Recent Advances in Pedestrian Identification Using LiDAR and Deep Learning Methods in Autonomous Vehicles
Autonomous vehicles; Deep learning; LiDAR; Pedestrian detection
The myriad benefits of autonomous vehicles (AVs) encompassing passenger convenience, heightened safety, fuel consumption reduction, traffic decongestion, accident mitigation, cost-efficiency and heightened dependability have underpinned their burgeoning popularity. Prior to their full-scale integration into primary road networks substantial functional impediments in AVs necessitate resolution. An indispensable feature for AVs is pedestrian detection crucial for collision avoidance. Advent of automated driving is swiftly materializing owing to consistent deployment of deep learning (DL) methodologies for obstacle identification coupled with expeditious evolution of sensor and communication technologies exemplified by LiDAR systems. This study undertakes exploration of DL-based pedestrian detection algorithms with particular focus on YOLO and R CNN for purpose of processing intricate imagery akin to LiDAR sensor outputs. Recent epochs have witnessed DL approaches emerge as potentially potent avenue for augmenting real-time obstacle recognition and avoidance capabilities of autonomous vehicles. Within this scholarly exposition we undertake exhaustive examination of latest breakthroughs in pedestrian detection leveraging synergy of LiDAR and DL systems. This discourse comprehensively catalogues most pressing unresolved issues within realm of LiDAR-DL solutions furnishing compass for prospective researchers embarking on journey to forge forthcoming generation of economically viable autonomous vehicles. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Halgatti A.; Chethana G.; Shivaprasad G.; Gayathri R.; Babu S.R.; Girish G.P.
Lecture Notes in Networks and Systems, Vol-1168 LNNS, pp. 187-196.
Springer Science and Business Media Deutschland GmbH
Halgatti A., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India; Chethana G., RV College of Engineering, Bengaluru, India; Shivaprasad G., Faculty of Management Studies, CMS Business School, JAIN (Deemed-to-be University), Bengaluru, India; Gayathri R., CMS Business School, JAIN (Deemed-to-be University), Bengaluru, India; Babu S.R., Faculty of Management Studies, CMS Business School, JAIN (Deemed-to-be University), Bengaluru, India; Girish G.P., Department of Finance, (a Deemed to-be-University Under Sec 3 of UGC Act 1956), ICFAI Business School, IFHE University, Hyderabad, India
Real-time Traffic Prediction in 5G Networks Using LSTM Networks
5G networks; deep learning; LSTM networks; network management; real-time traffic prediction
This research explores the application of Long Short-Term Memory (LSTM) networks for real-time traffic prediction within 5G networks, aiming to address the critical need for accurate prediction models in dynamic network environments. Leveraging the sequential learning capabilities of LSTM networks, the proposed methodology encompasses dataset preparation, model architecture design, training, and evaluation. Experimental results demonstrate the effectiveness of the LSTM-based prediction model in capturing temporal dependencies and providing reliable predictions across various prediction horizons. While promising, further research is warranted to enhance the model's performance and address remaining challenges. This study contributes to advancing the state-of-the-art in traffic prediction methodologies, facilitating more efficient network management and optimization in 5G environments. 2024 IEEE.
Kumar H.; Al-Jawahry H.M.; Manohar M.; Karthika K.; Victor M.; Shetty A.D.
2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024
Institute of Electrical and Electronics Engineers Inc.
Kumar H., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Application, Punjab, Mohali, India; Al-Jawahry H.M., 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; Manohar M., Ies College of Technology, Department of Electronics & Communication Engineering, M.P., Bhopal, India; Karthika K., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Victor M., Christ (Deemed to be University), School of Business and Management, India; Shetty A.D., (Deemed to be University) Nmam Institute of Technology (NMAMIT), Department of Cse Nitte, India
A Study on Challenges and Solutions in the Uptake of Agricultural Technology Startups Services in Karnataka
AgriTech; AgriTech adoption; AgriTech adoption challenges; Indian agriculture; Socio-economic factors; Solutions
In congruence with overarching trend of digitalization sweeping across India, agricultural sector is currently experiencing remarkable advancements propelled by innovative technological solutions introduced by emerging startups in agritech domain. The state of Karnataka is swiftly solidifying its position as preeminent leader in agritech industry attracting heightened interest from venture capital investors in recent times and emerging as dominant recipient of these investments garnering substantial 52% share followed by Maharashtra at 18% and Tamil Nadu at 9.2%. The principal aim of this research endeavor is to scrutinize socioeconomic impediments hindering adoption of AgriTech within rural precincts of Karnataka specifically in districts of Rural Bangalore (Doddabalapura and Nelmangala) and Davanagere (Shiramagondonahalli). The study seeks to gauge perceptions of farmers regarding potential solutions aimed at fostering greater adoption of AgriTech in these aforementioned regions. The study employed descriptive analysis by utilizing data obtained from judiciously selected sample of 120 farmers dichotomized into those who had availed themselves of AgriTech services and those who had not as provided by AgriTech firms. Empirical findings illuminate formidable impact of socioeconomic factors encompassing economic standing, land ownership classification and educational attainment in shaping farmers receptivity toward AgriTech utilization. The study unearthed valuable insights pertaining to propositions put forth by farmers to enhance adoption of AgriTech practices among their peers. The study furnishes valuable elucidations concerning barriers impeding adoption of AgriTech and offers viable solutions to invigorate increased participation among farmers in realm of AgriTech proffering pertinent recommendations to stakeholders such as AgriTech startup executives, researchers and policymakers urging them to meticulously assess local socioeconomic dynamics and tailor AgriTech services in accordance with discerned needs and preferences of farming community. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Lecture Notes in Networks and Systems, Vol-1168 LNNS, pp. 217-226.
Kumari S., Department of Economics, Christ (Deemed to be University), Bangalore, India; Vineeth M., Department of Economics, Christ (Deemed to be University), Bangalore, India; Girish G.P., Department of Finance, ICFAI Business School, IFHE University (Deemed to-be-University) Under Sec 3 of UGC Act, 1956), Hyderabad, India
Behavioral Time Management Analysis: Clustering Productivity Patterns using K-Means
Behavioral Analysis; Digital Tools; K-Means Clustering; Machine Learning; Productivity Patterns; Time Management
This paper focuses on investigating the efficiency profile through the three-time management behaviors using the K-Means clustering method. In the case of the study, the data gathered from digital time management tools for 100 participants for one month was preprocessed to distil features surrounding productivity, including daily working hours, focus time, break duration and frequency, and task completion ratios. The four groups that were agreed upon through K-Means clustering differed in terms of time management behaviours and productivity. Insert table 6 IT cluster 1 worked long hours with high productivity owing to the fact that they are IT professionals but had a tendency of multitasking. Employment Cluster 2 (marketing and sales professionals) achieved both personal and work-related self-care but identified the need for more concentrated time per task. As for the differences in the breaks, it can be noted that cluster 3 (management and administration personnel) had significantly higher task completion times and focus times, but their break intervals needed to be optimized. Hypothesis 2 stated that there will be many hours of leisure for Cluster 4 (students and interns) imply that their work hours should be adjusted to several small tasks a day, and their rates of task completion should be increased. From the study, it is possible to stress that time management should be considered as an individual activity that requires specific approaches to the given subject area and to the learner in particular. Specifically, demographic profiling identified the roles that age and occupational status may play in averting or exacerbating productivity deficiencies: insights that could be actionable in specific scenarios. The implications of this research offer practical insights into individual and organizational time management, as the usability aspects of machine learning techniques were considered and their applicability established, which further extends the scope of time management by revealing patterns and improving time management plans and practices. 2024 IEEE.
Winster Praveenraj D.D.; Habelalmateen M.I.; Shrivastava A.; Kaur A.; Valarmathy A.S.; Patnaik C.P.
https://doi.org/10.1109/IICCCS61609.2024.10763887 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214355456&doi=10.1109%2fIICCCS61609.2024.10763887&partnerID=40&md5=15af96345e7f4fcd27fac464dd7a682a
Winster Praveenraj D.D., Christ University, School of Business and Management, Bangalore, India; 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; Shrivastava A., Ies University, Ies Institute of Technology and Management, Department of Computer Science & Engineering, M.P., Bhopal, India; Kaur A., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Application, Punjab, Jhanjeri, Mohali, 140307, India; Valarmathy A.S., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Patnaik C.P., Aditya Institute of Technology and Management, Department of Mba, Andhra Pradesh, Tekkali, India
Cloud-Based Cataract Recognition System Using Hybrid Classifier Model
Cataract recognition system; Deep convolutional neural network; Fundus images; Support vector machine classifier; Telemedical solution
One of the key challenges of ophthalmologists is to diagnose the various ranges of ophthalmological illnesses such as diabetic retinopathy, cataract, and glaucoma. Here, cataract disease is identified as the one of the leading and most common ophthalmological problems that occurs due to aging. A computer-assisted cataract detection and diagnosis support system is required by the ophthalmologists to overcome the error that occurs during manual screening process. So, a cloud-based cataract recognition system is proposed using the convolutional neural network with support vector machine classifier model to improve the prediction accuracy, sensitivity, specificity, precision, recall, F1-score, and Mathews correlation coefficient. Moreover, the four-layer convolutional neural network is finetuned with a rich set of features and trained with various network models such as Inception V3, MobileNet, VGG-16, VGG-19, and ResNet-101. Therefore, the proposed hybrid combination of ResNet-101 with support vector machine classifier makes better cataract detection and outperforms the existing classifier models in terms of above-mentioned performance evaluation metrics. Moreover, the proposed hybrid approach provides the better telemedical solution to remote people by providing accurate disease prediction and severity grading such as normal, mild, premature, and severe cataract. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Electrical Engineering, Vol-1247 LNEE, pp. 511-518.
Rajavel R., Christ University, Karnataka, Bengaluru, 560074, India; Nagappan P., Galgotias University, Uttar Pradesh, Greater Noida, 203201, India
Multimodal Emotion Recognition in HumanComputer Interaction Using MFF-CNN
Deep learning; Emotion detection; HumanComputer Interaction (HCI); Machine learning; Multimodal data processing
The rise of technology in the digital era has amplified the importance of understanding human emotions in enhancing humancomputer interactions. Traditional interfaces, mainly focused on logical tasks, often miss the nuances of human emotion, creating a gap between human users and technology. Addressing this gap, the development of the HumanComputer Interface for emotional intelligence uses advanced algorithms and deep learning models to accurately recognize emotions from various cues like facial expressions, voice, and written text. This paper presented a significant approach for emotion detection in HCI and the challenges faced in capturing genuine emotional responses. Historically, the emphasis in HCI design was on operational tasks, neglecting emotional nuances. However, the tide is changing toward embedding emotional intelligence into these interfaces, leading to enhanced user experiences. This research introduces the MFF-CNN, a neural network model combining both textual and visual data for accurate emotion detection. Through sophisticated algorithms and the integration of advanced machine learning techniques, this paper presents a refined approach to emotion detection in HCI, supported by a comprehensive review of related works and a detailed methodology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-1107 LNNS, pp. 49-59.
Ahmad A., Department of Computer Science, Maharishi University of Information Technology, Lucknow, India; Singh V., Department of Computer Science, Maharishi University of Information Technology, Lucknow, India; Upreti K., Department of Computer Science, Christ University, Ghaziabad, India
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.
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
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.
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