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2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021

作者： Sharma, Bhanu Singh, Amar Singla, Jimmy School of Computer Application Lovely Professional University Jalandhar India School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9781728194912

WMN's or Wireless Mesh Network is a communication network that has features like self-healing., self-organizing and the ability to create a network automatically in its dynamic environment. It mainly consists of mesh routers., mesh clients and gateways which results in building a rich connection among the devices and nodes. Considering the functionalities of it., this paper presents an overview of different routing protocols for WMN's. In this paper., we analyzed the performances of soft computing and hard computing protocols i.e. AODV., DSR and BBBC. From simulations., we observed that on large networks BBBC based approach outperformed the performances of AODV and DSR. © 2021 IEEE.

关键词： Soft computing

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Learning from Time-Tracking Data: Gaussian Process Regression for Efficient Time Management Systems

Learning from Time-Tracking Data: Gaussian Process Regressio...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Thakur, Bandna Allirani, P. Chourasiya, Prashant Srinu, K. Al-Farouni, Mohammed Santhosh, S. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Aditya Engineering College Department of Artificial Intelligence & Machine Learning Surampalem India The Islamic University Department Of Computers Techniques Engineering College Of Technical Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Department of Ise Nitte India

ISBN: (纸本)9798350390759

This paper aims at analyzing the use of Gaussian Process Regression (GPR) in working and creating effective time management system. It incorporates the GPR model that helps in predicting the future time usage by dissecting the time-study data this assists the users in scheduling. The approach adopted entails harvesting data from automated tools and manual logs, pre-processing the data collected, using k-fold cross-validation in training and validating the model. The forecasting accuracy features a low Mean Absolute Error and a high R-squared value, which signifies that the models perform well in terms of predictive capability. This research proves that GPR can actively help with data on time management and benefit time management systems as a whole. Some of the challenges that faced the research include quality of the data and the computation required to process the data was enhanced by effective preprocessing and refined algorithms respectively. © 2024 IEEE.

关键词： Data accuracy

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Multiple disease prediction using Machine learning algorithms

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Materials Today: Proceedings 2023年 80卷 3682-3685页

作者： Arumugam, K. Naved, Mohd Shinde, Priyanka P. Leiva-Chauca, Orlando Huaman-Osorio, Antonio Gonzales-Yanac, Tatiana Department of Computer Science Karpagam Academy of Higher Education Tamilnadu Coimbatore India Department of Business Analytics Jagannath University Delhi-NCR India Master of Computer Application Department Government College of Engineering Maharashtra Karad415124 India Administration and Tourism Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru Economics and Accounting Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru

Data mining for healthcare is an interdisciplinary field of study that originated in database statistics and is useful in examining the effectiveness of medical therapies. Machine learning and data visualization Diabetes-related heart disease is a kind of heart disease that affects diabetics. Diabetes is a chronic condition that occurs when the pancreas fails to produce enough insulin or when the body fails to properly use the insulin that is produced. Heart disease, often known as cardiovascular disease, refers to a set of conditions that affect the heart or blood vessels. Despite the fact that various data mining classification algorithms exist for predicting heart disease, there is inadequate data for predicting heart disease in a diabetic individual. Because the decision tree model consistently beat the naive Bayes and support vector machine models, we fine-tuned it for best performance in forecasting the likelihood of heart disease in diabetes individuals. © 2021

关键词： Data mining

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A Hybrid Reinforcement Learning Framework for Optimizing Node Deployment in WSNs 14

A Hybrid Reinforcement Learning Framework for Optimizing Nod...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Priyadarshi, Rahul Rout, Jayant Kumar Gupta, Akash Kumar Ranjan, Rakesh Verma, Amit Teja, Panduranga Ravi Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India Dept of ECE BIT Mesra Deoghar India School of Computer Science UPES Dehradun India

ISBN: (纸本)9798331531935

Efficient node deployment in Wireless Sensor Networks (WSNs) is critical for maximizing network coverage, enhancing energy efficiency, and prolonging network lifetime. However, achieving optimal node placement is challenging due to the limited energy resources and dynamic environmental conditions in WSNs. This paper presents a novel hybrid approach that combines Reinforcement Learning (RL) with the Differential Evolution (DE) algorithm to optimize node deployment in WSNs. In this method, RL dynamically adapts node positions based on local network states, while DE performs global optimization to refine these placements and improve coverage. Simulation results demonstrate that the proposed RL-DE hybrid approach significantly outperforms traditional deployment methods, standalone RL, and DE in terms of coverage, energy conservation, and network longevity. The results suggest that the RL-DE approach provides an adaptive and robust solution for node deployment in WSNs, with potential applications in real-world sensor network deployments. © 2025 IEEE.

关键词： Energy efficiency

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Intelligent Time Management Recommendations Using Bayesian Optimization

Intelligent Time Management Recommendations Using Bayesian O...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Chandramowleeswaran, G. Sindhu, M. Alabdeli, Haider Mishra, Anshu Vinod, Priya Kaur, Harmeet Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Commerce and Business Administration Chennai600062 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India 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 Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Christ University School Of Business And Management India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India

ISBN: (纸本)9798350390759

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.

关键词： Scheduling algorithms

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Detection of Stroke in CT Scans Using Deep Learning and Image Processing Techniques

Detection of Stroke in CT Scans Using Deep Learning and Imag...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Kaur, Gagandeep Nigam, Nishesh Khamrokulovna, Akhmadaliyeva Gulnora Almusawi, Muntather Kala Priyadarshini, G. Ashwin Shenoy, M. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Fergana Public Health Medical Institute Teacher of the Department of Biomedical Engineering Fergana Uzbekistan 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 Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Cse Nitte India

ISBN: (纸本)9798350390759

This work describes a robust paradigm for inferring strokes from CT scans using deep reinforcement learning and image analysis. The steps which are as follows: first, a large volume of high quality CT scan images will be gathered second, the pre-processing of the scan images to improve the image quality and third, an advanced CNN model will be designed for accurate stroke detection. This performance has been further enhanced by integrating data augmentation and the application of data transfer learning, where the model reaches an accuracy of 94. AUC-ROC of 0. 2% for the mice group and an AUC-ROC of 0. 2% for the rats group. 97. In conclusion, the structural and functional understanding of the model proves that the model is sensitive coupled with high specificity and was able to generalize beyond the data used in developing this model and across the different types of strokes including ischemic, hemorrhagic, and transient ischemic strokes. Comparing with benchmark models, it is evident that the proposed approach outperforms the baseline counterparts considerably. These outcomes can be concluded as the fact that computer-aided methods can help radiologists in the quick and accurate decision making with a possibility of positive changes to the disease progression. The future research areas on the proposed method are the increase in model interpretability and the incorporation of other modalities in an effort to increase the accuracy of the results. The study thus establishes the potential of Deep Learning in overhauling the conventional medical diagnosis and especially in timely accurate identification and diagnosis of strokes thus enriching health care. © 2024 IEEE.

关键词： Deep reinforcement learning

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Current scenarios, obstacles, possibilities, and innovations for sustainable municipal solid waste management following COVID-19 pandemic in Kathmandu valley, Nepal 2

Current scenarios, obstacles, possibilities, and innovations...

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2nd International Conference on computer science, engineering and applications, ICCSEA 2022

作者： Shah, Krishna Bikram Visalakshi, S. Avinash Khatri, K.C. Panigrahi, Ranjit Pokhara University Nepal Engineering College Department of Computer Science & Engineering Changunarayan Bhaktapur Nepal Sikkim Manipal Institute Technology Sikkim Manipal University Department of Computer Application Majitar Sikkim India University of East London Docklands Campus London United Kingdom

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Solid waste management (SWM) issues continue to be at the forefront of worldwide environmental policy towards the UN Sustainable Development Goals 2030. Despite the fact that it has been over two and a half years since the original COVID-19 outbreak, the entire world is still suffering from the infectious sickness caused by coronavirus. As a consequence, the garbage generated by families, businesses, and medical institutions has changed in composition. Use of personal protective equipment (PPE) such as masks and gloves as well as hand sanitizers by frontline workers in medical facilities, banks, shops, the sanitation industry, and other organizations, as well as by the general public, has shifted the waste generation trend in a new direction and presented new challenges. To provide improved health and human security, an effective and long-term sustainable SWM system is required. This study discusses the current status of SWM, identifies the challenges encountered by the SWM sector during the pandemic, as well as the underlying possibilities to eliminate existing system gaps. In the industrial revolution 4.0 era, attaining sustainability in waste management is becoming increasingly realistic, reliable, transparent, efficient, and optimal through the use of IoT, GIS, and ICT. It would further try to make some recommendations of few futuristic Solid Waste Management technologies which might be adapted in Nepal for improving the efficiency and effectiveness of SWM in change circumstances instigated by the COVID-19 pandemic. While providing innovative responses to current waste management constraints, the research also makes several crucial recommendations to policymakers to assist them deal with any future pandemics in a comprehensive manner. This research contributes to the accomplishment of the following Sustainable Development Goals (SDGs) such as good health, and wellbeing (SDG 3) and Climate Action (SDG 13). © 2022 IEEE.

关键词： COVID-19

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Parameters Research of Facial Emotion Detection Algorithm Based on Machine Learning

Parameters Research of Facial Emotion Detection Algorithm Ba...

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Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Zhou Ting Amit Yadav Shi Xiao Jiang Asif Khan Department of Computer Science and Technology Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University Australia Department of Computer Application Integral University Lucknow India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

The purpose of emotional state recognition is to let computers have the ability to analyze and understand human emotions and intentions, and deeply analyze human psychological activities, so as to play a role in the fields of entertainment, education, intelligent medical treatment and so on. Different emotion recognition algorithms and different parameters of the same algorithm have different recognition effects. Among them, muscle-based feature model and 68 feature point calibration are two common face emotion recognition methods. The former uses deep learning to judge the emotional state by analyzing the movement of face muscles, while the latter calculates various feature parameters through the position relationship of 68 key points of the face, and then judges the emotional state. This paper mainly discusses the calibration method of 68 feature points. Through the use of two machine learning algorithms (KNN, SVM) and the study of different parameters in the algorithm, the influence of different parameters on the emotion recognition effect is compared and analyzed. The experiment proves that by detecting 68 key points of face, we can find the optimal parameter value in the current classification task.

关键词： Support vector machines Emotion recognition Technological innovation Machine learning algorithms Face recognition Psychology Nearest neighbor methods

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Blockchain-Enabled Secure and Fair Online Lottery Systems Using Smart Contracts

Blockchain-Enabled Secure and Fair Online Lottery Systems Us...

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Silchar Subsection Conference (SILCON), IEEE

作者： Suseta Datta Rituparna Mondal Rajdeep Roy Sourav Banerjee Utpal Biswas Department of Computer Application Narula Institute of Technology Kolkata West Bengal India Department of Computer Applications Techno India University Kolkata West Bengal India Department of Computer Science and Engineering University of Kalyani Kalyani West Bengal India Department of Computer Science and Engineering Kalyani Government Engineering College Kalyani West Bengal India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

In the capricious digital dominion, conventional online lottery systems come across consequential impediment regarding transparency, fairness, and security. This paper presents an inventive solution by merging Blockchain technology and smart contracts into online lotteries to take forward these challenges. The preferred disperse implementation capitalizes on Blockchain's decentralized category to covenant the translucency and immutability of lottery transactions, thereby undoing the requisite for trusted interceders. Smart contracts are immersed to automate the entire lottery process-from ticket provisioning to winner selection and payout-certifying agreement to preconceived rules and reinforcing contender trust. Through meticulous perusal and enactment, illustration of how Blockchain technology can transform online lottery systems, providing users with a secure, transparent, and fair platform have been performed. Furthermore, the suggested system's resistance to recurrent deceit and its prospective impression on the online gambling industry are talked about, foregrounding the welfares of a Blockchain-drove technique to online lotteries.

关键词： Resistance Industries Smart contracts Merging Transforms Decentralized applications Blockchains Security

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Deep Learning Approach for Optimal Segmentation and Classification of Multi-Class Skin Cancer 11

Deep Learning Approach for Optimal Segmentation and Classifi...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Shekhawat, Jyoti Kumar, Ritesh Jebaraj, N.R Solomon Brar, Khushmeen Kaur Al-Jawahry, Hassan M. Thakur, Gaurav Vivekananda Global University Computer Science and Application Jaipur India Maharishi University of Information and Technology Uttar Pradesh India Department of Cse and Information Technology Bangalore India Chandigarh University Department of Electronics and Communication Engineering Punjab Mohali India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798350350357

This study proposed an efficient image fusion method for skin cancer diagnosis which combines two methods: image segmentation and image enhancement. A new image is created by fusing the k-means clustering and gray scale pseudo-color transformation methods for images. To extract both fine and coarse information from an image, the basic DWT is first used. With DWT, the pictures are broken down into wavelet co-efficients. The approximation coefficient, horizontal detailed coefficient, vertical detailed coefficients, and diagonal detailed coefficients are the four coefficient matrices that are created following decomposition. A fused coefficient matrix is produced by fusing the wavelet coefficients of two images together. The finished image is then recreated using the Inverse Wavelet Transform. The last step is to use the Random Forest Classifier to classify the test features. A number of performance metrics have been employed for evaluating the classification of benign and malignant skin cancer types: accuracy, mean absolute error (MAE), root-mean squared error (RMS), relative absolute error (RAE), root relative squared error (RRS), TP rate, FP rate, precision, recall, F-Measure, MCC, ROC area, and PRC area. With a kappa value of 0.73, MAE of 0.17, RMS value of 0.27, RAE value of 55.60, and RRS value of 69.08, the suggested methodology achieved an accuracy of 92.5%, according to the data. Additionally, the results for the benign class are 0.98, 0.325, 0.92, 0.98, 0.95, 0.75, 0.92, and 0.97 for TP, FP, Precision, Recall, F, MCC, ROC, and PRC, respectively. In contrast, the malignant class yielded results for TP, FP, Precision, Recall, F, MCC, ROC, and PRC of 0.67, 013, 0.893, 0.67, 0.78, 0.75, 0.92, and 0.83, respectively. © 2024 IEEE.

关键词： Discrete wavelet transforms

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