Comparative Analysis of Non-Destructive Silkworm Cocoon Sex Classification using Machine Learning Models Based on X-Ray and Camera Images
image processing; machine learning; sericulture; sex classification; silkworm
Silk production plays a vital role in global economies, with sericulture heavily dependent on efficient seed production processes. Traditional methods involve manually cutting cocoons to classify silkworm sex, which leads to silk damage, labor intensiveness, and potential inaccuracies. In response, non-destructive technologies like X-ray and camera imaging have emerged, enabling sex classification without cocoon damage, thereby enhancing efficiency and reducing manual errors. This study undertakes a comparative analysis of X-ray and camera imaging methods for silkworm sex classification. X-ray imaging demonstrates superior efficiency in extracting detailed features from silkworm pupae, crucial for accurate classification. In contrast, camera imaging excels in the rapid and cost-effective classification of silkworms based on extracted features. The results reveal significant findings: using X-ray imaging model achieves 97.1% accuracy for FC1 and 96.3% accuracy for FC2, employing ensemble learning technique like AdaBoost. Meanwhile, camera imaging achieves an accuracy above 98% for both FC1 and FC2 using XGBoost, showcasing its effectiveness in real-time classification scenarios. Computational time analysis indicates that X-ray imaging is faster in feature extraction, while camera imaging consumes less memory during classification. These findings underscore the practical advantages of non-destructive imaging technologies and machine learning in revolutionizing sericulture practices. By enhancing productivity and sustainability through accurate sex classification of silkworms, these methods contribute significantly to the growth and efficiency of the silk industry. 2024 IEEE.
International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024
Institute of Electrical and Electronics Engineers Inc.
Thomas S., Christ University, Computer Science and Engineering, Bangalore, India; Thomas J., Christ University, Computer Science and Engineering, Bangalore, India
A Novel Approach to Enhance Influencer Marketing in E-commerce: A Cross-A-Siamese Perspective
e-commerce; influencer marketing; social media influencers (SMI)
One of the most notable aspects of the Internet is the fact that the cost of (global) communication has been drastically decreased. Individuals may potentially reach massive audiences with their messages over the Internet due to its widespread use. With the rise of blog services, social networking platforms, etc., people's technological talents are no longer a limiting factor. Data preprocessing, feature selection, and model training should all be done in this sequence of significance. Applying fundamental data preparation techniques guaranteed the data's accuracy and relevancy. Feature selection includes the computation of an influencer's overall rank based on six important criteria, which are used for influencer identification and ranking. Feature retrieval is the first step in training Unified Cross-A-Siamese models. The proposed method outperforms two cutting-edge methods: Attention module and siamese. Accuracy increased by 95.70 percent once the approach was used. 2024 IEEE.
Akram N.; Dahiya S.; Jafersadhiq A.; Akila D.; Girdharwal N.; Chauhan A.
Akram N., Magadh Professional Institute, Patna, India; Dahiya S., Universal Ai University, Department of Management, Maharashtra, Karjat, India; Jafersadhiq A., Kalasalingam Academy Research and Education, Kalasalingam Business School, Department of Business Administration, India; Akila D., Saveetha College of Liberal Arts and Sciences, Simats, Department of Computer Applications, Chennai, India; Girdharwal N., Prestige Institute of Management & Research (PIMR), Department of Management, Indore, India; Chauhan A., Christ (Deemed to be University), Department of Life Sciences, Bengaluru, India
Cyber-Secure Framework for the Insecure Designs in Healthcare Industry
blockchain; cleartext storage; Cyber Security; insecure design; machine learning; sensitive data
Sensitive data protection has been a top priority in the healthcare industry. This has led to the investigation of safe data storage and transaction. Despite various attempts to address this issue, data breaches continue to plague the healthcare industry. This study aims to investigate prevalent storage practices and security methodologies in the healthcare, recognizing the need for a robust framework. The work further extends with design of new security framework for healthcare industry. This framework identifies critical data and implement measures to prevent unauthorized access and data tempering. The industrial hype towards the implementation of adaptive machine learning craves the need for hybrid machine learning approaches to be adapted in the cyber secure framework. In order to improve security and confidentiality in the healthcare sector. Blockchain is used in the proposed cyber secure framework promising integrity of data with the features of immutability. This proposal aims to provide a comprehensive solution to the ongoing problem of protecting medical data. Grenze Scientific Society, 2024.
15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024, Vol-2, pp. 3824-3832.
Mansoor N., Department of computer science, CHRIST (Deemed to be University), Bangalore, India; Sandeep J., Department of computer science, CHRIST (Deemed to be University), Bangalore, India
Exploring the Adaptability of Attention U-Net for Post-operative Brain Tumor Segmentation in MRI Scans
Attention U-Net; Post-operative MRI; Recurrent tumor; Residual connections
This study explores the adaptability of a segmentation model, originally trained on pre-operative MRI data, in post-operative recurrent brain tumor segmentation. We utilized the Attention U-Net model for this study. In pre-operative training, the model achieved a Dice Coefficient of 0.92 and an IOU of 0.86 for brain tumor MRI segmentation. Due to the surgical artifacts in post-operative data, performance reduced with Dice Coefficient of 0.54 and an IOU of 0. To improve the performance, the model's architecture is fine-tuned by introducing dilated convolutions and residual connections. This refinement yielded improvements in results, with a Dice Coefficient of 0.68 and an IOU of 0.62 in the post-operative context. This improvement underscores the need for further research to select and adapt efficient models, retrain specific layers with an extensive collection of post-operative images, and fine-tune model parameters to enhance feature extraction during the encoding phase. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Smart Innovation, Systems and Technologies, Vol-395 SIST, pp. 333-340.
Springer Science and Business Media Deutschland GmbH
Sobha Xavier P., Department of Computer Science and Engineering School of Engineering and Technology, CHRIST University, Bengaluru, India; Sathish P.K., Department of Computer Science and Engineering School of Engineering and Technology, CHRIST University, Bengaluru, India; Raju G., Department of Computer Science and Engineering School of Engineering and Technology, CHRIST University, Bengaluru, India
Investor Perspectives: Evaluating the Impact of CSR on Excess Returns in Financial Companies
CSR; FAMA and French; Net Interest Margin; Portfolio; Return; Risk
This research aims to provide insights into Corporate social responsibility (CSR) performance and its impact on portfolio performance. The research would contribute to the broader understanding of how investors can achieve financial success and positive societal impact through the CSR performance of financial companies. This study uses 56 financial companies data from 20132014 to 20212022. Seemingly unrelated regression has been used to examine the impact of FAMA and French factors on the return of different portfolios. The findings of this research are significant for Banks and NBFCs, which shows that all the factors of the FAMA and French model are significant in showing the portfolios results. This study demonstrates that banks with better CSR performance yield higher expected returns than NBFC portfolios. This finding confirms that increased socially responsible activities yield better returns for banks. It showed that more socially responsible companies provide better financial returns than those not focusing on these issues. This suggests that when companies invest in being responsible and doing good for society, it can lead to better financial results for them and the investors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Springer Proceedings in Business and Economics, pp. 185-205.
Sachdeva S., Christ University, Bengaluru, India; Ramesh L., Christ University, Bengaluru, India
The Influence of Mobile Commerce on Consumer Behavior: A FCM-RF-DNN Analysis
consumer behavior; deep neural networks (DNN); M-commerce
For m-commerce vendors, the difficulty is to decipher what factors impact customer actions in the ubiquitous mobile setting. In addition, companies are attempting to incorporate social media into their mobile approach in some way. This proposed approach to the findings of a qualitative exploratory study regarding the use of social media and smartphones within the framework of mobile commerce. Keep in mind the order of importance while doing data preprocessing, feature selection, and training the model. The usual steps in getting data ready for processing, such as cleaning it, identifying users and sessions, and finding episodes. The IS-DT suggested method's implementation technique is utilized in feature selection. Unified FCM-RF-DNN models need to be trained after features have been retrieved. Two state-of-the-art approaches, RF and DNN, are outperformed by the suggested approach. Following the implementation of the method, accuracy improved by 96.13%. 2024 IEEE.
Akram N.; Dahiya S.; Chittapragada H.; Periyasamy V.M.; Vidhya P.; Chauhan A.
Akram N., Magadh Professional Institute, Patna, India; Dahiya S., Universal Ai University, Department of Management, Karjat, India; Chittapragada H., Madanapalle Institute of Technology & Science, Computer Science & Engineering, Madanapalle, India; Periyasamy V.M., Sona College of Technology, Department of Eee, Salem, India; Vidhya P., M.Kumarasamy College of Engineering, Department of Artificial Intelligence and Data Science, Karur, India; Chauhan A., Christ (Deemed to be University), Department of Life Sciences, Bengaluru, India
Synergizing Insights for Precise Rice Leaf Disease Diagnosis Via Multi-Modal Fusion
Early Blight of rice; Fusarium Wilt of rice; Leaf Spots of rice; Powdery Mildew of rice and Downey Mildew of rice; Rusts of rice
Rice holds a significant position in India, especially in the southern part of the country, where people tend to eat some rice at least once a day. Farmers are facing a huge loss due to diseases in leaf, which is the main problem of agriculture. By using techniques like machine learning, main problems detection can be done. This review, discusses common plant diseases that affect the leaf. Some include Leaf Spots, Rusts, Fusarium Wilt, Early Blight, Powdery Mildew and Downey Mildew. Our research found that machine learning techniques on rice plants make finding diseases on leaves easier. Finally, we concluded that the most accurate method is the Enhanced VGG16, with an accuracy of 99.60% because it is really good at spotting diseases on rice leaves because it's great at recognizing the small details and patterns in leaf pictures. This helps it to tell the diseases apart more accurately and make fewer mistakes in identifying them. 2024 IEEE.
10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024, pp. 1602-1607.
Pally S.C., Christ (Deemed to be University), Dept. of Computer Science and Engineering, Karnataka, Bengaluru, India; Jenefa J., Christ (Deemed to be University), Dept. of Computer Science and Engineering, Karnataka, Bengaluru, India
Optimization-Based Cash Management Model for Microfinance Applications Using GSA and PSO
Cash forecasting; Daily model; GSA; Minimization; Optimization; PSO
Banks and businesses use cash as a means for exchange in finance on a regular basis to please customers. Making decisions about cash management can be challenging because banks must keep significant sums of cash in order to sustain high levels of client satisfaction. In this paper, linear PSO and GSA models are given for estimating the daily cash demand of a bank by taking into account the variables Year of Reference (RY), Years Month (My), Months Day (Dm), Days Week (Dw), Payday Effect Salary (Se), and Holiday Effect (He). Using PSO and GSA in MATLAB, the algorithms for estimating both the model coefficients for short term are implemented from the real data of a specific bank branch. The proposed system's overall cost is minimized using a fitness function. It was discovered that the results are in good accord with the observed data and that the PSO-based cash management model outperformed other models with superior accuracy. The models are then used for future cash management for validation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Smart Innovation, Systems and Technologies, Vol-395 SIST, pp. 221-231.
Alli A., Presidency College, Bengaluru, India; Sheetal, Presidency College, Bengaluru, India; Deepa S., Christ University, Bengaluru, India
Computationally Efficient Machine Learning Methodology for Indian Nobel Laureate Classification
Convolutional Neural Networks (CNNs); Feature Engineering; Haar cascade style; Image Classification; Logistic Regression; Machine Learning (ML); Similarity Checker; Supervised Learning
A computationally efficient methodology for Indian Nobel Laureate classification is proposed in this study, emphasizing the optimization of image categorization through supervised learning techniques. Leveraging advancements in Convolutional Neural Networks (CNNs), the research aims to enhance the efficiency and precision of image classification tasks. The study utilizes Logistic Regression for dataset analysis, initially employing browser extensions for mass downloading categorized image data. Haar cascade classifiers are then used for data wrangling, focusing on facial, nose, and mouth recognition. Following this, feature engineering through wavelet transformation reduces image dimensionality, preparing the dataset for the chosen ML model, Logistic Regression. The primary focus is to simplify technology for improved image categorization. Support Vector Machines (SVM), Random Forest, and Logistic Regression are examined, with Logistic Regression emerging as the most effective model, achieving an accuracy rate of 87.5%. A thorough evaluation using Confusion Matrices reveals Logistic Regression's superior performance in classifying images of Indian Nobel laureates. A strategic up-sampling approach is implemented to address dataset inconsistencies, ensuring balanced representation across classes. The Haar wavelet transform is then applied for feature extraction, optimizing the dataset for ML models. The dataset is split into training and testing sets (80-20), and the three models are trained and evaluated for accuracy. Logistic Regression proves to be the best performer, offering insights into prominent leaders' identification. The research offers a detailed pipeline for data preprocessing, feature engineering, and model assessment, culminating in a robust image categorization system. Logistic Regression emerges as a reliable method for biographical picture identification, demonstrating superior accuracy over SVM and Random Forest. This research underscores the importance of efficient and accurate image classification methodologies for practical applications in real-world scenarios, particularly in recognizing influential leaders. 2024 IEEE.
RAICS - IEEE Recent Advances in Intelligent Computational Systems, No. 2024
Banerjee S., Artificial Intelligence & Machine Learning, Christ Deemed to be University, Department of Computer Science, Bengaluru, India; Joy H.K., Christ Deemed to be University, Department of Computer Science, Bengaluru, India
AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection
Accuracy; Building Safety; Computational Time; Ensemble Learning; Fuzzy Logic Systems; Gradient Boosting Machines; Internet of Things; Latency Detection; Sensor Data; Support Vector Machine
The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. 2024 IEEE.
Raja S.R.; Devi T.R.; Raj J.R.F.; Sankar V.K.; Krishnan R.S.; Venkatalakshmi R.
8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024 - Proceedings, pp. 73-80.
Raja S.R., Hindustan Institute of Technology and Science, Computer Applications, Chennai, India; Devi T.R., Christ University, Department of Computer Science and Engineering, Bangalore, India; Raj J.R.F., Saveetha Institute of Medical and Technical Sciences, Department of Electronics and Communication, Saveetha School of Engineering, Chennai, India; Sankar V.K., Scad College of Engineering and Technology, Department of Civil Engineering, Cheranmahadevi, India; Krishnan R.S., Scad College of Engineering and Technology, Department of Electronics and Communication Engineering, Cheranmahadevi, India; Venkatalakshmi R., Psna College of Engineering and Technology, Department of Information Technology, Dindigul, India
Unveiling the Dynamics: A Performance Analysis of RPL under Congestion in IoT Network
congestion parameters of RPL; ETX and congestion; RPL metrics in Cooja; RPL Protocol
The Routing Protocol for Low Power and Lossy Network (RPL) is a standardized routing protocol for resource constraint devices deployed in diverse applications in Internet of Things (IoT). RPL is the most efficient protocol which is carefully designed to meet energy efficiency of sensor nodes. However, this protocol is prone to network congestion which is one of most crucial bottlenecks of this protocol. In the current study a thorough analysis of effect of congestion on RPL routing metrics are analyzed. We have designed a congestion scenario using Cooja simulator and analyzed its effects on ETX, Power, Duty Cycle through graphs. The results of the experiments finally outline the critical parameters affected due to congestion in RPL. Grenze Scientific Society, 2024.
15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024, Vol-2, pp. 4510-4516.
Srivastava V., Department of Computer Science, Banasthali Vidyapith, Rajasthan, India; Paul A., Department of Computer Science, Banasthali Vidyapith, Rajasthan, India; Sinha S., Department of Computer Science, CHRIST Deemed to be University, Bengaluru, India
Python Driven Keyword Analysis for SEO Optimization
Content marketing; Domain relevancy; Google Autocomplete; Insights; Keyword research; Product development; Python; Real-time data; Search engine optimization (SEO); Trends
Every word or string of words a user types into a search engine has meaning. For example, a user might search for a 'hotel' or a 'hotel in New York City.' Keywords are the standard focus of search engine optimization (SEO), which offers a useful method of gauging demand for specific queries and aiding in a better understanding of how users look for goods, services, businesses, and, eventually, solutions. Any effective SEO strategy must include keyword research, and Python is a strong language that can be used to automate and accelerate the process. This project presents a Python-based keyword research tool that works on real-time data to identify the top searches over a user-specified domain to identify trends and customer needs. It does this by utilizing multiple Python libraries and Google Autocomplete. The Google Autocomplete results for the user-specified domain are first parsed by the tool before it can function. After that, unnecessary keywords are eliminated by filtering and cleaning the results. Subsequently, the remaining keywords are arranged for search volume and domain relevancy. The tool looks for trends by comparing the current keyword rankings with previous data. Thanks to this, users can see which keywords are growing in popularity. By identifying the most commonly asked questions and issues, the tool also offers insights into the needs of its users. The tool is simple and adaptable to each user's unique requirements. It can be used to create keyword lists for content marketing, SEO, and product development, among other uses. 2024 IEEE.
10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024, pp. 1170-1176.
https://doi.org/10.1109/ICACCS60874.2024.10717132 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208626488&doi=10.1109%2fICACCS60874.2024.10717132&partnerID=40&md5=47f383dee48888ea46db26a9d41fb7f9
Kanara A.P., Christ (Deemed to be University), Computer Science Engineering (Specialization in Data Science), Banglore, India; Kumari P., Christ (Deemed to be University), Computer Science Engineering (Specialization in Data Science), Banglore, India; Prathap B.R., Christ (Deemed to be University), Computer Science Engineering (Specialization in Data Science), Banglore, India
Deep Learning Advancements in E-commerce Supply Chain Management in Forecasting and Optimization Strategies
Deep learning; E-commerce; Forecasting; Optimization; Supply chain management
In this study, the influence of deep learning technologies on the optimization of supply chain management in the context of the e-commerce industry is examined. Using a dataset of historical data of sales, inventories, market fluctuations, and customer and supplier details, I investigate the efficiency of different deep learning models to predict demand and facilitate the optimal balance of inventories. Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and a model proposed by the authors are defined and applied, considering their accuracy, precision, recall, and F-1 score. The results show that the proposed model outperforms traditional products, achieving 97.5% of accuracy. In the context of the comparative analysis, the specific features of CNN, LSTM, and RNN are revealed, helping to understand the benefits and drawbacks of each recommendation. As a result, the proposed model proves that deep learning technologies have the power to change the approach to predictive analytics and supply chain management, allowing practitioners to focus on strengths and overcome the weaknesses of their structures. The impact of data preprocessing and hyperparameters is also considered along with the necessity to choose the most appropriate model evaluation technique. In the future, it is possible to implement other complex deep learning models, integrate additional data, and address the problem of data scaling and heterogeneity. In the era of modern technologies, e-commerce organizations should take these findings into consideration to discover the potential of deep learning, improve supply chain performance, reduce costs, and attract clients. This research contributes to the topic of using deep learning technologies in supply chain management, promoting innovation, and changes that may affect the industry drastically. 2024 IEEE.
Singh J.; Shelke N.A.; Upreti K.; Saiyad F.B.J.; Divakaran P.; Madhukar K.S.
2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024
Singh J., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Shelke N.A., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Upreti K., Christ (Deemed to be University), Department of Computer Science, Ghaziabad, India; Saiyad F.B.J., Bath spa university Rak, Department of Commerce, Ras Al-Khaimah, United Arab Emirates; Divakaran P., Himalayan University, Department of management, Itanagar, India; Madhukar K.S., KIT's Institute of Management, Education and Research, Kolhapur, India
An Optimal Load Balancing Framework for Fog-Assisted Smart Grid Applications
Cloud computing; Fog computing; Load balancing; Optimization; Performance evaluation; Smart grid
The growth of the Internet of Things (IoT) causes a significant amount of data to come in from physical devices and sensors, which adds to the latency and processing delays in smart grid applications. The pay-per-model method of transmitting gathered data that cloud computing offers improves scalability and functionality for end devices, which increases smart grid efficiency. Milliseconds matter in the crucial realms of load balancing, resource usage, and distribution systems, where any latency or jitter is unacceptable. By strategically positioning processing, networking, storage, and communication capabilities at the network edge, fog computing, an outgrowth of cloud technology, successfully addresses current issues in service groups. This paper introduces a unique hybrid framework on a highly virtualized platform and proposes three potential load balancing algorithms: throttled, Round Robin, and a novel Equilibrium Optimizer with Simulated Annealing (EO-SA). The article provides a comprehensive investigation on several load balancing techniques for obtaining optimized services in a smart grid environment thereby focusing on better utilization of network resources and reduction of costs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Sharma V.; Tripathy S.S.; Bebortta S.; Iwendi C.; Osamor J.
Lecture Notes in Networks and Systems, Vol-1043 LNNS, pp. 589-601.
Sharma V., Computer Science Department, Christ University, Delhi NCR Campus, India; Tripathy S.S., School of Computer Engineering, KIIT Deemed to Be University, Bhubaneswar, 751024, India; Bebortta S., Department of Computer Science, Ravenshaw University, Cuttack, 753003, India; Iwendi C., School of Creative Technologies, University of Bolton, Bolton, United Kingdom; Osamor J., University of Westminster, London, United Kingdom
Strategic Power Factor Management for Elevated Lift and Hoist Performance
Capacitance Bank; Energy Consumption; Induction Motor; Power Factor Correction; Reactive Power Compensation
The paper outlines the design and simulation of active power factor correction for a 100 hp induction motor using MATLAB/Simulink. In this system, the induction motor functions as the primary load, operating with a low power factor. Different load scenarios are simulated to examine the motor's performance. The current drawn from the supply is verified under varying conditions, both with and without the implementation of a variable capacitance bank. The power system network comprises apparatus such as Induction Motors, Power Transformers, and Induction Furnaces, contributing to a low power factor. The resultant low power factor leads to elevated energy consumption. To mitigate this, power factor correction is imperative. Utilizing a variable capacitance proves instrumental in enhancing the power factor. The capacitor compensates for a portion of the reactive power, consequently reducing the total reactive power drawn from the source. This reduction in reactive power contributes to an overall decrease in power consumption. The research focus is on the effective correction of the power factor for a 100 hp induction motor through comprehensive design and simulation using MATLAB/Simulink, providing valuable insights into the impact of variable capacitance on current draw under diverse load conditions. 2024 IEEE.
Parimalasundar E.; Jayakumar S.; Ravikumar R.; Kannan V.K.; Sindhuja R.; Suresh K.
10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024, pp. 270-274.
Parimalasundar E., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Jayakumar S., Sri Sairam College of Engineering, Electronics and Communication Engineering, Bengaluru, India; Ravikumar R., Karpagam Institute of Technology, Electrical and Electronics Engineering, Coimbatore, India; Kannan V.K., Knowledge Institute of Technology, Electrical and Electronics Engineering, Salem, India; Sindhuja R., Srm Institute of Science and Technology, Electrical and Electronics Engineering, Chennai, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bengaluru, India
Residual-Based Statistical Process Control Charts in the Presence of Multicollinearity: an EWMA Framework with (RK) Estimator
(rk) class estimator; EWMA; Multicollinearity; Process control; Regression control chart; Residual control chart
Reliability monitoring of financial health requires strong control mechanisms, and the residual chart is an invaluable instrument to perform it. One of the key problems statisticians face while modeling is the problem of multicollinearity which arises when there is a strong correlation between independent variables leading to imprecise coefficient estimates and poor outcomes. To solve this problem and to make sure that the control chart works even with correlated data, we integrated a Weighted Moving Average Exponential smoothing chart within the modeling technique. The theoretical approach assures long-term variability and consistency of the residual control chart. These control charts are used to understand the process and the performances in various sectors. The charts can be used as analytical instruments to help recognize patterns, variations, or anomalies in economic indicators specifically in budget deficit data and facilitate rapid identification of any changes or inconsistencies in the fiscal deficit by policymakers. Further advances in statistical process control are rendered feasible by this study, which deepens the understanding and awareness of the potential uses and implications of the Weighted Moving Average Exponential smoothing chart for fiscal deficit data in the Economic realm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-1085 LNNS, pp. 327-340.
Nancy M., Department of Statistics and Data Science, Christ University, Bangalore, India; Joshi H., Department of Statistics and Data Science, Christ University, Bangalore, India
Impact of Corporate Announcement of Green Innovation on Automaker's Market Value -An Event Study Methodology
Automobile Industry; Corporate announcement; Event Study Methodology; Green; Green Innovation; New Product Development; Organization strategy; Sustainability
The aim of this paper is to analyse the effects of the Green Innovation event and corporate announcements regarding green innovation on the stock price of the Automobile Industry and the performance of firms. The authors also aim to assess the impact of these events on business performance and identify the effective innovation strategy influenced by the type of corporate announcement. The study focuses on the corporate announcements made by the automobile industry and their impact on company performance, specifically in relation to the application of green innovation methods. Furthermore, there is no universally agreed upon standard for defining and categorising corporate announcements. The writers also exclude the impact of media and other events that occur during the event window when categorising these announcements. The findings of this study have important practical consequences. They suggest that the release of green innovations, which aim to protect the environment, can have an impact on an organization's stock market success. Specifically, the type of innovation and the trade segment in which the organisation operates can influence its stock market performance. Grenze Scientific Society, 2024.
Joshi M.C.; Chaurasia A.; Kasinathan S.; Garwal Y.S.; Chakravarthy S.; Mishra R.K.
15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024, Vol-2, pp. 2150-2158.
Joshi M.C., CDOE, Manipal University, Rajasthan, Jaipur, India; Chaurasia A., Department of Commerce, Finance and Accounting, CHRIST (Deemed to be University) Delhi- NCR., UP, Ghaziabad, India; Kasinathan S., CDOE, Manipal University, Rajasthan, Jaipur, India; Garwal Y.S., CDOE, Manipal University, Rajasthan, Jaipur, India; Chakravarthy S., CDOE, Manipal University, Rajasthan, Jaipur, India; Mishra R.K., Department of Commerce, Finance and Accounting, CHRIST (Deemed to be University) Delhi- NCR., UP, Ghaziabad, India
A Novel Technique for Magnetic Particle Separation Using Current-Carrying Slotted Plate
In this paper, a novel method for separating and trapping different magnetic particles is presented. Changes in the current-carrying structure yield disturbing the generated magnetic field. Here, slots were innovatively crafted on the current-carrying plate positioned beneath the microchannel, resulting in a non-uniform magnetic field distribution. This breakthrough enables the separation of different particle types using a constant and low electric current for the very first time, leading to a significant advancement in the field. More importantly, this proposed technique offers several advantages, including the generation of low levels of current and heat, ease of construction, and the ability to control the magnetic field produced by the electric current. In this study, the capability to effectively separate various particle types using a constant electric current was demonstrated with a remarkable separation efficiency of about 100%. By applying a 100[mA] electric current to the plate that carries electric current, the separation of two particle types M-450 and M-280 was achieved at a velocity of 2[?m/s]. 2024 IEEE.
Asgharivaskasi R.; Mashayekhi V.; Nameni A.; Azadi-Tinat N.; Kumar N.; Koohestani M.
IEEE Antennas and Propagation Society, AP-S International Symposium (Digest), pp. 2431-2432.
Asgharivaskasi R., Shahrood University of Technology, Department of Electrical Engineering, Shahrood, Iran; Mashayekhi V., Shahrood University of Technology, Department of Electrical Engineering, Shahrood, Iran; Nameni A., Shahrood University of Technology, Faculty of Mechanical Engineering, Shahrood, Iran; Azadi-Tinat N., Shahrood University of Technology, Department of Electrical Engineering, Shahrood, Iran; Kumar N., Christ University, Department of Electronics and Communication Engineering, Bengaluru, India; Koohestani M., ole Supieure d'ectronique de l'Ouest, Department of Electrical and Electronic Engineering, Angers, France
Enhancing Patient Well-Being in Healthcare Through the Integration of IoT and Neural Network
Healthcare; IoT systems; machine learning; patient monitoring; user-friendly interfaces
This study analyses the revolutionary integration of Internet of Things (IoT) structures in healthcare through a complete examination of outstanding case research. The first case study focuses on real-time patient fitness monitoring in a clinic setting. The suggested device utilizes an Internet of Things-ready device that has many sensors, including oxygen, pressure, and temperature sensors. The issues of forecasting patient health in advance are handled with the deployment of machine learning models, notably Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM). The second case study analyses IoT's effect on patient-precise medication identification and remote fitness monitoring, uncovering issues associated with accessibility, pricing, and human interfaces. Proposed alternatives, which incorporates greater education, increased accessibility, and user-pleasant interfaces with robust technical assistance, have been evaluated with 30 patients over a three-month duration. The results reveal a great growth in impacted person health, along with heightened attention of periodic health monitoring. The results highlight how IoT technologies may transform healthcare procedures by offering pro-active solutions for patients' well-being. This study offers insightful information that may be used to solve practical issues, promote patient-centered solutions, and broaden the scope of the healthcare period. A significant step towards a patient-centered and technologically advanced healthcare environment, the successful outcomes validate the capacity for sustained innovation, cooperation, and improvement in the integration of IoT systems for optimal patient care. 2024 IEEE.
Singh J.; Shelke N.A.; Upreti K.; Divakaran P.; Lingareddy N.; Deepika S.
Proceedings - 2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024, pp. 241-246.
https://doi.org/10.1109/INNOCOMP63224.2024.00047 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205541532&doi=10.1109%2fINNOCOMP63224.2024.00047&partnerID=40&md5=79f06348ccc77dcfcf2efb12099cc7c6
Singh J., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Shelke N.A., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Upreti K., CHRIST (Deemed to Be University), Department of Computer Science, Ghaziabad, India; Divakaran P., Himalayan University, Itanagar, India; Lingareddy N., Vignan Institute of Technology and Science, Department of CSE, Deshmukhi, India; Deepika S., Sreyas Institute of Engineering and Technology, Department of CSE, Hyderabad, India
Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
Decision Trees; Deep learning; K-Nearest Neighbour; Reinforcement learning; Semi-supervised learning; Supervised learning; Unsupervised learning
Wireless Sensor Networks (WSNs) are used in various healthcare and military surveillance applications. As more sensitive data is transmitted across the network, achieving security becomes critical. Ensuring security is also challenging because most sensors are deployed in remote areas, making them vulnerable to many security attacks. Sybil attacks are one of the most destructive attacks. Security against Sybil attackers can be attained by implementing effective detection techniques to distinguish attackers from genuine nodes. This paper reviews existing machine learning-based approaches for detecting Sybil attacks, and their performance is compared based on different parameters. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Anita E.A.M.; Jenefa J.; Vinodha D.; Lapina M.
Lecture Notes in Networks and Systems, Vol-863 LNNS, pp. 67-75.
Anita E.A.M., Department of Computer Science and Engineering, Christ University, Bangalore, India; Jenefa J., Department of Computer Science and Engineering, Christ University, Bangalore, India; Vinodha D., Department of Computer Science and Engineering, Christ University, Bangalore, India; Lapina M., Department of Information Security of Automated Systems, North-Caucasus Federal University, Stavropol, Russian Federation