An Efficient and Robust Explainable Artificial Intelligence for Securing Smart Healthcare System
Explainable Artificial Intelligence; IoT; Security; Smart Healthcare
The advent of IoT technologies has a tremendous impact on the healthcare sector enabling efficient monitoring of patients and utilizing the data for better analytics. Since every activity related to a patients health is monitored, the focus on smart healthcare applications has significantly transferred from service provision to a security perspective. As most healthcare applications are automated security plays a vital role. The technique of machine learning has been widely used in securing smart healthcare systems. The major challenge is that these applications require high-quality labeled images, which are difficult to acquire from real-time security applications. Further, it highly time-consuming and cost-expensive process. To address these constraints, in this paper, we define an efficient and robust explainable artificial intelligence technique that takes a small quantity of labeled data to train and de-ploy the security countermeasure for targeted healthcare applications. The proposed approach enhances the security measure through the detection of drifting samples with explainability. It is observed that the proposed approach improved accuracy, high fidelity, and explanation measures. Also, this approach is proven to be considerably resistant against numerous security threats. 2023 IEEE.
Seetharaman T.; Sharma V.; Balamurugan B.; Grover V.; Agnihotri A.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 1066-1071.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/SmartTechCon57526.2023.10391664 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184799769&doi=10.1109%2fSmartTechCon57526.2023.10391664&partnerID=40&md5=c133e7ba5cc502b676dd4c3bda212643
Seetharaman T., Department of CSE, CMR University, Bangalore, India; Sharma V., Computer Science Department, CHRIST (Deemed to be University), India; Balamurugan B., Shiva Nadar University, India; Grover V., School of Management, Noida Institute of Engineering and Technology, India; Agnihotri A., School of Business, Galgotias University, India
Unraveling Campus Placement Success Integrating Exploratory Insights with Predictive Machine Learning Models
EDA; Machine Learning; Performance analysis; Placement Prediction; Students
The dynamics of campus placements have garnered considerable attention in recent years, with educational institutions, students, and employers all keenly invested in understanding the factors that drive successful recruitment. This surge in interest stems from the potential implications for academic curricula, student preparation, and hiring strategies. In this study, we aimed to unravel the myriad factors that influence a student's placement success, drawing from a comprehensive dataset detailing a range of academic and demographic attributes. Our methodology combined thorough exploratory data analysis with advanced predictive modeling. The exploratory phase unveiled notable patterns, particularly highlighting the roles of gender, academic performance analysis, Degree and MBA specialization in placement outcomes. In the predictive modeling phase, the spotlight was on state-of-the-art machine learning models, with a particular emphasis on their capacity to forecast placement success. Notably, algorithms like Logistic Regression and Support Vector Machines not only confirmed the insights from our exploratory analysis but also showcased remarkable predictive prowess, with accuracy scores nearing perfection. These findings not only demonstrate the capabilities of machine learning in the academic and recruitment spheres but also emphasize the enduring importance of core academic achievements in influencing placement outcomes. As a prospective direction, future research might benefit from examining how placement trends evolve over time and integrating qualitative insights to provide a holistic view of the campus recruitment process. 2023 IEEE.
Amzad Basha M.S.; Kumar Raju R.P.; Oveis P.M.; Sarim M.
7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023 - Proceedings
Amzad Basha M.S., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Kumar Raju R.P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Oveis P.M., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India; Sarim M., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India
A Comparative Analysis of LSB & DCT Based Steganographic Techniques: Confidentiality, Contemporary State, and Future Challenges
Data Hiding; Data Security; Image Steganography; Imperceptibility; Information Hiding; LSB insertion; Steganalysis
In order to maintain anonymity and security, the steganography is the technique of cloaking confidential data within what seems like harmless digital material. Several steganographic methods have been established devised over time, but those centered around the discrete cosine transformation (DCT) and the least significant bit (LSB) have drawn the most consideration. In this study, two common steganographic methods are compared and contrasted with an emphasis on the secrecy they can keep, the usage they are now receiving, and any potential difficulties in the future. As an alternative, the DCT-based method uses the frequency domain properties of cover media to obfuscate hidden information. Since it spreads the concealed information across several frequency coefficients, it provides greater security than LSB-based techniques. The resilience and imperceptibility of the concealed data are improved by a variety of DCT-based algorithms, such as the modified quantization and matrix encoding approaches, which we explore in detail. We also give a general summary of both approaches'current state in terms of their application, constraints, and areas in which they may be used. We evaluate the benefits and drawbacks of each approach, considering elements like payload size, computing difficulty, and detection resistance. 2023 IEEE.
Upreti K.; Verma A.; Parashar J.; Vats P.; Verma A.; Singh J.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 1581-1588.
Upreti K., CHRIST (Deemed to Be University) Delhi NCR, Department of Computer Science, Ghaziabad, India; Verma A., Department of Computer Science & Engineering ADGITM, New Delhi, India; Parashar J., Department of Computer Science & Engineering ADGITM, New Delhi, India; Vats P., SCSE Manipal University Jaipur Jaipur, Department of CSE, Rajasthan, India; Verma A., KIET Group of Institutions Delhi -NCR, Department of Computer Applications, Ghaziabad, India; Singh J., School of CSE and Technology, Bennett University Greater, Noida, India
Prior Cardiovascular Disease Detection using Machine Learning Algorithms in Fog Computing
Decision tree; Fog computing; Heart disease; K- Nearest Neighbor; Logistic regression; Random Forest; Support Vector Machine
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.
Suresh K.; Subramani R.; Rio N.; Donald C.; Sinha S.; Soman S.
2023 IEEE 3rd Mysore Sub Section International Conference, MysuruCon 2023
https://doi.org/10.1109/MysuruCon59703.2023.10396992 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184811941&doi=10.1109%2fMysuruCon59703.2023.10396992&partnerID=40&md5=8241092230fbdf9cb72cdca907f410e6
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
Apriori algorithm; Association rule mining; Eclat algorithm; FP-Growth algorithm; Market Basket Analysis; Sales performance
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.
2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
https://doi.org/10.1109/ICCCNT56998.2023.10307411 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179844042&doi=10.1109%2fICCCNT56998.2023.10307411&partnerID=40&md5=07a92cac65fcfa33b862d61a1160b5b5
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
Convolutional Neural Network (CNN); Fire Alarm System; Particle Swarm Optimization (PSO)
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.
Kanagaraj K.; Amruthaluru U.; Selvaganesan J.; Singh Y.; Janakiraman V.; Chauhan A.
International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 1124-1129.
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
Cricket analytics; Cricket prediction; Machine Learning; Player Classification; Regression and Classification; Score prediction
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.
Suguna R.; Praveen Kumar Y.; Suriya Prakash J.; Neethu P.S.; Kiran S.
https://doi.org/10.1109/MysuruCon59703.2023.10396955 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184821608&doi=10.1109%2fMysuruCon59703.2023.10396955&partnerID=40&md5=69cf6c86786d9dc70ec4b7a34b10ed00
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
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 mitigating hallucinations. 2023 Association for Computational Linguistics.
Rawte V.; Chakraborty S.; Pathak A.; Sarkar A.; Tonmoy S.M.T.I.; Chadha A.; Sheth A.; Das A.
EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, pp. 2541-2573.
Association for Computational Linguistics (ACL)
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
Autonomous Driving; Driverless; Kalman Filter; Lane Detection
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.
Subramani R.; Suresh K.; Donald C.; Vijayalakshmi C.
Procedia Computer Science, Vol-230, pp. 598-607.
https://doi.org/10.1016/j.procs.2023.12.115 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184082311&doi=10.1016%2fj.procs.2023.12.115&partnerID=40&md5=466a6174636b3cb1d0f87d66fa82abf1
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
Convolutional Neural Network; Deep Learning; Weather Forecasting; Weather Prediction
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.
Krishnasamy L.; Anand D.V.; Vigneshwaran T.; Pathak L.K.; Maheswaran S.
https://doi.org/10.1109/ICCCNT56998.2023.10307319 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179841437&doi=10.1109%2fICCCNT56998.2023.10307319&partnerID=40&md5=6d8c79ae8d95be2f6ff764096fd0a33a
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
Child mortality; decision support; infants' health; machine learning; predictive modeling; risk assessment
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.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 615-624.
https://doi.org/10.1109/SmartTechCon57526.2023.10391399 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184811405&doi=10.1109%2fSmartTechCon57526.2023.10391399&partnerID=40&md5=c2a75a52748fb1dbcc35e83a4f0c9370
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
Cloud Computing; Edge Computing; Fog Computing; Smart Grid
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.
Nag R.; Samantraj A.S.; Mishra S.; Sharma V.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 455-459.
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
Autonomous IoT; Deep Federated Algorithms; Hostile Areas; Robotics
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.
Archana U.; Roslin Dayana K.; Jayadurga R.; Sahaana G.; Praveena K.; Saravanan V.
2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023
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
Diamonds; Estimated values; Machine learning; Regression Models and Cross validation
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.
Basha M.S.A.; Raju R.P.K.; Uma Devi A.; Raja S.; Maheshwari A.
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
edge detection; enhancement; Filters; Kidney images; Mean Standard Error (MSE); noise; Peak-Signal-Noise-Ratio (PSNR); Pre-processing; quality; Ultrasound (US)
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.
2023 1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023
https://doi.org/10.1109/ICAEECI58247.2023.10370900 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183531754&doi=10.1109%2fICAEECI58247.2023.10370900&partnerID=40&md5=9b10f832d53eb781d5a2eb4faeb32225
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
Brain Tumor Segmentation; Central Nervous System (CNS); FuzzyClustering Method (FCM)
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.
Saranya R.; Pushpakumar R.; Srinivasan P.; Aparna N.; Karthika K.; Chauhan A.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 523-528.
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
Complete ground; human phantom; ISM; Leaf pattern; Slot antenna; WBAN
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.
Annalakshmi T.; Neethu P.S.; Revathi G.; Vanitha L.; Sushanth S.G.; Joy S.
IEEE 9th International Conference on Smart Structures and Systems, ICSSS 2023
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
Detection; Doppler; LMMSE; Low Earth Orbit; MRC; OTFS; Satellite
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.
Proceedings - 10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023
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
Feature Extraction; Image Classification; Lung Cancer; Machine Learning; Radiomics; Selection
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.
Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023, pp. 1479-1485.
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
deep learning; feature fusion; Imaging; prediction; strokes
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.
Sharma V.; Sinha A.; Wiryaseputra M.; Kumar B.; Kumar T.R.; Alkhayyat A.
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