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arXiv 2024年

作者： Maji, Prasenjit Mondal, Amit Kumar Mondal, Hemanta Kumar Mahanty, Saraju P. Computer Science and Design Dr. B. C. Roy Engineering College WB 713206 India Computer Science and Engineering Bengal College of Engg. and Tech. WB 713212 India Electronics and Communication Engg. National Institute of Technology WB Durgapur713209 India Dept. of Computer Science and Engineering University of North Texas United States

The rapid advancements in artificial intelligence (AI) have revolutionized smart healthcare, driving innovations in wearable technologies, continuous monitoring devices, and intelligent diagnostic systems. However, security, explainability, robustness, and performance optimization challenges remain critical barriers to widespread adoption in clinical environments. This research presents an innovative algorithmic method using the Adaptive Feature Evaluator (AFE) algorithm to improve feature selection in healthcare datasets and overcome problems. AFE integrating Genetic Algorithms (GA), Explainable Artificial Intelligence (XAI), and Permutation and Combination techniques (PCT), the algorithm optimizes Clinical Decision Support Systems (CDSS), thereby enhancing predictive accuracy and interpretability. The proposed method is validated across three diverse healthcare datasets using six distinct machine learning algorithms, demonstrating its robustness and superiority over conventional feature selection techniques. The results underscore the transformative potential of AFE in smart healthcare, enabling personalized and transparent patient care. Notably, the AFE algorithm, when combined with a Multi-layer Perceptron (MLP), achieved an accuracy of up to 98.5%, highlighting its capability to improve clinical decision-making processes in real-world healthcare applications. © 2024, CC bY.

关键词： Electronic health record

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Enhanced Monitoring and Security Measures with Video Analysis

Enhanced Monitoring and Security Measures with Video Analysi...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： V. Esther Jyothi G. Rakesh Vipparla Aruna Shobana Gorintla Yalanati Ayyappa U Ganesh Naidu Department of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Vijayawada A.P. India Department of Computer Science and Engineering NRI Institute of Technology (Autonomous) Pothavarapadu A.P India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu India B V Raju Institute of Technology Narsapur Telangana India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

In modern surveillance, activities have increasingly become dependent on the continuous observation offered by CCTV systems. Still, with massive amounts of video data generated in a minute, sifting through this information manually to pick up any anomalies would be extremely burdensome and nearly impossible without significant human labor and vigilant watchfulness. CNN and CLIP models changed everything with regards to the working of surveillance systems. It makes use of CNN's capability for video frame processing and analysis to detect and classify these activities in the frames as either normal or suspicious. At the same time, it improves this by adding the CLIP model's capability of understanding textual descriptions and visual content together for better nuance detection in suspicious activities. This methodology transforms video surveillance by breaking video streams into frames and analyzing the behavior and interactions of persons in those frames. The synergy of CNN and CLIP models not only ensures real-time, efficient, and accurate surveillance but also minimizes dependency on extensive manual labor for monitoring. This paper aims to provide a contribution toward developing better security infrastructures in public spaces, transportation hubs, and private facilities. The introduction of this anomaly-detection mechanism can hugely improve the capability of implemented surveillance systems with a proactive response based on real-time detection and response capabilities against suspicious activities.

关键词： Visualization Transportation Transforms Manuals Streaming media Video surveillance Real-time systems Security Feeds Streams

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blind Source Separation: A Performance Review Approach 5

Blind Source Separation: A Performance Review Approach

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5th International Conference on Signal Processing and Information Security, ICSPIS 2022

作者： Taha, Luay Yassin Abdel-Raheem, Esam Div. of Business Eng. and Inf. Sciences and Tech. AltoonaPA16601 United States University of Windsor Department of Electrical and Computer Engineering WindsorONN9B 3P4 Canada

ISBN: (纸本)9781665492652

blind source separation (bSS) has been used extensively for the last three decades to extract different sources such as speech, biomedical, and image sources. The quality of extraction is governed by the extraction metrics of the bSS algorithms. The aim of this work is to review the extraction metrics for 27 different algorithms used in some previous works. Results show that the best algorithms in speech extraction is obtained when using independent low-rank matrix analysis (ILRMA) and canonical correlation (CC) based bSS. For image source separation, ZEODS algorithm has shown the highest extraction evaluation score. For fetal electrocardiogram (FEEG) source separation, the null space idempotent transformation matrix (NSITM) and the generalized functional link artificial neural network (GFLANN) show the highest evaluation scores. For electroencephalogram(EEG) signals de-noising and source extraction, the short and long term prediction blind source extraction (SLTP-bSE), b-spline mutual information independent component analysis (bMICA), and FASTICA, show the highest evaluation scores. The impact of evaluating the extraction algorithms result in optimum source separation. © 2022 IEEE.

关键词： blind source separation

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IoT-Genomics Analysis for Personal Health with Cybergenomics and Cloud technology

IoT-Genomics Analysis for Personal Health with Cybergenomics...

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International Conference on Communications and Signal Processing

作者： T. Prabakaran A. Ramesh babu S. Hemalatha Jayabharathi Ramasamy Rajesh Kambattan Kovarasan b Elango Department of Computer Science and Engineering Joginpally B.R. Engineering College Hyderabad Telangana India School of Computer Applications Vidya Vikas Institute of Engineering And Technology Mysore Karnataka India Department of Artificial Intelligence and Data Science Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study combines the potential of wearable health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may be achieved by seamlessly integrating real-time health data collected from IoT-enabled wearables with individual genetic profiles. Using algorithms for machine learning, connections between genetic differences and instantaneous health responses may be uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can be used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has been done sheds light on the revolutionary potential of merging genetics with the Internet of Things to establish a new paradigm of preventative and individualized medical treatment.

关键词： technological innovation Merging Medical treatment Genomics Real-time systems Internet of Things bioinformatics

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Adaptable Individualized Investment Recommendation System

Adaptable Individualized Investment Recommendation System

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： N. Sabiyath Fatima N. Noor Alleema C. Mahesh R. Umanesan K. Senthil P. Santhosh Kumar Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

Internetization has played a massive role in the rapid development and growth of the financial investment industry. Due to the rapid development, there is a demand for more financial knowledge services to help make well-informed investments. Accessing and conducting investments through online financial platforms has made the field highly accessible. This has made the investment recommendation system a popular research area. Personalized recommendations will help the user make investments that are safe for their financial situation and also suit their particular needs and interests. This work proposes a method where investment recommendations are given based on three key factors. The user interest sectors, risk appetite-based clustering, and market sentiment of the stock. These factors will be calculated by the use of Lasso regression, SVM, stepwise regression, generalized boosted regression, random forest, ridge regression and fuzzy clustering. based on these three factors, the proposed method results in investment recommendations made for users that are personalized to the specific user. This allows existing investors to gain new opportunities and new investors to start investing with lower amount of risk using the information available.

关键词： Support vector machines Uncertainty Social networking (online) Market research Stock markets Recommender systems Random forests Portfolios Pattern matching Investment

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A Statistical Analysis of Impact of COVID19 on the Global Economy and Stock Index Returns

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SN computer Science 2021年第1期2卷 27页

作者： Verma, Parag Dumka, Ankur bhardwaj, Anuj Ashok, Alaknanda Kestwal, Mukesh Chandra Kumar, Praveen Computer Science and Engineering Uttaranchal University Dehradun India Computer Science and Engineering Women Institute of Technology (Govt.) Dehradun India Computer Science and Engineering Chandigarh University Punjab India Computer Science and Engineering College of Technology G.B. Pant University of Agriculture and Technology Pantnagar India Atal Incubation Center EMPI New Delhi India Yuvayana Tech and Craft (P) Ltd. Uttar Pradesh Bilaspur India

The outbreak of pandemic COVID-19 across the world has completely disrupted the political, social, economic, religious, and financial structures of the world. According to the data of April 22nd, 2020, more than 4.6 million people have been screened, in which the infection has made more than 2.7 million people positive, in which 182,740 people have died due to infection. More than 80 countries have closed their borders from transitioning countries, ordered businesses to close, instructed their populations to self-quarantine, and closed schools to an estimated 1.5 billion children. The world’s top ten economies such as the United States, China, Japan, Germany, United Kingdom, France, India, Italy, brazil, and Canada stand on the verge of complete collapse. In addition, stock markets around the world have been pounded, and tax revenue sources have fallen off a cliff. The epidemic due to infection is having a noticeable impact on global economic development. It is estimated that by now the virus could exceed global economic growth by more than 2.0% per month if the current situation persists. Global trade may also fall from 13 to 32% depending on the depth and extent of the global economic slowdown. The full impact will not be known until the effects of the epidemic occurred. This research analyses the impact of COVID-19 on the economic growth and stock market as well. The aim of this research is to present how well COVID-19 correlated with economic growth through gross domestic products (GDP). In addition, the research considers the top five other tax revenue sources like S&P500 (GPSC), Crude oil (CL = F), Gold (GC = F), Silver (SI = F), Natural Gas (NG = F), iShares 20 + Year Treasury bond (TLT), and correlate with the COVID-19. To fulfill the statistical analysis purpose this research uses publically available data from yahoo finance, IMF, and John Hopkins COVID-19 map with regression models that revealed a moderated positive correlation between them. The model was

关键词： COVID19 Crude oil revenue Gold revenue Gross domestic products (GDP) iShares 20 + year treasury bond revenue Natural gas revenue Regression model S&P500 revenue Silver revenue World economy growth

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Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm

Lung Cancer Detecting using Radiomics Features and Machine L...

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technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： M.N. Rajesh Nikita Tanni Z. Tanveer baig Electronics and Communication Engineering Jain (Deemed-to be-University) Bengaluru India B.Tech in Computer Science and Engineering (Data Science) Christ (Deemed to be University) Bengaluru India Dept of IT Amity University Taskent Uzbekistan

Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GbM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably.

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Non-Rigid Registration with Volumetric Mesh for Liver Surgery

SSRN

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SSRN 2024年

作者： Zhang, Yanying Liu, Peter xiaoping Hou, Wenguo School of Mechanical Engineering Zhejiang Sci-Tech University Zhejiang Hangzhou China School of Information Science and Engineering Zhejiang Sci-Tech University Zhejiang Hangzhou China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

In image-guided liver surgery, the registration of preoperative reconstructed liver model with intraoperative captured surfaces is extremely challenging, due to the limited visibility of liver surfaces and the impact of soft tissue deformation. In order to solve this problem, this paper presents a novel nonrigid registration method that achieves accurate alignment between the preoperative reconstructed volumetric liver mesh and a portion of the intraoperative captured liver surface. A deformation subspace is incorporated to characterize the deformation of volumetric liver mesh during registration, ensuring volume preservation of the liver mesh. The proposed method enables to capture both surface and inside deformation of the liver, thus improving the flexibility and accuracy of the *** majorization-minimization (MM) algorithm with limited-memory broyden fletcher goldfarb shanno (L-bFGS) is introduced to solve the optimization problem in the registration process, thereby effectively improves the computational efficiency of the non-rigid registration. The experimental results show that the volume relative error is less than 2% and the registration error is less than 1mm. The proposed method can reduce mesh distortion, and maintain organ volume. It provides a promising solution for aligning the liver mesh between preoperative and intraoperative periods. © 2024, The Authors. All rights reserved.

关键词： Computational efficiency

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A Recommendation System for Crop Prediction Under Diverse Weather Conditions

A Recommendation System for Crop Prediction Under Diverse We...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： P ShunmugaLakshmi V Ram Kishore Pillai Pratham Ramachandran S Santhi S Kalaiselvi B.Tech Information Technology National Engineering College Kovilpatti Tamil Nadu Dept. of Information Technology National Engineering College Kovilpatti Tamil Nadu Dept. of Computer Science and Engineering National Engineering College Kovilpatti Tamil Nadu

In order to develop strategic initiatives in agricultural belonging for trading protocol and tripling countrymen earnings, early and accurate agricultural output estimation is pivotal in computable and commercial decision at the territory plane. Crop production forecasting, which is done to foresee a greater crop output, is the impressive difficulty in the agronomic division. To help farmers increase agricultural yield, this study has collected and evaluated data on N, P, K, temperature, humidity, ph, rainfall and soil_moisture. The superior values of this work are to recommend the crop to cultivate, suggest the appropriate fertilizers to use and predict the disease of a crop. A python environment is used to perform initial preprocessing on the data. KNN classification is used for developing the recommendation system. The link between the count, nutrients, temperature, humidity, pH, rainfall, and crop, are visualized using bar graphs and scatter plots. The tensor and torch were used to forecast crop disease and also to suggest appropriate fertilizer based on the data. The application was developed using Python Flask. This Classification was compared with other algorithms such as SVM, DT , RF and K-means.

关键词： Support vector machines Temperature sensors Linear regression Crops Humidity Prediction algorithms Classification algorithms

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A Secure Data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography

A Secure Data Encryption Mechanism in Cloud Using Elliptic C...

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Intelligent and Innovative technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： I. Sudha Cecil Donald S. Navya G. Nithya Manivannan balamurugan S. Saravanan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences – SIMATS Chennai India Department of Computer Science CHRIST (Deemed to be University) Bengaluru India Department of Computer Science Engineering Raghu Engineering College Visakhapatnam India Department of CSE V. S. B College of Engineering Technical Campus Coimbatore India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu India

Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, Dropbox, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-based environment. These providers also grant users the ability to access and use these applications from any location worldwide. The subject of security poses significant challenges in contemporary times. The primary objective of cloud security is to establish a sense of confidence between cloud service providers and data owners inside the cloud environment. The cloud service provider is responsible for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. Data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The objective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and robust encryption system due to its ability to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation.

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