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3rd International Conference on Integrated Circuits and communication systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Amudha, K. Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Mullana Ambala India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance. © 2025 IEEE.

关键词： Students

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Machine Learning Heart Disease Prediction Using KNN and RTC Algorithm 3

Machine Learning Heart Disease Prediction Using KNN and RTC ...

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3rd International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2022

作者： Senthil, G.A. Prabha, R. Razmah, M. Veeramakali, T. Sridevi, S. Yashini, R. Agni College of Technology Dept of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Dept of Data Science and Business Systems Chennai India Sri Ramachandra Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462754

Heart Disease or cardiovascular disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age, sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include KNN and decision tree classifier which is compared to yield the desired and efficient output. © 2022 IEEE.

关键词： Heart

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Holographic-Pattern Based Multi-User Beam Training in RHS-Aided Hybrid Near-Field and Far-Field communications

arXiv

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arXiv 2024年

作者： Zhang, Shupei Di, Boya Kaushik, Aryan Eldar, Yonina C. State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China The Department of Computing & Mathematics Manchester Metropolitan University United Kingdom The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Reconfigurable holographic surfaces (RHSs) have been suggested as an energy-efficient solution for extremely large-scale arrays. By controlling the amplitude of RHS elements, high-gain directional holographic patterns can be achieved. However, the complexity of acquiring real-time channel state information (CSI) for beamforming is exceedingly high, particularly in large-scale RHS-assisted communications, where users may distribute in the near-field region of RHS. This paper proposes a one-shot multi-user beam training scheme in large-scale RHS-assisted systems applicable to both near and far fields. The proposed beam training scheme comprises two phases: angle search and distance search, both conducted simultaneously for all users. For the angle search, an RHS angular codebook is designed based on holographic principles so that each codeword covers multiple angles in both near-field and far-field regions, enabling simultaneous angular search for all users. For the distance search, we construct the distance-adaptive codewords covering all candidate angles of users in a real-time way by leveraging the additivity of holographic patterns, which is different from the traditional phase array case. Simulation results demonstrate that the proposed scheme achieves higher system throughput compared to traditional beam training schemes. The beam training accuracy approaches the upper bound of exhaustive search at a significantly reduced overhead. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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An Investigation of Smart Detection for Small Lung Tumor with Tumor Pattern Recognition Algorithm

An Investigation of Smart Detection for Small Lung Tumor wit...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Hariharan, G. Prasanth, P. Devarani, P. Arthi Sajana, T. Indhumathi, C. Kumar, Ashok Malla Reddy University Deportment of Artificial Intelligence and Machine Learning Telangana Hyderabad500100 India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamil Nadu Chennai600062 India R. M K College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Thiruvallur601206 India KoneruLakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram522502 India R.M. K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai601206 India BanasthaliVidyapith Department of Computer Science Rajasthan304022 India

ISBN: (纸本)9798350334357

The Small-cell lung tumor is the prime public concern, resulting in increased mortality. Various therapeutic approaches have made progress in the handling of small-cell lung tumor. It's considered the backbone of cytotoxic chemotherapy treatment. However, clinical research shows that because small-cell lung tumor is chemically approachable, high response rates among small-cell lung tumorinfecters are generally not translated into cure. Therefore, it is high time to make progress in small-cell lung tumor research because every success in clinical trials is fought, and every failure is a lesson. A recent study of small-cell lung tumor has improved the prognosis of patients. Some notable research areas have provided new options for patients by conducting multiple clinical trials. In this paper smart edge detection based small lung tumor identification method was proposed. This model operates with the pattern recognition algorithm model. This algorithm smartly finds the edges of the tumor cells and provides the edge results. This will helpful for the doctors to identify the tumor part exactly. © 2023 IEEE.

关键词： Tumors

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A Smart Framework to Analyze Hotel Services after COVID-19 9

A Smart Framework to Analyze Hotel Services after COVID-19

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Alsehaimi, Afnan Abdulrahman A. Abi Sen, Adnan Ahmed Srinivas, Rajesh Bahbouh, Nour Mahmoud Alsubhy, Abdullah M. Mobarak Al Masabi, Majed M. Northern Border University College of Science Arar Saudi Arabia Islamic University Faculty of Comp. and Info. Systems Al-Madinah Saudi Arabia University of Dayton Department of Computer Science OH United States Ugr Department of Information and Communication Sciences Granada Spain King Abdulaziz University Department of Comp. and Info. Systems Jeddah Saudi Arabia King Abdulaziz University College of Engineering Jeddah Saudi Arabia

ISBN: (纸本)9789380544441

In the last decade, many websites have allowed customers to provide reviews about their experiences in dealing with different hotels like describing their opinions about different sides like cleanliness, food, employees, and other services. However, after COVID-19, new concerns have emerged linked to the health precautionary and preventive processes. At the same time, the amount of generated data (Textual Data) has become very huge (Big Data), and it needs to auto classifier for processing it where it is impossible to review manually. So, this research proposed a smart method to detect useful information from text by extracting the interesting services from comments automatically. The method depended on machine learning (ML) and compared between five different classification models (Spacy, Naïve Bayes (NB), Stochastic Gradient Descent (SGD), Passive Aggressive, and AdaBoost) to determine which one provides the best results and accuracy. According to the experiment on a real dataset containing more than 1000 records, the NB was the best accurate with 98%, while the Spacy was less and that related to the small size of training data. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： COVID-19

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Code Writing Problems for Basic Object-Oriented Programming Study in Java Programming Learning Assistant System

Code Writing Problems for Basic Object-Oriented Programming ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Khaing Hsu Wai Nobuo Funabiki Soe Thandar Aung Xiqin Lu Yanhui Jing Htoo Htoo Sandi Kyaw Wen-Chung Kao Department of Information and Communication Systems Okayama University Okayama Japan Department of Computer and Information Science Tokyo University of Agriculture and Technology Tokyo Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

Nowadays, Java has been extensively used in industries as a reliable and portable object-oriented programming (OOP) language. To support novice students in learning Java programming, we have developed a web-based system called Java Programming Learning Assistant System (JPLAS). JPLAS offers the Code Writing Problem (CWP) among several types of exercise problems. A CWP instance requests a student to write a source code that will pass the provided test code in the assignment where the accuracy of the code is verified by running them on JUnit. In Java programming, every student should master basic OOP concepts, which can be challenging for novice students. This paper presents CWP instances for studying six basic OOP concepts including encapsulation, polymorphism, constructor, inheritance, abstract, and interface in JPLAS. Our preliminary evaluations of them confirm their validity.

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3D polarimetry of laser induced speckle fields for phase detection thyroid gland polycrystalline structure and traumatic necrosis of human internal organs

3D polarimetry of laser induced speckle fields for phase det...

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Photonics Applications in Astronomy, communications, Industry, and High Energy Physics Experiments 2024

作者： Ushenko, Olexander Bilookyi, Olexander Ushenko, Yuriy Dubolazov, Alexander Soltys, Irina Olar, Olexander Mikirin, Ivan Skliarchuk, Valeriy Salega, Olexander Karas, Oleksandr Rakhimbayeva, Gulbarshin Rogovyi, Yuriy Kozan, Nataliia Zheng, Jun Alimbayeva, Zhadyra Omiotek, Zbigniew Taizhou Institute of Zhejiang University Taizhou China Optics and Publishing Department Chernivtsi National University Chernivtsi Ukraine Department of Surgery Bucovinian State Medical University Chernivtsi Ukraine Computer Science Department Chernivtsi National University Chernivtsi Ukraine Biomedical Engineering and Optic-Electronic Systems Department Vinnitsia National Technical University Vinnytsia Ukraine Department of Information Communication Technologies Kazakh National Medical University named after S.D. Asfendiyarova Almaty Kazakhstan Department of Pathological Physiology Bucovinian State Medical University Chernivtsi Ukraine Department of Forensic Medicine Medical and Pharmaceutical Law State Medical Academy Ivano-Frankivsk Ukraine Kazakh National Women's Teacher Training University Almaty Kazakhstan Faculty of Electrical Engineering and Computer Science Lublin University of Technology Lublin Poland

ISBN: (数字)9781510685796

ISBN: (纸本)9781510685789

A short theory of the processes of formation of the laser speckle fields polarization structure of biological tissues with optically anisotropic architectonics is presented. A method of polarization interference mapping of speckle fields with layer-by-layer phase scanning of holographically reconstructed distributions of complex amplitudes of biological preparations of various morphological structures and physiological states has been proposed and experimentally tested - biopsies of the thyroid gland and blood films of rats with different severity of sepsis. Layer-by-layer maps of polarization ellipticity of native histological sections of thyroid biopsies from donors and sick patients were studied. As part of the statistical analysis of phase coordinate distributions of polarization ellipticity, objective markers for the differential diagnosis of thyroid pathology were found - nodular goiter, autoimmune thyroiditis and papillary cancer. Very good (~90%) and excellent (≥90%) accuracy of differential diagnosis was achieved. Statistical markers for temporal monitoring of septic changes in polarization azimuth maps of polycrystalline blood films of rats were found. An example of further research using laser-induced autofluorescence polarimetry methods is presented. © 2024 SPIE.

关键词： Optical anisotropy

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Vulnerability Testing on IoT Networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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Advances in Data Engineering and Intelligent Computing systems (ADICS), International Conference on

作者： R. Mohandas D.S.John Deva Prasanna Baiju B V S. Nathiya T. Dhiliphan Rajkumar C. Sivashankar Department of Computational Intelligence SRM Institiute of Science and Technology Chennai India Department of Datascience and Business systems SRM Institiute of Science and Technology Chennai India School of Computer Science and Engineering(SCOPE) VIT University Vellore TamilNadu India Department of Electronics and Communication Engineering Prince shri Venkateshwara Padmavathi Engineering College Chennai Dept. Of Computer Science and Engineering Kalasalingam Academy Of Research And Education Tamilnadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennnai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks; therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities.

关键词： Learning systems Industries Pathology Machine learning algorithms Intrusion detection Machine learning Internet of Things

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Evolution-Based Performance Prediction of Star Cricketers

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computers, Materials & Continua 2021年第10期69卷 1215-1232页

作者： Haseeb Ahmad Shahbaz Ahmad Muhammad Asif Mobashar Rehman Abdullah Alharbi Zahid Ullah Department of Computer Science National Textile UniversityFaisalabadPakistan Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900PerakMalaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems King Abdulaziz UniversityJeddahSaudi Arabia

Cricket databases contain rich and useful information to examine and forecasting patterns and *** paper predicts Star Cricketers(SCs)from batting and bowling domains by employing supervised machine learning *** this aim,each player’s performance evolution is retrieved by using effective features that incorporate the standard performance measures of each player and their *** is performed by applying Bayesianrule,function and decision-tree-based *** evaluations are performed to validate the applicability of the proposed *** particular,the impact of the individual features on the prediction of SCs are ***,the category and model-wise feature evaluations are also conducted.A cross-validation mechanism is applied to validate the performance of our proposed approach which further confirms that the incorporated features are statistically ***,leading SCs are extracted based on their performance evolution scores and their standings are cross-checked with those provided by the International Cricket Council.

关键词： Online social databases cricket star cricketers prediction machine learning

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Temperature Dependence of Hot-Electron Graphene Fet Bolometric Detectors Response to Modulated Terahertz Radiation

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International Journal of High Speed Electronics and systems 2024年第4期33卷

作者： Ryzhii, Maxim Ryzhii, Victor Tang, Chao Otsuji, Taiichi Mitin, Vladimir Shur, Michael S. Department Of Computer Science And Engineering University Of Aizu Aizu-Wakamatsu Fukushima965-8580 Japan Research Institute For Electrical Communication Tohoku University Miyagi Sendai980-8577 Japan Department Of Electrical Engineering University At Buffalo Suny BuffaloNY14260 United States Department Of Electrical Computer And Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

In this paper, we analyze the modulation characteristics and the ultimate modulation frequency of the terahertz (THz) hot-electron FET bolometers with the graphene channels (GCs), metal gate (MG), and gate barrier layers (BLs) in a wide temperature range. Our results predict that the responsivity of GC-FET bolometers decreases with decreasing operating temperature. This is attributed to a dramatic drop in the thermionic GC-MG current (characterized by the relatively large activation energy) and its modulated component when the temperature lowers. A further decrease in the temperature results in a fairly strong responsivity roll-off. In contrast, the responsivity of the GC-FET detectors with the temperature-adjusted load resistance rises with decreasing temperature because the temperature lowering leads to an increase in the electron energy relaxation time and promotes more effective heating by the impinging THz radiation. In this case, the weakening of the thermionic current is compensated by the commensurate increase in the load resistance. © 2024 World Scientific Publishing Company.

关键词： Graphene

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