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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sourav Rampal Shreenidhi H S Manish Shrivastava Akhilendra Pratap Singh Reena R Santosh D. Kumar Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

Actual-time statistics to date has updated, increasingly vital as the quantity of virtual information maintains updated growth. Effective choice-making depends on the capability to update access and analyze records. Device up-to-date techniques provide the capacity up-to-date enhance actual-time statistics up-to-date by using offering insights from disparate pieces of information. This paper addresses the potential benefits of using a system with date knowledge and updated, up-to-date strategies to broaden extra clever statistics up to date updated systems. It summarizes the contemporary nation of the art and highlights studies in machine learning and up-to-date imaginative and prescient real-time information up-to-date. It additionally discusses present-day challenges and shows viable instructions for future studies. The studies network up-to-date recall how gadgets are up to date know that updated strategies may be used to facilitate real-time get entry updated the maximum precious pieces of facts.

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Exploring the Use of Machine Learning Techniques for Enhancing Real-Time data Access

Exploring the Use of Machine Learning Techniques for Enhanci...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

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Predictive Performance and Clinical Implications of Machine Learning in Early Coronary Heart Disease Detection

Predictive Performance and Clinical Implications of Machine ...

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Communication & Computing (WCONF), World Conference on

作者： V Dankan Gowda Annepu Arudra K M Mouna Sanjog Thapa Vaishali N. Agme KDV Prasad Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Computer Science & Engineering Rajiv Gandhi Institute of Technology Bangalore Karnataka India Department of Electronics & Communication Engineering BGS Institute of Technology/ACU Mandya Karnataka India Department of Information System and Analysis Lamar University Beaumont Texas USA Department of Applied Science Bharati Vidyapeeth College of Engineering Navi Mumbai Maharashtra India Department of Research Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

The identification of CHD in the early stage is crucial due to a high probability of death and ineffective treatment outcomes. This paper assesses the feasibility as well as clinical usefulness of several machine learning techniques in the early diagnosis of CHD. We employed a large set of data containing patients' features: demographic characteristics, health history, results of tests and diagnosis. The work of the study was to use Random Forest, Support Vector Machine and Neural Networks to estimate the probability of the incidence of CHD. Cohesion was performed using cross-validation for training and validation of the models with accuracy, precision, recall, F1 score, and ROC-AUC study parameters. The findings of this study show that through machine learning, the identification and diagnosis of CHD early stages can be significantly improved with the RF model giving the highest accuracy. Challenges and possibilities of using these predictive tools in clinical practice are examined, with the focus on possible enhancements of patients' treatment and outcomes. Thus, the research under consideration proved that machine learning-based predictive models can be considered a promising avenue for the early diagnosis of CHD, which should be further explored and implemented.

关键词： Heart Training Support vector machines Radio frequency Accuracy Neural networks Predictive models Vectors Random forests Diseases

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Leveraging Machine Learning to Predict IFC Project Success and Failure

Leveraging Machine Learning to Predict IFC Project Success a...

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Women in Computing (InCoWoCo), International Conference for

作者： S. Jayanthi JeyaGanesan J Ismatha Begum E Ramesh Ravikumar Balaraman Sunkam Gunasree Department of AI&DS Faculty of Science and Technology The ICFAI Foundation for Higher Education Telangana India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Information Technology Guru Nanak Institute of Technology Ibrahimpatnam Telangana India Department of Finance and Accounting ICFAI Business School The ICFAI Foundation for Higher Education Telangana India Department of Information Technology Teegala Krishna Reddy Engineering College Telangana India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

This research expounds on the efficacy and impact of a machine learning-based approach in managing the investment services projects of the International Finance Corporation (IFC). It is achieved by focusing on the impact of financial interventions and project completion. We used a detailed dataset to assess trends in project status over time. The details about the growth of completed and active projects and the fluctuations in pending stages, like signing, disbursement, and approval are available in the dataset. We experimented with the ML models, including Random Forest, Gradient Boosting, Logistic Regression, SVC, and Decision Tree, to predict project outcomes. These models are evaluated with the metrics such as, accuracy, precision, recall, F1-score, and ROC AUC. After rigorous experiments, we observed that Gradient Boosting achieved the highest accuracy (90.76 % ) and Random Forest closely followed (90.43%). We identified significant trends that include a high success rate in project completion, an incremental growth in active projects, and a low occurrence of hold and pending approval statuses. We also performed statistical analysis and observed that loans dominate IFC's financial support and risk management and guarantees see less use. Additionally, correlations between total investments and other financial products highlight a diversified investment strategy. The findings emphasize opportunities for improving project approval efficiency, managing bottlenecks, and implementing data-driven decision-making to optimize investment strategies and enhance sustainable development outcomes. This research offers actionable insights for policymakers, demonstrating how ML can improve project management processes and maximize the impact of IFC investments in developing economies.

关键词： Accuracy Biological system modeling Computational modeling Decision making Finance Predictive models Boosting Market research Random forests Investment

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Enhancement of IoT based Flood Detection and Prevention using Arduino UNO with WiFi Module

Enhancement of IoT based Flood Detection and Prevention usin...

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International Conference on Advanced Computing and Communication systems (ICACCS)

作者： R. M. Dilip Charaan J. Shobana P. Krishnamoorthy B. Anni Princy R. J. Abinaya K. Murugesan Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Tamil Nadu India Department of Computer and Communication Engineering Panimalar Engineering College Tamil Nadu India Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Tamil Nadu India Department of Electronics and Communication Engineering Loyola Institute of Technology Tamil Nadu India

The overflow of water from a lake or river usually causes flooding. Sometimes, a dam breach might result in the unexpected release of vast quantities of water. Some of the water seeps into the ground, flooding the region. In a station, rivers are involving the riverbanks. Along with a lack of goods and houses, businesses, and offices, street infrastructure floods contain bacteria, sewage from waste sites, and chemical spills, which later cause a number of diseases. The rate of change in river stage in real time, which can assist signal the gravity and immediacy of this hazard, is a crucial piece of information for flood predictions. Understanding the type of storm that produced the moisture, including its duration, strength, and actual extent, is important for determining the possible severity of the flood. In this system, an Arduino Uno is connected to four separate sensors: a humidity sensor, a flow sensor, a float sensor, and an ultrasonic sensor. The float sensor detects when the water is full. With the aid of IOT, these sensor combinations are utilised to predict floods, alert the appropriate authorities, and sound an immediate alarm in adjacent communities to rapidly relay information about potential floods. These sensors provide data via the IOT’s WiFi module. When flooding conditions are detected, the system warns the nearby villages and places and estimates how long it would take for help to reach at a particular location. The technology also determines when it might be considered a flood and gives them a window of time to leave in case it does.

关键词： Dams Storms Sensor systems Real-time systems Sensors Rivers Floods

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Empirical Evaluation of Phishing Attack Identification Over Websites by Using Intelligent Deep Learning Principle

Empirical Evaluation of Phishing Attack Identification Over ...

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Emerging Trends in engineering and Medical sciences (ICETEMS), International Conference on

作者： Anitha G Mouleeswaran S K Dickson Irudayaraj V G B Santhi Rajkumar Chadge Hani Attar Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of CSE Dayananda Sagar University Bangalore India Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Chennai Tamil Nadu India Department of CSE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Energy Engineer Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

Phishing schemes keep on dominating the cyber threat landscape hence requiring enhancement of detection methods. This research sets out a new and novel idea for the detection of phishing website approach incorporating the XGBoost with ANN to boost the identification performance. The proposed model was compared with nine machine learning and deep learning models which includes Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), SVM, KNN, Naïve Bayes, XG Boost, Artificial Neural Networks (ANN), CNN. It is apparent that the Ensembled XGBoost-ANN model was able to attain a higher accuracy rate of 9,463% and ranks on top of all the other models. It also showed great accuracy to prove precision, recall and F1-score metrics of the model. The findings reported herein support the suitability of the ensemble technique in modeling the intricate relationships characteristic of phishing websites, thereby holding the promise of being a practical solution in real-world environments. The reason why this methodology is promising for deployment in cybersecurity frameworks targeting the mitigation of phishing attacks is its capability of achieving high detection rates without making many false-positive detections.

关键词： Deep learning Support vector machines Radio frequency Measurement Accuracy Phishing Prevention and mitigation Artificial neural networks Nearest neighbor methods Random forests

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Traffic Light Control Using Reinforcement Learning

Traffic Light Control Using Reinforcement Learning

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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： S M Masfequier Rahman Swapno SM Nuruzzaman Nobel Ramachandra A C Md Babul Islam Rezaul Haque Mohammad Mominur Rahman Dept of CSE Bangladesh University of Business and Technology Dhaka Bangladesh Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Dept of CSE East West University Dhaka Bangladesh College of Science and Engineering Hamad Bin Khalifa University Qatar

Nowadays, one of the biggest issues in urban areas is traffic. This problem wastes important time and contributes to air and sound pollution. This affects people’s general quality of life in addition to posing health dangers. Our study attempts to mitigate these problems, effectively cutting down on wait times and delays. Our Reinforcement Learning method creates intelligent agents that can adjust traffic lights at crossings instantly. Our objective is to minimize delays, reduce congestion, reduce travel times, improve safety, and improve traffic flow. We implemented the Deep Q Learning algorithm which activities yield the greatest benefits under various traffic scenarios. Our model can the sequence time since the Green signal (GS) lasts 10 seconds and the Red signal (RS) lasts 5 seconds. The waiting period is shortened by 50% as a result. This study suggests reinforcement learning may improve traffic signal controller synchronization and urban traffic congestion. This novel method may improve transport efficiency and sustainability.

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Information Security and Privacy in the Digital World: Some Selected Topics

arXiv

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arXiv 2024年

作者： Sen, Jaydip Mayer, Joceli Dasgupta, Subhasis Nandi, Subrata Krishnaswamy, Srinivasan Mitra, Pinaki Singh, Mahendra Pratap Kundeti, Naga Prasanthi Rao, Chandra Sekhara M.V.P. Chekuri, Sudha Sree Pallapothu, Seshu Babu Nanjundan, Preethi George, Jossy P. Allali, Abdelhadi E.I. Morino, Ilham Oussous, Salma A.I.T. Beloualid, Siham Tamtaoui, Ahmed Bajit, Abderrahim Department of Data Science Praxis Business School Kolkata India Department of Computer Science and Engineering IIT Guwahati Assam India Department of Electronics and Electrical Engineering IIT Guwahati Assam India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangaluru India Department of CSE Lakireddy Balireddy College of Engineering Affiliated to JNTU Mylavaram Kakinada India Department of CSE RVR & JC college of Engineering Guntur India Department of Mathematics and statistics K.B.N. College Vijayawada India Department of Electrical Engineering Federal University of Santa Catarina Florianópolis Brazil Department of Data Science Christ University Lavasa Pune India National School of Applied Sciences Kenitra Morocco SC Department Mohammed V University Laboratory of Advanced Systems National Institute of Posts and Telecommunications Rabat Morocco

In the era of generative artificial intelligence and the Internet of Things,while there is explosive growth in the volume of data and the associated needfor processing, analysis, and storage, several new challenges are faced inidentifying spurious and fake information and protecting the privacy ofsensitive data. This has led to an increasing demand for more robust andresilient schemes for authentication, integrity protection, encryption,non-repudiation, and privacy-preservation of data. The chapters in this bookpresent some of the state-of-the-art research works in the field ofcryptography and security in computing and communications. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Predictive Analytics for Ear Health Management Using IoT and Ensemble Learning

Predictive Analytics for Ear Health Management Using IoT and...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： S.K. Saravanan Palaniraj Rajidurai Parvathy R. Meenakshi Prabu K T. R. GaneshBabu B Meenakshi Department of Big Data and Network Security Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Mphasis Corporation Chandler Arizona USA Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Gojan School of Business and Technology Redhills Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Proactive health monitoring solutions are becoming more important due to a growing population of older people. To better manage the ear health of the elderly, this research investigates ensemble learning methods within the domain of predictive analytics using the Internet of Things (IoT). It provides an ensemble learning approach that uses IoT capabilities to predict potential ear health problems in this population. It evaluates the efficacy of many ensembles learning approaches, including bagging, boosting, and stacking, using real-world sensor data acquired from IoT devices. Our results show that ensemble learning methods considerably improve prediction accuracy compared to standalone models. Ensemble learning overcomes the limitations of individual algorithms and maximizes their combined prediction potential by efficiently combining varied models. Our findings have important implications for the management of healthcare for the elderly, providing new opportunities to enhance early diagnosis and intervention techniques for ear health concerns via the use of predictive analytics provided by the IoT. It highlights the potential of ensemble learning approaches to improve predictive analytics in the senior healthcare sector, especially with IoT-driven solutions.

关键词： Accuracy Stacking Ear Medical services Predictive models Ensemble learning Internet of Things Older adults Predictive analytics Monitoring

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Text Mining for Automated data Analysis and Information Retrieval

Text Mining for Automated Data Analysis and Information Retr...

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International Conference on Computing and Networking technology (ICCNT)

作者： Vaishali Singh S Varalakshmi N.T. Velusudha Ashwini R. Nawadkar Manish Srivastava Hitesh Kalra Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Center for Management Studies Jain (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Electrical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Textual content Mining is the method of automatically analyzing vast amounts of natural language documents so that you can discover latent topics, extract vital understanding, and summarize relevant statistics. It consists of techniques. It presents critical insights into the content of information articles, emails, books, and other files, allowing us to gain insight into trends and styles that otherwise wouldn’t be seen. It can also stumble on sentiment, discover underlying entity relationships, extract key terms, and convey summaries. Textual content Mining is, for this reason, turning into an effective device for automatic information evaluation and records retrieval.

关键词： Text mining data analysis Natural languages Turning Market research Information retrieval Electronic mail Predictive analytics Robots

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