A Narrative Synthesis on the Role of Affective Computing in Fostering Workplace Well-Being Using a Deep Learning Model
Affective computing; AI; Deep learning model; Employee emotions; Productivity; ResNet50; Workplace well-being
Emotional information is more valued in the modern workplaces with increased focus on the need for sensing, recognizing and responding to human emotions. Integrating human emotions as information for communication and decision-making is possible through the computer-based solution called as affective computing. Affective computing is a relatively less explored AI platform though the notion is more than two decades old. The cognitive algorithms employed in affective computing operates in three key areas, viz. context sensitivity, augmented reality, and proactiveness, with outcomes in the fields of emotion management, health, and productivity. Affective computing promises better management of organizational outcomes such as fostering workplace well-being, promoting happiness, productivity, engagement levels, and communication. Further, affective computing can play vital roles in an employees life cycle with applications in functional areas of HRM like employee selection, training and development, and performance management. Even as workplaces are increasingly adopting affective computing, an analysis of its positive effects can help practitioners take informed decisions about its implementation. This paper outlines the theoretical underpinnings of affective computing, discusses the relevance of ResNet50 in image analysis, and proposes a step-by-step methodology for implementing affective computing techniques in the workplace. The potential benefits and challenges of adopting affective computing in fostering workplace well-being are also discussed. Thus, this chapter investigates the role of affective computing in fostering well-being in the workplace usinga deep learning model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Delina G.; Badada B.; Krishnaraj R.; Thiruthuvanathan M.M.; Kumar R.M.
Lecture Notes in Networks and Systems, Vol-1117 LNNS, pp. 431-445.
Springer Science and Business Media Deutschland GmbH
Delina G., Kalasalingam Academy of Research and Education (Deemed to Be University), Tamil Nadu, Krishnankoil, Srivilliputhur, 626126, India; Badada B., Kalasalingam Academy of Research and Education (Deemed to Be University), Tamil Nadu, Krishnankoil, Srivilliputhur, 626126, India; Krishnaraj R., Dambi Dolo University, Dembi Dollo, 000260, Ethiopia; Thiruthuvanathan M.M., Christ (Deemed to Be University), Karnataka, Bengaluru, 560029, India; Kumar R.M., Christ (Deemed to Be University), Karnataka, Bengaluru, 560029, India
LULC Analysis of Green Cover Loss in Bangalore
Bangalore; Convolutional Neural Networks (CNN); Deep Learning; Green Cover Loss; Land Use Land Cover (LULC)
Urbanization of the cities especially the Indian City of Bangalore has led to the creation of an important discourse concerning development and conservation. The study carries out a detailed LULC study with special reference to Green Cover Loss in city of Bangalore. Using satellite images from 2014 to 2023 period and machine learning tools, the study establishes declines in green spaces with economic, environmental and health consequences of the city's uncontrolled expansion. The innovations afforded to the study regard methodologically on the use of ResNet50 for accurate LULC classification with an accuracy of 92% Hence the study reveals the interaction between urbanization and conservation, the efficiency of which requires policy adjustments that depend on existing knowledge. The study not only accustomizes the progression in the geography of Bangalore but it also shapes the technology and methodology for the further geospatial research in the areas under rapidly urbanizing in the future. 2024 IEEE.
2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
Institute of Electrical and Electronics Engineers Inc.
Mathew J.A., Christ University, Department of Data Science, Karnataka, Bangalore, 560029, India; Eapen N.G., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; George J.P., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India
Optimization Ensemble Learning Techniques for Reliable Crop Yield Prediction using ML
Decision Tree Regressor; Ensemble Learning; Gradient Boosting Regressor; Hyperparameter tuning; Outlier Analysis; R-squared Score; Random Forest Regressor
The agricultural sector's increasing reliance on technology has paved the way for advanced data-driven methodologies, with crop yield prediction emerging as a critical focus. This study dives into the complex landscape of crop yield prediction, employing a comprehensive approach that involves data preprocessing, model development, and performance evaluation. This research goes into enhancing crop yield prediction through a thorough data-driven approach. Beginning with comprehensive data preprocessing, including outlier analysis and feature scaling, the study ensures dataset integrity. Ensemble learning, employing Gradient Boosting Regressor, Random Forest Regressor and Decision Tree Regressor, captures intricate relationships within the dataset. Model performance, assessed through R-squared scores, demonstrates promising predictive capabilities. Subsequent outlier analysis and hyperparameter tuning yield substantial improvements, contributing valuable insights for agricultural decision-making. The research not only advances crop yield prediction but also offers practical guidance for integrating machine learning into agriculture, promising transformative outcomes for sustainable practices. The research also highlights how significant interpretability is to machine learning models so that stakeholders can comprehend and embrace them. This allows for a smooth integration of the models into current agricultural practices and encourages openness and reliability in decision-making. 2024 IEEE.
Upreti K.; Lingareddy N.; Deepika S.; Kumar N.; Parashar J.; Divakaran P.
Proceedings - 2024 1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024, pp. 431-436.
https://doi.org/10.1109/TIACOMP64125.2024.00078 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212043398&doi=10.1109%2fTIACOMP64125.2024.00078&partnerID=40&md5=80771159abaa3f5e48b09bb5179a808d
Upreti K., Christ (Deemed to Be University), Dept. of Computer Science, Delhi NCR, India; Lingareddy N., Vignan Institute of Technology and Science, Department of Cse, Telangana, India; Deepika S., Sreyas Institute of Engineering and Technology, Department of Cse, Telangana, Hyderabad, India; Kumar N., Christ (Deemed to Be University), School of Business and Management, Bangalore, India; Parashar J., Adgitm, Dept. of Computer Science & Engg, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India
Cloud-Based Diabetic Retinopathy Severity Recognition System Using Ensemble Deep Convolutional Neural Network Classifier Model
Deep convolutional neural network; Diabetic retinopathy disease; Ensemble classifier model; Fundus images
One of the key reasons for visual impairments is due to the ignorance of diabetic retinopathy disease. This research study focuses on the early recognition of diabetic retinopathy disease from the fundus images and identifies its severity stages to make successful treatments against blindness risk. Some traditional approaches explored the decision tree, kernel-based support vector machine, and Nae Bayes classifier models to extract the features from fundus images. Most of the researchers applied the modern approach of convolutional neural network model through transfer learning mechanism to extract relevant features from the fundus images. It helps in the diagnosis of diabetic retinopathy that may delay the prediction process and create inconsistency among the doctors. So, a deep learning-based approach is proposed in this research study to provide stage-wise prediction of diabetic retinopathy disease with a multi-task learning mechanism. As a result, the proposed deep convolutional neural network classifier with an ensemble model outperforms the existing classifier with EfficientNet-B4, EfficientNet-B5, SE-ResNeXt50 (380?380), and SE-ResNeXt50 (512?512) networking methods in the context of prediction correctness, sensitivity, specificity, macro F1, and quadratic weighted kappa (QWK) score metrics. Exploiting hyperparameter optimizations on the deep learning classifier model and multi-task regression learning approaches make significant improvements over the performance evaluation metrics. Finally, the proposed approaches make the effective recognition of diabetic retinopathy disease stages based on the human fundus image. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-856 LNNS, pp. 323-330.
Rajavel R., Christ University, KA, Bangalore, 560074, India; Nagappan P., Galgotias University, UP, Greater Noida, 203201, India
Applying Artificial Bee Colony Algorithm to Improve UWSNs Communication
Artificial Bee Colony algorithm; bio-inspired optimization; communication reliability; data transmission; energy efficiency; network scalability; Underwater Wireless Sensor Networks
The research in this study aims at implementing the ABC algorithm to enhance the communication within UWSNs. The ABC algorithm, motivated by the CPG approach being analogous to that of honey bees searching for food, specifies optimal values for critical parameters of the network such as energy consumption, reliability in data transfer, and scalability. From the analyses conducted in this exposition, it is apparent that the envisaged methodology outperforms other conventional routing parlances in the following ways: minimal energy usage, high data delivery ratios, low packet drops, and longest network lifetime. Therefore, from the above results it can be concluded that, the said ABC algorithm is helping in achieving a better result in terms of improved underwater communication as well as in mitigating with the difficulties of UWSNs. 2024 IEEE.
Habelalmateen M.I.; Kaur A.; Tiwari A.; Shalini M.; Victor M.; Kumar N.S.
2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024
Habelalmateen M.I., The Islamic University, College of Technical Engineering, Department of Computers Techniques Engineering, Najaf, Iraq, The Islamic University of Al Diwaniyah, College of Technical Engineering, Department of Computers Techniques Engineering, Al Diwaniyah, Iraq; Kaur A., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Science Engineering, Punjab, Mohali, India; Tiwari A., Ies College of Technology, Department of Electronics & Communication Engineering, M.P., Bhopal, India; Shalini M., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Victor M., Christ (Deemed to be University), School of Business and Management, India; Kumar N.S., (E.Tech.) Srm Institute of Science & Technology, Department of Computer Science & Engineering, Chennai, 26, India
Label-Based Feature Classification Model for Extracting Information with Dynamic Load Balancing
Big data information extraction; Classification and dimensionality of data; LFCM model
Efficient extraction of information from various sources is very tedious. Achieving this requires very sophisticated feature classification model and ability of the system to adapt to changing environments of data and its random distributions with an efficient use of computational resources. Label-based feature classification model (LFCM) with dynamic load balancing is proposed to address an efficient model to extract information in data set. This technique is effective in data analysis to discover the new feature set. Label approach incorporates unique label concept and it avoids any data duplication using labels. Each data sample is assigned to only one label to improve the accuracy and effectiveness of the retrieval process. Based on the data relevancy and specific features that can be extracted using proposed algorithm, classification model and semantic representation of data in vector form minimizes the data loss, and dimensionality reduction plays a vital role in building an efficient model. Various graphs and results obtained from the experiments show an improvement of information extraction using this proposed labeled LFCM approach. This approach brings lots of real time challenges that are handled to bring accuracy factor as the main focus in this proposed system. Both classification and extraction uses different model to obtain the intended results. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Swamy C.M.; Babu T.M.R.; Kumar M.S.V.; Vinodha D.; Kiran B.
Lecture Notes in Electrical Engineering, Vol-1259 LNEE, pp. 21-33.
Swamy C.M., School of Engineering and Technology, Christ University, Bengaluru, India; Babu T.M.R., PES College of Engineering, Mandya, India; Kumar M.S.V., Maharaha Institute of Technology, Mysore, India; Vinodha D., School of Engineering and Technology, Christ University, Bengaluru, India; Kiran B., ATME College of Engineering, Mysore, India
An improved AI-driven Data Analytics model for Modern Healthcare Environment
Approach; Improve; Powerful; Progressed; Statistics
AI-driven statistics analytics is a swiftly advancing and impactful era that is transforming the face of healthcare. By leveraging the energy of AI computing and gadget studying, healthcare organizations can speedy gain insights from their huge datasets, offering a greater comprehensive and personalized approach to hospital therapy and populace health management. This paper explores the advantages of AI-driven statistics analytics in healthcare settings, masking key benefits along with progressed analysis and treatment, better-affected person effects, and financial savings. Moreover, this paper addresses the main challenges associated with AI-pushed analytics and offers potential solutions to enhance accuracy and relevance. In the long run, statistics analytics powered by way of AI gives powerful opportunities to improve healthcare outcomes, and its use is expected to expand within the coming years. 2024 IEEE.
Patil S.C.; Takkalapally P.; Kasula B.Y.; Logeshwaran J.
Patil S.C., United States; Takkalapally P., Lewis University, Department of Engineering, Computing and Mathematical Sciences, United States; Kasula B.Y., University Of The Cumberlands, Dept of Information Technology, Williamsburg, KY, United States; Logeshwaran J., CHRIST (Deemed to be University), Department of Computer Science, Karnataka, Bengaluru, 560029, India
Pothole Detection and Powertrain Control for Vehicular Safety
CNN; ECU; Electric Vehicles; Jetson Nano; Pothole
A new era of automotive technology has begun with the rapid advancement of electric vehicles (EVs), which promise efficiency and sustainability. With electric vehicles (EVs) becoming an integrated part of the traction systems, there is a growing need for novel safety and performance-enhancing features. The development of an Adaptive Cruise Control (ACC) system for autonomous powertrain control and pothole detection in electric vehicles is examined in this paper. The paper focuses on integrating an intelligent system that can detect potholes and autonomously regulate the powertrain to improve both the driving experience and safety of electric vehicles. The system makes use of Jetson Nano as the processing unit for regulation of the EV powertrain. This board enables quick and accurate reactions to changing road conditions by facilitating real-time data analysis and decision-making. The powertrain regulation will be performed by controlling the acceleration and braking signal provided to the powertrain. 2024 IEEE.
D'Couto A.L.; Juliana David M.; Parag Jose C.; Praghash K.; Wang Y.
Proceedings of IEEE International Conference on Vehicular Technology and Transportation Systems, ICVTTS 2024
D'Couto A.L., CHRIST University, Karnataka, Bangalore, India; Juliana David M., CHRIST University, Karnataka, Bangalore, India; Parag Jose C., CHRIST University, Karnataka, Bangalore, India; Praghash K., CHRIST University, Karnataka, Bangalore, India; Wang Y., Binghamton University, New York, United States
Intelligent Time Management Recommendations Using Bayesian Optimization
Bayesian optimization; Gaussian Processes; intelligent time management; personalized scheduling; productivity enhancement
This paper focuses on the improvement of the intelligent time management system which employ Bayesian optimization for suggesting time management plans for each particular person. In this sense, through historical data of input-output patterns and users' preferences, the system aims at increasing productivity and user satisfaction. In the study, Gaussian Processes are used as the surrogate model in the Bayesian optimization so that the required evaluations by the algorithm to realize optimal scheduling methodologies are kept to a minimum. Implementation is done as a web application where users submit their tasks and get the recommended schedule instantly. Indicators like, the degree of task accomplishment, time, and scheduling compliance, and probably the users' satisfaction suggest that system helped enhance time management results. Lack of feedback from the users is removed through questionnaire that reveals the simplicity of the system and the quality of its recommended times, thereby supporting the idea of Bayesian optimization as a game changer in the management of time. This research significance points to the need for maintaining efficient and individualized approaches to time management strategies and agrees with others' findings, which suggest that this is an area ample fiction research needs to acknowledge and pursue. 2024 IEEE.
Chandramowleeswaran G.; Sindhu M.; Alabdeli H.; Mishra A.; Vinod P.; Kaur H.
Chandramowleeswaran G., Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology, Department of Commerce and Business Administration, Chennai, 600062, India; Sindhu M., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Alabdeli H., The Islamic University, College Of Technical Engineering, Department Of Computers Techniques Engineering, Najaf, Iraq, The Islamic University Of Al Diwaniyah, College Of Technical Engineering, Department Of Computers Techniques Engineering, Al Diwaniyah, Iraq; Mishra A., Ies University, Ies Institute of Technology and Management, Department of Computer Science & Engineering, M.P., Bhopal, India; Vinod P., Christ University, School Of Business And Management, India; Kaur H., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Application, Punjab, Jhanjeri, Mohali, 140307, India
Should Crypto Integrate Micro-Finance option?
awareness; cryptocurrency; economically challenged; investment behaviour; Micro finance; perception; policy makers; readiness; trustworthiness; younger population
Purpose - The purpose of the study is to identify the readiness or acceptance by the younger population specifically, the school and university students towards the investment in cryptocurrency if the micro-finance option is included in such new asset investments. Further to this the research also focusses on the mediating factor as trustworthiness to identify the impact or influence of the variable towards the acceptance of the new asset investment.Design/methodology/approach - The research conducted through relevant literature with sufficient variables measuring with five point Likert's scale. The research also tested with hypothesis on the relationship with variables. A total of 293 valid respondents data were collected and analysed through Structural Equation model.Findings - The analysis and results suggested that the perception, awareness and trustworthiness has positive impact towards the readiness towards crypto investments. Whereas, the investment behaviour has complex acceptability towards the readiness as it failed to accept the hypothesis.Research limitations/implications - the research is limited with the younger population however the research did not focusses on the economically challenged population as they may not be afford to invest in such platforms. The future studies can also be focussed on the same area with more towards the other factors that influence the economically challenged population and identify solution their economic growth. Furthermore, the study may be game changer for the policy makers in legalising the crypto investments in the country.Originality/value - According the wider background study and with substantial literature the research is of first in its kind as per the author's knowledge to integrate the micro finance concept in crypto investments to promote the investment habit among the younger population. 2024 IEEE.
Subramanian P.; Williams M.J.; Cheriyan A.; Monica L.; Ganesh L.; Rao P.N.; Adhikary D.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
https://doi.org/10.1109/TQCEBT59414.2024.10545169 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212801914&doi=10.1109%2fTQCEBT59414.2024.10545169&partnerID=40&md5=312af8c4c997f60cc964c6c3c6bfe396
Subramanian P., Christ University, India; Williams M.J., Sri Krishna Arts & Science College, India; Cheriyan A., Christ University, India; Monica L., Christ University, India; Ganesh L., Christ University, India; Rao P.N., Christ University, India; Adhikary D., Christ University, India
Integrating AI and Cybersecurity: Advancing Autonomous Vehicle Security and Response Mechanisms
AI integration; Dynamic response mechanisms; Risk mitigation; Vehicle security operation centers
The rapid evolution of autonomous and connected vehicles has led to their integration with numerous technologies and software, rendering them vulnerable targets for cybersecurity attacks. While efforts have traditionally focused on preventing these attacks, the escalating risk underscores the importance of also vindicating their wallop. Nevertheless, this procedure is often onerous & facade scalability confronted, particularly due to connectivity issues in automobiles. This research advises a vehicle-based vibrant imposition response scheme, enabling swift responses to a variety of incidents and reducing reliance on external security centers. The classification encompasses an inclusive range of probable retorts, a procedure for evaluating retorts, & innumerable assortment approaches. Implemented on an embedded platform, the solution was evaluated using two distinct cyberattack use cases, highlighting its adaptability, responsiveness, volume for dynamic arrangement constraint alterations & nominal memory trail. Concurrently, this paper presents an innovative (AVSF) that synergistically integrates (AI) and cybersecurity techniques to fortify AV resilience against evolving threats. Additionally, the framework incorporates advanced cybersecurity measures such as encryption, authentication, and intrusion detection to mitigate vulnerabilities and safeguard critical AV systems. The fusion of AI and cybersecurity not only enhances AV security posture but also enables intelligent cyber threat monitoring and response capabilities. Extensive simulations and experimental evaluations demonstrate the efficacy of the AVSF in real-time scenarios, contributing to the development of robust security solutions for autonomous vehicle deployment and advancing safer transportation systems in the era of AI-driven mobility. 2024 IEEE.
Namburi V.L.; Adapa S.R.; Chamala S.S.K.; Yerram M.; Gupta P.; Upreti K.
2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 253-258.
Namburi V.L., Software Systems Engineer Danlaw Inc, MI, United States; Adapa S.R., Software Engineer OpenLogix LLC, MI, United States; Chamala S.S.K., Software Developer Software Bots Inc, MI, United States; Yerram M., Computer Science with IOT VNRVJIET, Hyderabad, India; Gupta P., Computer Science Engineering ADGIPS, Delhi, India; Upreti K., Dept. of Computer Science, Christ University, Delhi NCR Campus, India
An Integrated Scalable Healthcare Management System Using IOT
And Sensors; Android; ECC; Mlab; Raspberry Pi; SHMS
Healthcare management is the challenging task of maintaining the patients medical-related data and images. Pervasive computing, which consists of a wireless network, is an innovative medium for medical data transmission. Here, we propose SHMS (Scalable Healthcare Management System) and interoperability, an available and user-friendly platform. It utilizes a huge amount of data and medical images that must be managed and stored for processing and further investigation. In our work, data like heartbeat, temperature, blood pressure, and ECG readings are collected using different sensors and in one gateway protocol. This design is used for transferring, managing, and accessing documents containing health-related information, which is scattered across different system and organization domains. It is scalable because cloud platforms provide communication APIs, the web service interfaces ensure interoperability, the availability makes patients, doctors, or administrators able to access medical-related data anywhere, and Android OS makes it user-friendly. The security of the data collected can be achieved by authenticating storage using a cryptographic ECC algorithm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Jalapur S.; Yogish D.; Mithun B.N.; Anoop G.L.
Lecture Notes in Networks and Systems, Vol-856 LNNS, pp. 165-178.
Jalapur S., CSE Department, Christ University, Bangalore, India; Yogish D., CSE Department, Christ University, Bangalore, India; Mithun B.N., CSE (Cyber Security) Department, A P S College of Engineering, Bangalore, India; Anoop G.L., CSE Department, Christ University, Bangalore, India
Investigating the Use of Natural Language Processing in Electronic Medical Record
Efficiency; Language; Natural; Robotical; Speciality
Natural language processing (NLP) implemented in digital scientific records (EMRs) can substantially enhance the nice and efficiency of affected person care. The purpose of NLP implemented in EMRs is to extract applicable facts from affected persons' notes written in a human language together with English. This information can then be stored in a suitable structured form for further evaluation and records mining. NLP has been carried out in the clinical field for the reason that Fifties as a green approach for retrieving textual content-based data and reading interactions among affected persons and healthcare professionals. With the arrival of electronic facts, NLP has come to be extra extensively applied for the diffusion of purposes, inclusive of automatic coding, scientific choice aid, and medical doctor order access. This summary makes a of exploring the usage of NLP in EMRs. The scope of this research consists of an evaluate of present NLP technologies and their software in EMRs. It additionally outlines a number of the present-day demanding situations inside the use of NLP for clinical information and shows capability answers. Finally, the potential applications of NLP-driven EMRs are discussed, inclusive of making use of in-health practitioner order entry, scientific choice assistance, and population health control. 2024 IEEE.
Kasula B.Y.; Patil S.C.; Takkalapally P.; Gupta K.; Logeshwaran J.
Kasula B.Y., University Of The Cumberlands, Dept of Information Technology, Williamsburg, KY, United States; Patil S.C., United States; Takkalapally P., Lewis University, Department of Engineering, Computing and Mathematical Sciences, United States; Gupta K., University Of The Cumberlands, Dept of Information Technology, Williamsburg, KY, United States; Logeshwaran J., CHRIST (Deemed to be University), Department of Computer Science, Karnataka, Bengaluru, 560029, India
Multi-Agent Deep Reinforcement Learning for Hybrid Motion Planning in Dynamic Environments
Autonomous agents; Distributed algorithms; Dynamic obstacles; Multi-agent systems; Trajectory optimization
This research presents a novel approach to addressing the challenges of gesture forecasting in impenetrable and dynamic atmospheres by integrating a hybrid algorithm within a multi-agent system framework. Traditional methods such as Force-based motion planning (FMP) & deep reinforcement learning (RL) often struggle to handle complex scenarios involving multiple autonomous agents due to their inherent limitations. To overcome these challenges, we propose a hybrid algorithm that seamlessly combines the strengths of RL and FMP while leveraging the coordination capabilities of a multi-agent system. By integrating this hybrid algorithm into a multi-agent framework, we demonstrate its effectiveness in enabling multiple agents to navigate densely populated environments with dynamic obstacles. Through extensive simulation studies, we illustrate the superior performance of our approach compared to traditional methods, achieving higher success rates and improved efficiency in scenarios involving simultaneous motion planning for multiple agents. A hybrid motion planning algorithm is also introduced in this very research. Performance Comparison of Hybrid Algorithm, Deep RL, and FMP are also discussed in the result section. This research paves the way for the development of robust and scalable solutions for motion planning in real-world applications such as collaborative robotics, autonomous vehicle fleets, and intelligent transportation systems. 2024 IEEE.
Chopra J.; Avhad P.; Ekatpure S.R.; Rallapalli M.; Kumari S.; Upreti K.
2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 235-241.
Chopra J., SEAS, GWU, Washington, DC, United States; Avhad P., Computer Engineering, Veermata Jijabai Technological Inst., Mumbai, India; Ekatpure S.R., Industrial Engineering, Kulicke and Soffa, PA, United States; Rallapalli M., CSE - AI, JAIN University, Bengaluru, India; Kumari S., HSBC, Maharashtra, India; Upreti K., Computer Science Engineering, CHRIST University, Delhi Ncr, India
Impact of Artificial Intelligence on Business Strategy and Decision-Making
Artificial intelligence; Business strategy; Decision tree; KNN
Market analysis as knowledge-enhancement function, its use in internal politics, its abuse, and its ability to generate market understanding were recognised as the four key performance variables in market analysis. Profitability and bottom line may be increased, inefficiencies in corporate processes can be reduced, and other hidden insights can be uncovered by analyzing financial accounting transactions. The research focuses on the business strategy in decision-making using artificial intelligence. Reviewing existing research and providing recommendations. In this study, firstly collect the dataset finance data from Kaggle for the better-trained model. After that perform the pre-processing data for outlier removal. The implementation work is complete on the Python programming language. The results showed that the proposed KNN, the Decision tree model, achieved high accuracy. Businesses and organisations working in the field of artificial intelligence (AI) might greatly benefit from this research in terms of narrowing down the profiles that are certain to avoid in the future. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Hada B.S.; Bharti H.R.; Rastogi S.; Saxena N.K.; Maheshwari A.
Lecture Notes in Electrical Engineering, Vol-1231 LNEE, pp. 307-326.
Hada B.S., School of Commerce, Finance and Accountancy CHRIST (Deemed to Be University), Delhi, India; Bharti H.R., Institute of Commerce, Nirma University, Ahmedabad, India; Rastogi S., School of Commerce, Finance and Accountancy CHRIST (Deemed to Be University), Delhi, India, Institute of Commerce, Nirma University, Ahmedabad, India; Saxena N.K., School of Commerce, Finance and Accountancy CHRIST (Deemed to Be University), Delhi, India, Institute of Commerce, Nirma University, Ahmedabad, India; Maheshwari A., School of Commerce, Finance and Accountancy CHRIST (Deemed to Be University), Delhi, India, Institute of Commerce, Nirma University, Ahmedabad, India
High-Speed Parity Number Detection Algorithm inRNS Based onAkushsky Core Function
Akushsky core function; High performance computing; Non-modular operations; Parity detection; Residue number system
The Residue Number System is widely used in cryptography, digital signal processing, image processing systems and other areas where high-performance computation is required. One of the computationally expensive operations in the Residue Number System is the parity detection of a number. This paper presents a high-speed algorithm for parity detection of numbers in Residue Number System based on Akushsky core function. The proposed approach for parity detection reduces the average time by 20.39% compared to the algorithm based on the Chinese Remainder Theorem. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lutsenko V.; Geryugova A.; Babenko M.; Lapina M.; Mary Anita E.A.
Lecture Notes in Networks and Systems, Vol-1121 LNNS, pp. 491-504.
Lutsenko V., North-Caucasus Federal University, Stavropol, Russian Federation; Geryugova A., North-Caucasus Federal University, Stavropol, Russian Federation; Babenko M., North-Caucasus Federal University, Stavropol, Russian Federation; Lapina M., North-Caucasus Federal University, Stavropol, Russian Federation; Mary Anita E.A., Christ University, Bangalore, India
Data Mining Techniques to Enhance Customer Segmentation and Targeted Marketing Strategies
Association Rule mining; Customer Behavior Analysis; Customer Segmentation; Market basket analysis; RFM Analysis
The retail industry is facing an ever-increasing challenge of effectively identifying and targeting its customers. Using traditional segmentation techniques to fully capture the intricate and ever-changing character of customer behavior is difficult. This project will examine sales data from a general shop using an assortment of data mining technologies in order give insights into customer habits and purchasing trends. Retail sales records builds the dataset. K-means clustering, association rule mining, and regency, the frequency, and monetary (RFM) analysis will all be employed to look into the data. This study contributes to create something of focused marketing strategies and consumer segmentation by identifying high-value and atrisk clients. Association rule mining illuminates consumer taste and actions by identifying hidden patterns and correlations in large datasets. These discoveries extend the scope of our comprehension of consumer purchasing habits and offer data for more targeted advertising initiatives. Additionally, the K-means clustering algorithm divides customers according to their purchasing habits and behavior, allowing profound knowledge to enhance marketing and sales strategies. Findings from the research will give an extensive awareness of customer behavior and purchasing dynamics, which will improve the efficacy of the general store's marketing and sales campaigns. The most effective technique for exploiting insights from sales data will be discovered by contrasting the outcomes of RFM analysis, K-means clustering, and association rule mining. This work promises to make substantial improvements to data mining and buyer behavior research algorithms, and it has the capacity to be implemented across an extensive selection of corporate restrictions intended to improve their sales strategies. 2024 IEEE.
Rajan D., CMR Institute of Technology, Department of Mca, Bangalore, India; Helen Josephine V.L., Christ University, School of Business and Management, Bangalore, India
Exploring Shopping Opportunities and Elevating Customer Experiences Through AI-Powered E-Commerce Strategies
Clustering algorithms; Customer experiences; E-commerce; Personalized recommendations; Segmentation
This research explores the efficacy of clustering algorithms in enhancing customer experiences within the e-commerce landscape. Through experiment trials utilizing K-means and DBSCAN clustering techniques, valuable insights have been gleaned. The trials yielded silhouette scores ranging from 0.55 to 0.72, indicating moderate to good clustering quality across different experiments. In K-means clustering, the number of clusters varied from 3 to 6, with inertia values spanning approximately 722.41456.8. Conversely, DBSCAN clustering resulted in varying cluster numbers, ranging from 2 to 4, contingent on the combinations of epsilon and min_samples values explored. These findings underscore the significance of judiciously selecting clustering algorithms and parameter settings to achieve meaningful segmentation of e-commerce data. Effective utilization of clustering algorithms empowers businesses to discern valuable insights into customer behavior, preferences, and patterns. Consequently, businesses can tailor their strategies to deliver personalized experiences, targeted marketing campaigns, and optimized product recommendations. This research propels the exploration of additional clustering techniques and parameter refinements for enhanced clustering performance in e-commerce applications. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Maurya N.; Singh J.; Singh N.P.; Chaudhary A.; Patil S.; Tiwari M.
Lecture Notes in Networks and Systems, Vol-1117 LNNS, pp. 149-159.
Maurya N., Department of Management, KCC Institute of Legal and Higher Education, Greater Noida, India; Singh J., Alliance School of Advanced Computing, Alliance University, Bengaluru, India; Singh N.P., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Chaudhary A., School of Law, CHRIST (Deemed to be University) Delhi-NCR, Ghaziabad, India; Patil S., Dr. D. Y. Patil Vidyapeeth, Centre for Online Learning, Pimpri, Pune, India; Tiwari M., Department of Computer Science and Engineering, Bharati Vidyapeeths College of Engineering, Delhi, India
Utilizing Deep Learning Techniques for Lung Cancer Detection
Architectures; Augmentation; Convolutional; Radiographs; Segmentation
Deep learning can extract meaningful insights from complex biomedical statistics, which includes Radiographs and virtual tomosynthesis. Traits in contemporary deep studying architectures have enabled faster and more correct mastering of the functions gifted in clinical imagery, main to better accuracy and precision in medical analysis and imaging. Deep studying strategies may be used to pick out patterns within the pics which may be indicative of illnesses like lung cancer. Those ailment patterns, which include small lung nodules, can be used for early detection and prognosis of the sickness. Recent studies have employed deep learning strategies consisting of Convolutional Neural Networks (CNNs) and switch learning to come across most lung cancers in CT pictures. The first step in this manner is to generate datasets of pictures of the lungs, each from wholesome people and those with most lung cancers. Those datasets can then be used to teach a deep knowledge of a set of rules that may be optimized to it should locate those styles. Once educated, the version can be used to come across styles indicative of lung most cancers from new take a look at images with high accuracy. For further accuracy and reliability, extra up-processing techniques, along with segmentation and records augmentation, may be used. Segmentation can be used to detect a couple of lung nodules in a photo, and records augmentation can be used to lessen fake high quality outcomes. 2024 IEEE.
Madasu S.; Rolla K.J.; Patil S.C.; Gupta R.; Logeshwaran J.
https://doi.org/10.1109/ICCCNT61001.2024.10724114 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213323501&doi=10.1109%2fICCCNT61001.2024.10724114&partnerID=40&md5=0eb609ff194a8a06f3bb18910cfa7bcb
Madasu S., Microsoft, Charlotte, NC, United States; Rolla K.J., Independent Researcher, Fort Mill, SC, United States; Patil S.C., United States; Gupta R., Tejankar Hospital, MP, Ujjain, India; Logeshwaran J., CHRIST (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, 560029, India
AdvanDNN: Deep Neural Network Analysis of Neuroimaging for Identifying Vulnerable Brain Regions in Autism Spectrum Disorder
AAL116 atlas; AdvanDNN; Autism spectrum disorder; Brain connectivity; Deep learning; Neuroimaging analysis
Exploring the neurological framework of autism spectrum disorder (ASD) presents a significant challenge due to its diverse manifestations and cognitive impacts. This study introduces an innovative deep learning approach, employing an advanced deep neural network (AdvanDNN) model to identify and analyze brain regions vulnerable to ASD. Utilizing the AAL116 brain atlas for anatomical standardization, our model processes a comprehensive set of neuroimaging data, including structural and functional MRI scans, to discern distinct neural patterns associated with ASD. The AdvanDNN model, with its robust deep learning architecture, was meticulously trained and validated, demonstrating a notable accuracy of 91.17% in distinguishing between ASD-affected individuals and controls. This marks an improvement over the state of the art, contributing a significant advance to the diagnostic processes. Notably, the model identified a pronounced anticorrelation in brain function between anterior and posterior regions, corroborating existing empirical evidence of disrupted connectivity within ASD neurology. The analysis further pinpointed critical regions, such as the prefrontal cortex, amygdala, and temporal lobes, that exhibit significant deviations from typical developmental patterns. These findings illustrate the potential of deep learning in enhancing early detection and providing pathways for intervention. The application of the AdvanDNN model offers a promising direction for personalized treatment strategies and underscores the value of precision medicine in addressing neurodevelopmental disorders. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lamani M.R.; Julian Benadit P.; Guruprasad C.
Lecture Notes in Electrical Engineering, Vol-1247 LNEE, pp. 497-510.
Lamani M.R., CHRIST (Deemed to Be University), Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India; Julian Benadit P., CHRIST (Deemed to Be University), Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India; Guruprasad C., Departmentof Psychiatry, Bangalore Medical College & Research Institute (BMCRI), Karnataka, Bangalore, 560002, India