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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Riazulhameed, Arshadh Ariff Mohamed Abuthahir Ranganathan, Chitra Sabapathy Radhika, M. Saravanan, R. Visumathi, J. Srinivasan, C. Blackstraw Technologies Pvt Ltd Tamil Nadu Chennai India Mphasis Corporation Tamil Nadu Chennai India Department of Information Technology R.M.D. Engineering College Kavaraipettai Tamil Nadu Chennai India Department of Electrical and Electronics Engineering CARE College of Engineering Tamil Nadu Trichy India Department of Information Technology Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

Proper sleep is necessary for growth and development;hence, sleep stage prediction is very important for understanding people's sleep habits and quality, especially in children. A non-invasive and practical solution, wearable electronics, has recently emerged as a potential instrument for continual monitoring of sleep. Automated sleep stage prediction in children utilizing data acquired from wearable devices is proposed in this research using a cloud-based Support Vector Machine (SVM) technique. The technology makes use of data collected from wearable sensors that measure physiological signals, such as electrocardiograms (ECG), accelerometers, and electroencephalograms (EEG). To improve prediction accuracy while reducing computational complexity, feature extraction methods are used to extract relevant properties from raw sensor data. These features are selected for further use. The collected characteristics are used to train a SVM classifier on a cloud-based platform, which then sorts sleep phases. With its cloud-based design, massive data sets can be processed easily, and real-time predictions can be made with ease. When tested on industry-standard sleep datasets, the proposed technique outperforms innovative approaches in terms of accuracy, sensitivity, and specificity. By combining the power of wearable devices with cloud computing, the proposed approach provides an effective and dependable method for predicting the stages of a child's sleep automatically. © 2024 IEEE.

关键词： Sleep research

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Randomness-Efficient Constructions of Capacity-Achieving List-Decodable Codes

arXiv

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arXiv 2024年

作者： Mosheiff, Jonathan Resch, Nicolas Shang, Kuo Yuan, Chen Department of Computer Science Ben-Gurion University Informatics Institute University of Amsterdam Netherlands School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

In this work, we consider the task of generating list-decodable codes over small (say, binary) alphabets using as little randomness as possible. Specifically, we hope to generate codes achieving what we term the Elias bound, which means that they are (ρ, L)-list-decodable with rate R ≥ 1 − h(ρ) − O(1/L). A long line of work shows that uniformly random linear codes (RLCs) achieve the Elias bound: hence, we know O(n2) random bits suffice. Prior works (Guruswami and Mosheiff, FOCS 2022;Putterman and Pyne, arXiv 2023) demonstrate that just O(Ln) random bits suffice, via puncturing of low-bias codes. These recent constructions are essentially combinatorial, and rely (directly or indirectly) on graph expansion. We provide two new constructions, which are algebraic. Compared to prior works, our constructions are considerably simpler and more direct. Furthermore, our codes are designed in such a way that their duals are also quite easy to analyze. Our first construction — which can be seen as a generalization of the celebrated Wozencraft ensemble — achieves the Elias bound and consumes Ln random bits. Additionally, its dual code achieves the Gilbert-Varshamov bound with high probability, and both the primal and dual admit quasilinear-time encoding algorithms. The second construction consumes 2nL random bits and yields a code where both it and its dual achieve the Elias bound. As we discuss, properties of a dual code are often crucial for applications of error-correcting codes in cryptography. In all of the above cases – including the prior works achieving randomness complexity O(Ln) – the codes are designed to "approximate" RLCs. More precisely, for a given locality parameter L we construct codes achieving the same L-local properties as RLCs. This allows one to appeal to known list-decodability results for RLCs and thereby conclude that the code approximating an RLC also achieves the Elias bound (with high probability). As a final contribution, we indicate that such a proof

关键词： Random processes

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Enhanced Crop Price Prediction & Forecasting System 1

Enhanced Crop Price Prediction & Forecasting System

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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Sajithabanu, S. Ponmalar, A. Soundari, A. Gnana Reshma, N.V. Sunraja, K. Sindhumathi, R. Mohamed Sathak Engineering College Department of Mca Kilakarai India Sri Sai Ram Institute of Technology Department of Information Technology Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India

ISBN: (纸本)9798350397840

Agriculture Price prediction takes into consideration of all the attributes that have been in the quo for long time. In an economy like India, where in the market is open to all and is a majorly Agri based economy, the prediction and forecasting of crop prices and forecasting the markets will be of great boost to the farmers and also to the country. This paper puts forward a claim to implement Machine Learning algorithms to foresee the Agriculture Price predictions and deploy the latest trends in ML for the same. There are multiple techniques used for data mining such as K-Means, K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) in machine learning to support the cause. © 2022 IEEE.

关键词： Python

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DL-based Rheumatoid Arthritis Prediction using Thermal Images 6

DL-based Rheumatoid Arthritis Prediction using Thermal Image...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Kumar, Dirisala J Nagendra Vidhya, K. Sagar Imambi, S. Pramila, P.V. Kumar, Ashok Vijayabhaskar, V. Vishnu Institute of Technology Department of Information Technology Andhra Pradesh 534202 India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore641407 India Koneru Lakshmaih Deemed to Be University Department of Computer Science Engineering Andhra Pradesh Vaddeswaram522302 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science Engineering Tamil Nadu Sriperumbudur600124 India Banasthali Vidyapith Department of Computer Science Rajasthan304022 India Tamil Nadu Kavaraipettai601206 India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Rheumatoid arthritis, often known as rheumatoid, is an inflammatory condition brought on by the immune system's *** preliminary tests were proposed to predict this chronic illness. This study proposes a deep learning model which can detect the presence of rheumatoid by analyzing the thermal images of a person. For this purpose, the palms of the rheumatoid patients and the control group were scanned to produce a sample of thermal pictures of human hands. The efficiency of this training is then improved by preprocessing the thermal pictures. The CNN-LS TM approach is used to build a deep learning model. Then, to accurately forecast the presence of rheumatoid, this model is trained using thermal pictures. The training's outcomes are noted and reviewed. Validation comes after training, and the outcomes of the validation are also tabulated. For simpler analysis, the findings are also plotted as graphs. The results show that as the number of epochs rises, accuracy, precision, and recall value all significantly increase. As the number of epochs rises, the loss value also falls. The model is then tested to determine the final values for each parameter after training and validation. The final accuracy score of the model is 92.78, while the loss score is 3.78, which is so minuscule as to occasionally be ignored. The model's precision is 95.4%, and its recall value is 93.7%. This deep learning model can be utilized as a screening tool for rheumatoidbecause of its improved accuracy and precision values. © 2022 IEEE.

关键词： Infrared imaging

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AgInS_(2)/ZnS quantum dots for noninvasive cervical cancer screening with intracellular pH sensing using fluorescence lifetime imaging microscopy

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Nano Research 2022年第6期15卷 5193-5204页

作者： Wenhua Su Dan Yang Yulan Wang Yawei Kong Wanlu Zhang Jing Wang Yiyan Fei Ruiqian Guo Jiong Ma Lan Mi Department of Optical Science and Engineering Shanghai Engineering Research Center of Ultra-precision Optical ManufacturingKey Laboratory of Micro and Nano Photonic Structures(Ministry of Education)School of Information Science and TechnologyFudan UniversityShanghai 200433China Institute for Electric Light Sources School of Information Science and TechnologyFudan UniversityShanghai 200433China Department of Gynecology and Obstetrics The Central Hospital of WuhanTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430014China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence NanophotonicsSchool of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Institute of Biomedical Engineering and Technology Academy for Engineer and TechnologyFudan University220 Handan RoadShanghai 200433China Shanghai Engineering Research Center of Industrial Microorganisms the Multiscale Research Institute of Complex Systems(MRICS)School of Life SciencesFudan University220 Handan RoadShanghai 200433China

Intracellular pH plays a critical role in biological functions,and abnormal pH values are related to various ***,we report on an intracellular pH sensor AgInS_(2)(AIS)/ZnS quantum dots(QDs)that show long fluorescence lifetimes of hundreds of nanoseconds and low *** lifetime imaging microscopy(FLIM)combined with AIS/ZnS QDs is used for the imaging of live cells in different pH buffers and different cell *** FLIM images of AIS/ZnS QDs in live cells demonstrate different intracellular pH values in different regions,such as in lysosomes or *** method can also distinguish cancer cells from normal cells,and the fluorescence lifetime difference of the AIS/ZnS QDs between the two types of cells is 100±7 *** importantly,the exfoliated cervical cells from 20 patients are investigated using FLIM combined with AIS/ZnS *** lifetime difference value between the normal and cervical cancer(CC)groups is 115±9 ns,and the difference between the normal and the precancerous lesion group is 64±9 *** the first time,the noninvasive method has been used for cervical cancer screening,and it has shown great improvement in sensitivity compared with a clinical conventional cytology examination.

关键词： AIS/ZnS quantum dots fluorescence lifetime imaging microscopy intracellular pH sensing cervical cancer screening noninvasive

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Machine Learning Based Smart Social Media Mining for Disorder Communication Detection 1

Machine Learning Based Smart Social Media Mining for Disorde...

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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Sajithabanu, S. Ponmalar, A. Dhanalakshmi, R. Lakshmi, R. Vivitha, M. Krishnan R, Harish Anantha Mohamed Sathak Engineering College Department of Mca Kilakarai India Sri Sai Ram Institute of Technology Department of Information Technology Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India

ISBN: (纸本)9798350397840

Social Media are gaining momentum in this digital era. Data is being shared on social media on random intervals across the world. The Population of the persons in social media has overcome the population of certain countries of the world, which implicates the growth of the social media at this juncture. Any news or posts in the social media can be so trending that the post get viral within fraction of time, thus attracting the attention of the other persons. Whilst this can be taken in the positive account, precautions have to be taken to avoid the social media being misused. Any news or posts related to hatred, spread of misappropriate content relating to religion, sex, countries or borders, or any other news that can harm or spread hatred towards any community or people has to be stopped, else it may create havoc or damages of property or a brand that can affect the lives and the reputation of many. This paper puts out a novel approach to identify those mental disorders communication from the social media accounts by using machine learning approach. © 2022 IEEE.

关键词： Python

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Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving engineering Applications

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computers, Materials & Continua 2024年第6期79卷 3631-3678页

作者： Ibraheem Abu Falahah Osama Al-Baik Saleh Alomari Gulnara Bektemyssova Saikat Gochhait Irina Leonova OmParkash Malik Frank Werner Mohammad Dehghani Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Software Engineering Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Science and Information Technology Software EngineeringJadara UniversityIrbid21110Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed UniversityPune412115India Neuroscience Research Institute Samara State Medical UniversitySamara443001Russia Faculty of Social Sciences Lobachevsky UniversityNizhny Novgorod603950Russia Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural *** draws its inspiration from the sit-and-wait hunting strategy of these *** algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after *** assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark *** functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test ***’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative *** simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search *** balanced approach enables FLO to outperform several competing algorithms in numerous test ***,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization ***,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

关键词： Optimization engineering bio-inspired metaheuristic frilled lizard exploration exploitation

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Benefits and drawbacks of radiofrequency ablation via percutaneous or minimally invasive surgery for treating hepatocellular carcinoma

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World Journal of Gastrointestinal Surgery 2024年第11期16卷 3400-3407页

作者： Ching-Lung Hsieh Cheng-Ming Peng Chun-Wen Chen Chang-Hsien Liu Chih-Tao Teng Yi-Jui Liu Department of Computer Science and Information Engineering Feng Chia UniversityTaichung 40724Taiwan Department of Surgery Chung Shan Medical University HospitalTaichung 40201Taiwan School of Medicine Chung Shan Medical UniversityTaichung 40201Taiwan Department of Radiology Taichung Armed Forces General HospitalTaichung 406Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and TechnologyTaichung 406Taiwan Department of Radiology School of MedicineNational Defense Medical CenterTaipei 11490Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital and China Medical UniversityHsinchu 302Taiwan Institute of Nuclear Engineering and Science National Tsing Hua UniversityHsinchu 300Taiwan Department of Radiology Nantou Hospital of the Ministry of Health and WelfareNantou 540Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia UniversityTaichung 407Taiwan Department of Automatic Control Engineering Feng Chia UniversityTaichung 407Taiwan

The management of early stage hepatocellular carcinoma(HCC)presents significant *** radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter hospital stays,its high recurrence rate remains a significant ***,achieving improved survival solely through RFA is challenging,particularly in retrospective studies with inherent *** is commonly used for guiding percutaneous RFA,but its low contrast can lead to missed tumors and the risk of HCC *** enhance the efficiency of ultrasound-guided percutaneous RFA,various techniques such as artificial ascites and contrast-enhanced ultrasound have been developed to facilitate complete tumor *** invasive surgery(MIS)offers advantages over open surgery and has gained traction in various surgical *** studies suggest that laparoscopic intraoperative RFA(IORFA)may be more effective than percutaneous RFA in terms of survival for HCC patients unsuitable for surgery,highlighting its ***,combining MIS-IORFA with these enhanced percutaneous RFA techniques may hold greater significance for HCC treatment using the MIS-IORFA *** article reviews liver resection and RFA in HCC treatment,comparing their merits and proposing a trajectory involving their combination in future therapy.

关键词： Percutaneous radiofrequency ablation Minimally invasive surgery Hepatocellular carcinoma Intraoperative radiofrequency ablation Contrast-enhanced ultrasound

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Real-time Glass Recycling Quality Assurance and Contamination Reduction with IoT and Random Forest algorithm 7

Real-time Glass Recycling Quality Assurance and Contaminatio...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Balasubadra, K. Shadaksharappa, B. Seeni, Senthil Kumar Sridevi, V. Thamizhamuthu, R. Srinivasan, C. R.M.D. Engineering College Department of Information Technology Chennai Tamil Nadu Kavaraipettai India Sri Sairam College of Engineering Department of Computer Science And Engineering Bengaluru Karnataka Anekal India Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Academy of Maritime Education And Training Deemed To Be University Electrical And Electronics Engineering Tamil Nadu Chennai India Srm Institute of Science And Technology Department of Computing Technologies Kattankulathur Campus Chennai Tamil Nadu Chengalpattu India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

The recycling industry is crucial to environmental protection and resource conservation. Glass recycling has great sustainability potential, but quality assurance and contaminant reduction are major challenges. Integrating the Internet of Things (IoT) with the Random Forest algorithm, this work offers a new solution to these difficulties. IoT devices are used in glass recycling collection, transportation, sorting, and processing. These devices measure glass quality, contaminants, temperature, humidity, and other parameters in real-time. Data is sent to a central system for analysis and decision-making. Random Forest, a machine learning method that handles complicated, multidimensional datasets, powers the system. This algorithm learns from past data and updates with real-time data. It detects contaminants, foreign elements, and faults in recycled glass. This permits fast remedial action, enhancing recycled glass quality and decreasing contamination. Application of this mechanism yields two outputs. First, it improves glass recycling efficiency and produces high-quality glass for remanufacturing. Second, it significantly minimizes the environmental and economic implications of contamination and low-quality glass, which may limit recycling yields and increase waste disposal. In comparison to traditional approaches, the proposed approach that utilizes the IoT and the Random Forest algorithm reduces contamination and ensures the quality of glass recycling by 30%. © 2024 IEEE.

关键词： Internet of things

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Enhancing Cryptocurrency Value Prediction: A Comparative Study of Novel Random Forest and K-Nearest Neighbor Algorithms for Improved Accuracy

Enhancing Cryptocurrency Value Prediction: A Comparative Stu...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： S. Kiruthiga R. Balamanigandan R. Mahaveerakannan A. Mary Jenifer Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The effectiveness of the Novel Random Forest (RF) Algorithm for predicting cryptocurrency prices was evaluated and compared to the K-Nearest Neighbor (KNN) Algorithm. Machine learning methods were used to develop the two algorithms, and a pretest power analysis was conducted using two groups with the iteration of 10 at 85% of G-power and the setup parameters are alpha = 0.05 and beta = 0.85. Hence the P value is less than 0.005 (P<0.05) there is a statistical significance (p=0.007) between these two algorithms. The Novel RF Algorithm achieved an accuracy of 87.7330%, while the KNN Algorithm achieved an accuracy of 72.1250%. When the two algorithms were compared using an independent sample t-test, the difference in accuracy was found to be statistically significant at 0.760.

关键词： Radio frequency Accuracy Machine learning algorithms Software algorithms Estimation Nearest neighbor methods Prediction algorithms Software Cryptocurrency Random forests

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