer the assistance that is required threshold. We identified lift values are more when considering frozen meat, milk, and yogurt. if the customer decides to buy any of these items together, there is a chance that the customer will buy 3rd item from that group. Research arrived High confidence score is for Items like Semi Finished Bread and Milk so these products should be sold together, Followed by Packaged food and rolls. As retailers continue to face increasing competition and pressure to improve their operations, The aforementioned techniques may provide you a useful tool to comprehend consumer buying habits and tastes and for utilising that knowledge to come up with data-driven decisions that optimise product placement, enhance customer satisfaction, and attract sales. 2023 IEEE.
creator: Helen Josephine V.L.; Rajan D.
source: 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033509-5
format: Online
language: English
type: Conference paper
coverage: Helen Josephine V.L., School of Business and Management, Christ University, Business Analytics, Bangalore, India; Rajan D., Cmr Institute of Technology, Department of Mca, Bangalore, India

A Dynamic Anomaly Detection Approach for Fault Detection on Fire Alarm System Based on Fuzzy-PSO-CNN Approach
subject: Convolutional Neural Network (CNN); Fire Alarm System; Particle Swarm Optimization (PSO)
description: Early detection is crucial due to the catastrophic threats to life and property that are involved with fires. Sensory systems used in fire alarms are prone to false alerts and breakdowns, endangering lives and property. Therefore, it is essential to check the functionality of smoke detectors often. Traditional plans for such systems have included periodic maintenance; however, because they don't account for the condition of the fire alarm sensors, they are sometimes carried out not when necessary but rather on a predefined conservative timeframe. They describe a data-driven online anomaly detection of smoke detectors, which analyzes the behavior of these devices over time and looks for aberrant patterns that may imply a failure, to aid in the development of a predictive maintenance approach. The suggested procedure begins with three steps: preprocessing, segmentation, and model training. A pre-processing unit can enhance data quality by compensating for sensor drifts, sample-to-sample volatility, and disturbances (noise). The proposed approach normalizes the data in preparation. The smoke source can be detected by using segmentation to differentiate it from the background. Following segmentation, Fuzzy-PSO-CNN is used to train the models. CNN and PSO, two of the most used alternatives, are both outperformed by the proposed method. 2023 IEEE.
creator: Kanagaraj K.; Amruthaluru U.; Selvaganesan J.; Singh Y.; Janakiraman V.; Chauhan A.
source: International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 1124-1129.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835031398-7
format: Online
language: English
type: Conference paper
coverage: Kanagaraj K., Srm Institute of Science and Technology, Department of Computational Intelligence, Chennai, India; Amruthaluru U., Vardhaman College of Engineering, Department of Cse, Telangana, India; Selvaganesan J., Veltech Rangarajan Dr Sagunthala R&d Institute of Science and Technology, Department of Electronics and Communication Engineering, Chennai, India; Singh Y., Mausam Bhawan, India Meteorological Department, New Delhi, India; Janakiraman V., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Mechanical Engineering, Chennai, India; Chauhan A., School of Sciences, Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India

Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization
subject: Cricket analytics; Cricket prediction; Machine Learning; Player Classification; Regression and Classification; Score prediction
description: Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket. 2023 IEEE.
creator: Suguna R.; Praveen Kumar Y.; Suriya Prakash J.; Neethu P.S.; Kiran S.
source: 2023 IEEE 3rd Mysore Sub Section International Conference, MysuruCon 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835034035-8
format: Online
language: English
type: Conference paper
coverage: Suguna R., Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology, Department of Computer Science and Engineering, Chennai, India; Praveen Kumar Y., Vidhya Jyothi Institute of Technology, Department of Computer Science and Engineering, Hyderabad, India; Suriya Prakash J., JAIN (Deemed-to-be-University), Department of Computer Science and Engineering, Bengaluru, India; Neethu P.S., CHRIST (Deemed to Be University), School of Engineering and Technology, Bengaluru, India; Kiran S., Nitte Meenakshi Institute of Technology, Department of Mathematics, Bangalore, India

The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations
description: The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a fine-grained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) and (ii) silver lining (SL). To provide a more comprehensive understanding, both orientations are further sub-categorized into intrinsic and extrinsic, with three degrees of severity - (i) mild, (ii) moderate, and (iii) alarming. We also meticulously categorize hallucination into six types: (i) acronym ambiguity, (ii) numeric nuisance, (iii) generated golem, (iv) virtual voice, (v) geographic erratum, and (vi) time wrap. Furthermore, we curate HallucInation eLiciTation (), a publicly available dataset comprising of 75,000 samples generated using 15 contemporary LLMs along with human annotations for the aforementioned categories. Finally, to establish a method for quantifying and to offer a comparative spectrum that allows us to evaluate and rank LLMs based on their vulnerability to producing hallucinations, we propose Hallucination Vulnerability Index (HVI). Amidst the extensive deliberations on policy-making for regulating AI development, it is of utmost importance to assess and measure which LLM is more vulnerable towards hallucination. We firmly believe that HVI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making. In conclusion, we propose two solution strategies for mitigati…
creator: Rawte V.; Chakraborty S.; Pathak A.; Sarkar A.; Tonmoy S.M.T.I.; Chadha A.; Sheth A.; Das A.
source: EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, pp. 2541-2573.
publisher: Association for Computational Linguistics (ACL)
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-889176060-8
format: Online
language: English
type: Conference paper
coverage: Rawte V., AI Institute, University of South Carolina, United States; Chakraborty S., Christ University, India; Pathak A., Christ University, India; Sarkar A., Christ University, India; Tonmoy S.M.T.I., Islamic University of Technology, Bangladesh; Chadha A., Stanford University, United States, Amazon AI, United States; Sheth A., AI Institute, University of South Carolina, United States; Das A., AI Institute, University of South Carolina, United States

Lane Detection using Kalman Filtering
subject: Autonomous Driving; Driverless; Kalman Filter; Lane Detection
description: Autonomous vehicles are the future of transportation. Modern high-tech vehicles use a sequence of cameras and sensors and in order to assess their atmosphere and aid to the driver by generating various alerts. While driving, it is always a challenging task for drivers to notice lane lines on the road, especially at night time, it becomes more difficult. This research proposes a novel way to recognize lanes in a variety of environments, including day and night. First various pre-processing techniques are used to improve and filter out the noise present in the video frames. Then, a sequence of procedure with respect to lane detection is performed. This stable lane detection is achieved by Kalman filter, by removing offset errors and predict future lane lines. 2023 Elsevier B.V.. All rights reserved.
creator: Subramani R.; Suresh K.; Donald C.; Vijayalakshmi C.
source: Procedia Computer Science, Vol-230, pp. 598-607.
publisher: Elsevier B.V.
date: 2023-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 18770509
format: Online
language: English
type: Conference paper
coverage: Subramani R., Department of Mathematics, CHRIST (Deemed to be University, Bengaluru, India; Suresh K., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Donald C., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Vijayalakshmi C., Department of Statistics and Applied Mathematics, Central University of Tamil Nadu, Thiruvarur, India

An Enhanced Data-Driven Weather Forecasting using Deep Learning Model
subject: Convolutional Neural Network; Deep Learning; Weather Forecasting; Weather Prediction
description: Predicting present climate and the evolution of the ecosystem is more crucial than ever because of the huge climatic shift that has occurred in nature. Weather forecasts normally are made through compiling numerical data on from the atmospheric state at the moment and also applying scientific knowledge in the atmospheric processes to forecast on how the weather atmosphere would evolve. The most popular study subject nowadays is rainfall forecasting because of complexity in handling the data processing in addition to applications in weather monitoring. Four different state temperature data were collected and applied deep learning methods to predict the temperature level in the forthcoming months. The results brought out with the accuracy from 92.5% to 97.2% for different state temperature data. 2023 IEEE.
creator: Krishnasamy L.; Anand D.V.; Vigneshwaran T.; Pathak L.K.; Maheswaran S.
source: 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033509-5
format: Online
language: English
type: Conference paper
coverage: Krishnasamy L., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Anand D.V., Kongu Engineering College, Department of Information Technology, Erode, India; Vigneshwaran T., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Pathak L.K., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Maheswaran S., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India

A Study on the Factors Affecting Infants' Health-Related Issues and Child Mortality using Machine Learning
subject: Child mortality; decision support; infants' health; machine learning; predictive modeling; risk assessment
description: Child mortality and infant health-related issues remain significant challenges worldwide. Understanding the factors that influence these outcomes is crucial for implementing effective interventions and improving child health outcomes. In this study, we employ machine learning techniques to identify and analyze the key factors affecting infants' health-related issues and child mortality. Further, we identify several significant factors that influence infants' health-related issues and child mortality. These factors include maternal health indicators, access to healthcare services, socioeconomic status, environmental factors, and demographic characteristics. The machine learning models provide insights into the relative importance of these factors, enabling policymakers and healthcare professionals to prioritize interventions and allocate resources effectively. Additionally, we investigate the potential interaction effects among these factors and their impact on child health outcomes. This analysis helps in understanding the complex relationships and causal pathways involved in infants' health-related issues and child mortality. The findings of this study contribute to the existing knowledge by leveraging machine learning techniques to identify and analyze the factors affecting infants' health-related issues and child mortality. The insights gained from this research can inform evidence-based policies and interventions aimed at reducing child mortality rates and improving infant health outcomes globally. By addressing the underlying factors identified through this study, we can work towards achieving better health outcomes for infants and reducing the burden of child mortality worldwide. 2023 IEEE.
creator: Verma I.; Prasad S.K.
source: 2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 615-624.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030541-8
format: Online
language: English
type: Conference paper
coverage: Verma I., School of Computing Science and Engineering, Galgotias University, UP, Greater Noida, India, CHRIST (Deemed to be university) Delhi NCR, Ghaziabad, India; Prasad S.K., School of Computing Science and Engineering, Galgotias University, UP, Greater Noida, India

An Integrated and Optimized Fog Computing enabled Framework to minimize Time Complexity in Smart Grids
subject: Cloud Computing; Edge Computing; Fog Computing; Smart Grid
description: A distributed computing paradigm known as 'cloud computing'works as a connection between IoT devices and cloud data centres. The environment system model in this work is on basis of clouds and fog and includes smart grids, which we explore. Prior to understanding the use of fog computing in smart grids we discuss about various features of cloud computing and talk about how to manage the connection between fog and cloud computing. Along with the usual performance of low latency, low cost, and high intelligence, the distinctive characteristics and service scenarios are also explored. Based on the outcome of the simulation, it appears that our suggested PSO-SA algorithm outperforms other optimization algorithms. It recorded a least mean response time of 3.86 seconds only. While the model build up delay was 4.6 seconds, the model execution delay was also found to be only 4.9 seconds with PSO-SA method. The improved efficiency of the technique can be credited to the best aspects of particle swarm optimisation (PSO) and a modified inertia weight obtained by simulated annealing. 2023 IEEE.
creator: Nag R.; Samantraj A.S.; Mishra S.; Sharma V.
source: Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 455-459.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030448-0
format: Online
language: English
type: Conference paper
coverage: Nag R., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Samantraj A.S., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Mishra S., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., CHRIST (Deemed to Be University), Computer Science Department, Delhi NCR, India

AUTONOMOUS IOT MOVEMENT IN HOSTILE AREAS USING ROBOTICS AND DEEP FEDERATED ALGORITHMS
subject: Autonomous IoT; Deep Federated Algorithms; Hostile Areas; Robotics
description: Innovative solutions are required when Internet of Things (IoT) devices are deployed in hostile or difficult locations to ensure dependable and effective operation. In order to enable autonomous IoT mobility in such challenging circumstances, this study suggests a novel approach integrating robotics and deep federated algorithms. Robotics and IoT can work together to create a system that can adapt to dangerous environments, extreme weather conditions, and unexpected terrain. Deep federated algorithms further improve system performance by facilitating dispersed device collaboration for learning while protecting data privacy. The suggested framework covers the issues of communication stability, energy optimization, and real-time decision-making. We illustrate the practicality of this strategy in strengthening the dependability and efficiency of IoT deployments in hostile situations through simulations and tests. 2023 IEEE.
creator: Archana U.; Roslin Dayana K.; Jayadurga R.; Sahaana G.; Praveena K.; Saravanan V.
source: 2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030570-8
format: Online
language: English
type: Conference paper
coverage: Archana U., Department of Computer Science and Design, R.M.K. Engineering College, Tamilnadu, Kavaraipettai, India; Roslin Dayana K., Department of Computer Science and Engineering, R.M.D. Engineering College, Tamilnadu, Kavaraipettai, India; Jayadurga R., Department of Computer Science, Christ University, Yeshwantpur(YPR) Campus, Karnataka, Bengaluru, India; Sahaana G., Department of ECE, Veltech Multitech Dr Rangarajan Dr Shakuntala Engineering College, Tamilnadu, Chennai, India; Praveena K., Department of ECE, Mohan Babu University, Erstwhile Sree Vidyanikethan Engineering College, Andhra Pradesh, Tirupati, India; Saravanan V., Department of Computer Science, Dambi Dollo University, Oromia Region, Dambi Dollo, Ethiopia

Mapping the Landscape of Business Intelligence Research: A Bibliometric Approach
subject: Diamonds; Estimated values; Machine learning; Regression Models and Cross validation
description: The integration of Business Intelligence (BI) is an essential element in contemporary enterprises, facilitating the conversion of voluminous data into valuable insights to support informed decision-making. Consequently, a considerable body of literature has been devoted to investigating the utilization of Business Intelligence (BI) in enhancing company efficiency and competitiveness. The present investigation employs bibliometric methods as a means to examine the research pertaining to Business Intelligence (BI). This includes an examination of the main writers and universities, publication patterns, and the intellectual framework of the domain. This investigation centers on the timeframe spanning from 2000 to 2022 and scrutinizes a corpus of 3729 Scopus articles pertaining to business intelligence. The findings suggest that the domain of Business Intelligence (BI) has experienced a substantial expansion recently. The study's results reveal significant contributors, establishments, nations, and references in the discipline, along with developing research patterns and prospects for further investigation. In general, this research emphasizes the significance of bibliometric evaluation as a means of comprehending the present status of BI research and discovering approaches to enhance the utilization of BI in contemporary organizational decision-making procedures. This study has the potential to provide valuable insights into the present state of research within the field, pinpoint significant trends and themes, and highlight potential avenues for future research. 2023 IEEE.
creator: Basha M.S.A.; Raju R.P.K.; Uma Devi A.; Raja S.; Maheshwari A.
source: 7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835034314-4
format: Online
language: English
type: Conference paper
coverage: Basha M.S.A., Gitam School of Business, Gandhi Institute of Technology and Management, Deemed to Be University, Bengaluru, India; Raju R.P.K., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Uma Devi A., Srm Valliammai Engineering College, Department of Management Studies, Chengalpattu, India; Raja S., School of Management, Vel-Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology, Avadi, India; Maheshwari A., School of Commerce, Finance and Accountancy Christ (Deemed to Be University), Ghaziabad, India

Optimizing Kidney Ultrasound images through Pre-Processing Filters
subject: edge detection; enhancement; Filters; Kidney images; Mean Standard Error (MSE); noise; Peak-Signal-Noise-Ratio (PSNR); Pre-processing; quality; Ultrasound (US)
description: Medical image processing and analysis have greatly advanced in the past decade, significantly contributing to the diagnosis of various diseases.However, It is crucial to address the need for effective data management in the medical field due to the significant rise in data generation and storage. It necessitates the exploration of compression methods as a means of achieving efficient data handling. Consideration should be given to image processing approaches to minimize redundancy. Ultrasound imaging has gained importance in recent years, but the presence of artifacts in ultrasound images has complicated diagnoses. An evaluation has been performed to identify appropriate Pre-processing techniques for kidney images before extracting kidney features. Observing the sensitivity and calculating the PSNR and MSE of the filtered image are used to assess the applied methods. The results indicate that the median filter is ideal for image quality enhancement, while the Sobel filter is highly effective in detecting kidney edges. 2023 IEEE.
creator: Kausar F.; Ramamurthy B.
source: 2023 1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835034279-6
format: Online
language: English
type: Conference paper
coverage: Kausar F., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, 560029, India; Ramamurthy B., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, 560029, India

Image Analysis of MRI-based Brain Tumor Classification and Segmentation using BSA and RELM Networks
subject: Brain Tumor Segmentation; Central Nervous System (CNS); FuzzyClustering Method (FCM)
description: Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. Due to the sheer volume of MRI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-RELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-RELM is used to train the models with the input features. The proposed technique outperforms BSA and RELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method. 2023 IEEE.
creator: Saranya R.; Pushpakumar R.; Srinivasan P.; Aparna N.; Karthika K.; Chauhan A.
source: 2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 523-528.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835034023-5
format: Online
language: English
type: Conference paper
coverage: Saranya R., Department of Computer science with Data Analytics, PSG College of Arts and Science, Coimbatore, India; Pushpakumar R., Department of Information Technology, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu, Chennai, India; Srinivasan P., Department of ECE, Sona College of Technology, Tamilnadu, Salem, India; Aparna N., Department of Biomedical Engineering, Dhaanish Ahmed Institute of Technology, Tamil Nadu, Veerappanur, India; Karthika K., Civil Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Chauhan A., Department of Life Sciences, School of Sciences, CHRIST (Deemed to be university), Karnataka, Bengaluru, India

Wearable Leaf-Shaped Slotted Antenna Including Human Phantom for WBAN Applications
subject: Complete ground; human phantom; ISM; Leaf pattern; Slot antenna; WBAN
description: A 5.8 GHz leaf-shaped slotted antenna for Wireless Body Area Network (WBAN) applications is presented in this piece of content. The leaf structure includes tri leaves, having a complete ground plane at the lowest floor and a central circle slot. The suggested antenna is 60 mm by 60 mm by 1.16 mm in total dimensions. The ISM (Industrial Science and Medical) band frequency of 5.8 GHz is covered by this antenna's radiation range of 5.5 to 6.4 GHz. The radiated pattern, efficiency, S11 magnitude and gain were the different attributes of the leaf-patterned slot antenna. The creation of a stylish leaf-shaped antenna that can be incorporated into clothing designs is the main goal of this project. This antenna may be used in difficult situations because of its flexible base and conductive fabric. The method considers the needs of wearable antennas, such as the impact of human interactions on this antenna, as well as the opposite. 2023 IEEE.
creator: Annalakshmi T.; Neethu P.S.; Revathi G.; Vanitha L.; Sushanth S.G.; Joy S.
source: IEEE 9th International Conference on Smart Structures and Systems, ICSSS 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835038420-8
format: Online
language: English
type: Conference paper
coverage: Annalakshmi T., S.A.Engineering College, Ece Department, Chennai, India; Neethu P.S., School of Engineering and Technology Christ (Deemed to Be University), Ece Department, Bangalore, India; Revathi G., Saveetha School of Engineering, Ece Department, Chennai, India; Vanitha L., S.A.Engineering College, Ece Department, Chennai, India; Sushanth S.G., School of Engineering and Technology Christ (Deemed to Be University), Bangalore, India; Joy S., New Horizon College of Engineering, Ece Department, Bangalore, India

Performance Evaluation of OTFS Under Different Channel Conditions for LEO Satellite Downlink
subject: Detection; Doppler; LMMSE; Low Earth Orbit; MRC; OTFS; Satellite
description: Orthogonal Time Frequency Space (OTFS) modulation scheme is being actively pursued as a viable alternative to Orthogonal Frequency Division Multiplexing (OFDM) modulation scheme in future wireless standards due to the inherent ability of OTFS to mitigate the Doppler effects in high mobility scenarios. The inclusion of Non Terrestrial Network (NTN) in Release 17 of 3GPP (3rd generation partnership project) New Radio (NR) standard signifies the vital role of Satellite Communications to achieve coverage extension, capability augmentation and seamless global connectivity. In this context, it becomes important to study the suitability of OTFS modulation scheme with respect to satellite channel scenarios. In this paper, we consider the downlink channel scenarios defined by 3GPP NR NTN for Low Earth Orbit (LEO) satellites at sub-6 GHz and millimetre wave frequencies for evaluating the performance of OTFS modulation schemes. Simulation results using LMMSE (Linear Minimum Mean Square Error) and MRC (Maximum Ratio Combining) detection algorithms confirm that OTFS modulation is highly robust against Doppler effects and performs consistently across all channel conditions. From simulation results, it is observed that the performance of iterative MRC detection is better than LMMSE for 16QAM and 64QAM modulation schemes by achieving respective gains of around 5 dB and 10 dB for corresponding Bit Error Rate (BER) values of 0.01 and 0.1. 2023 IEEE.
creator: Devarajalu S.K.; Jose D.
source: Proceedings - 10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835032967-4
format: Online
language: English
type: Conference paper
coverage: Devarajalu S.K., Christ University, Department of Ece, Bengaluru, India; Jose D., Christ University, Department of Ece, Bengaluru, India

Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm
subject: Feature Extraction; Image Classification; Lung Cancer; Machine Learning; Radiomics; Selection
description: Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada Boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GBM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably. 2023 IEEE.
creator: Rajesh M.N.; Tanni N.; Baig Z.T.
source: Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023, pp. 1479-1485.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835034233-8
format: Online
language: English
type: Conference paper
coverage: Rajesh M.N., Jain (Deemed-to Be-University), Electronics and Communication Engineering, Bengaluru, India; Tanni N., Christ (Deemed to Be University), B.Tech in Computer Science and Engineering (Data Science), Bengaluru, India; Baig Z.T., Amity University, Dept of It, Taskent, Uzbekistan

Predicting the Cerebral Blood Flow Change Condition during Brain Strokes using Feature Fusion of FMRI Images and Clinical Features
subject: deep learning; feature fusion; Imaging; prediction; strokes
description: By fusing clinical information with functional magnetic resonance imaging (fFMRI) pictures, this study describes a novel method for predicting changes in cerebral blood flow during brain strokes. The FMRI data and patient-specific variables, such as age, gender, and medical history, are combined via feature fusion in the proposed technique. As a result, the model developed can accurately forecast changes in cerebral blood flow that occur during brain strokes. The efficiency of the suggested strategy is shown by experimental findings. The performance of the model is greatly enhanced when FMRI data and clinical characteristics are combined as opposed to just one data source. The findings of this study have important ramifications for increasing the accuracy of stroke diagnosis and treatment and, eventually, for bettering patient outcomes. The experimental results showed that the proposed method a high level of accuracy in predicting changes in cerebral blood flow after brain strokes. The performance of the model was much enhanced by combining clinical characteristics with FMRI data as opposed to using only one of these data sources. This emphasizes the value of including pertinent clinical information in the diagnosis and management of stroke. 2023 IEEE.
creator: Sharma V.; Sinha A.; Wiryaseputra M.; Kumar B.; Kumar T.R.; Alkhayyat A.
source: 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033509-5
format: Online
language: English
type: Conference paper
coverage: Sharma V., Christ University, Institute of Information Technology, Delhi Campus, India; Sinha A., Ignou, Department of Computer Science and Information Technology, New Delhi, India; Wiryaseputra M., Soegijapranata Catholic University, Semarang, Indonesia; Kumar B., Amity Univeristy, Department of Computer Science, Jharkhand, India; Kumar T.R., Iit Madras, Computational Mathematics & Data Science, India; Alkhayyat A., The Islamic University, College of Technical Engineering, Najaf, Iraq

Implementing Ensemble Machine Learning Techniques for Fraud Detection in Blockchain Ecosystem
subject: Block Chain; Ensemble ML; Fraud Detection
description: A new era of digital innovation, notably in the area of financial transactions, has been conducted in by the rise pertaining to block-chain technology. Although the decentralized nature of blockchain technology renders it prone to fraud, it has been praised for its capacity to offer a safe and transparent platform for financial transactions. The integrity of the entire blockchain network may be compromised by fraudulent transactions, which may also damage user and stakeholder trust. This study aims to assess machine learning's efficacy in detecting fraudulent transactions within blockchain networks and identifying the most effective model. To achieve its objectives, this study used a combination of data collection, data preprocessing, and machine learning techniques. The data used in this study was dataset of blockchain transactions and pre-processed using techniques such as feature engineering and normalization. Then trained and evaluated using several machine learning models, including Logistic Regression (LR), Naive Bayes (NB), SVM, XGboost, LightGBM, Random Forest(RF), and Stacking, in order to determine their effectiveness in detecting fraudulent transactions. XGBoost demonstrated the highest accuracy of 0.944 in the stacking model, establishing it as the top-performing model, closely followed by Light GBM. The study's discoveries offer significant practical implications for advancing fraud detection methods in blockchain networks. By pinpointing the most efficient machine learning model and crucial predictive fraud features, this research provides vital insights for refining precise detection algorithms, enhancing blockchain network security, and broadening their reliability across various applications. 2023 IEEE.
creator: Josephine H.V.L.; Kumar P.T.; Joy J.; Mansurali A.
source: 2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 1980-1985.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835034023-5
format: Online
language: English
type: Conference paper
coverage: Josephine H.V.L., Business analytics, School of business and Management, Christ University, Karnataka, Bangalore, India; Kumar P.T., Lean Operations and Systems., School of business and management, Christ University, Karnataka, Bangalore, India; Joy J., Lean Operations and Systems., School of business and management, Christ University, Karnataka, Bangalore, India; Mansurali A., Department of Management, Central University of Tamil Nadu, Tamil Nadu, Thiruvarur, India

Evaluating Energy Consumption Patterns in a Smart Grid with Data Analytics Models
subject: Accuracy; Big data analytics; Decision tree; Effective energy management; Smart grid
description: With the rapid pace of technological advancement, it is a well established fact that in todays era, economical and industrial development go hand in hand with the growth in technology. Today, massive amounts of data are generated everyday and are only growing exponentially. The collected data, whether structured or unstructured, could prove to be very beneficial in terms of improving operational efficiency by analyzing and extracting valuable information to find patterns to optimize asset utilization and improve asset intelligence. Big data analytics can very well contribute to the evolution of the digital electrical power industry. The objective of this paper is to explore how smart grid technology can be enhanced by leveraging big data analytics. Different predictive models are used for the purpose. Among them, decision tree model outperformed others recording a training and tetsing accuracy of 94.4% and 92.7% respectively while noting a least execution latency of only 4.3 seconds. 2023 IEEE.
creator: Mishra S.; Sivani T.; Swain T.; Sharma V.; Alkhayyat A.
source: 2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 1072-1077.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030541-8
format: Online
language: English
type: Conference paper
coverage: Mishra S., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sivani T., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Swain T., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., CHRIST(Deemed to be University), Delhi-NCR, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq

Brain Tumor Detection and Classification Using a Hyperparameter Tuned Convolutional Neural Network
subject: Brain Tumor detection; Convolutional neural network; Data pre-processing; MRI scans; Prediction
description: Brain tumor detection using MRI scans when integrated with a deep learning approach can be immensely applied in identifying the tumor at early stages, with minimum medical professional aid. This research paper aims to develop an advanced predictive model that accurately classify brain tumors as benign or malignant using MRI scans. Here, a novel convolutional neural network (CNN) model is proposed to automate tumor detection and improve diagnosis accuracy. The model used a dataset of around 7000 brain cancer data classified into 4 labels which include glioma, meningioma, pituitary, and no tumor. Data wrangling and pre-processing are then applied to unify the images into a single format and remove any inconsistencies. Further the records are segregated into train and test samples with a 70-30 split. The proposed model recorded an optimum accuracy of 94.82%, precision of 94.2%, recall value of 93.7% and f-score metric of 93.9% respectively. In conclusion, the paper concluded that the proposed model can be applied to enhance the precision of both brain tumor diagnosis and prognosis. 2023 IEEE.
creator: Banerjee A.; Jaiswal K.; Biswas T.; Sharma V.; Bal M.; Mishra S.
source: Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 502-506.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030448-0
format: Online
language: English
type: Conference paper
coverage: Banerjee A., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Jaiswal K., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Biswas T., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., CHRIST (Deemed to Be University, Computer Science Department, Delhi NCR, India; Bal M., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India