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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Vaidya, Ruchika Dattangire, Rahul Biradar, Divya Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Data Engineering Publicis Sapient HoustonTX77002 United States University of Texas Department of Computer Science ArlingtonTX76019 United States

ISBN: (纸本)9798350372816

The current study focuses on how various Machine Learning (ML) algorithms can be used in waste management and emphasizes the significance of intelligent technologies in addressing contemporary issues. By leading a broad writing survey, the paper features the meaning of incorporating these advances into waste management practices. The system segment subtleties the information assortment, preprocessing, model determination, and assessment measurements utilized in the review. The accuracy and performance metrics of various classifiers, including K Nearest Neighbors, Linear Support Vector Classifier, Logistic Regression, Decision Tree, Multi-Layer Perceptron, and Random Forest, are demonstrated by the results of experiments with them. Especially noteworthy is the MLP classifier's 91.81% accuracy. The discoveries highlight the viability of ML approaches in ssmart waste management. In addition, a comparison table that summarizes the classifiers' accuracy scores provides insight into their suitability for applications in waste management. © 2024 IEEE.

关键词： Adversarial machine learning

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Sorting the Digital Stream: Big Data-driven Insights into Email Classification for Spam and Ham Detection

Sorting the Digital Stream: Big Data-driven Insights into Em...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Shah, Syed Attique Arputham, Emil Anthony Ahmed, Awais Farah, Mohamed Ben Shah, Attal Aziz, Abdul Birmingham City University STEAMhouse School of Computing and Digital Technology BirminghamB47RQ United Kingdom University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan China Faculty of Ict Buitems Department of Computer Engineering Quetta Pakistan Universidad de Zaragoza Aragon Institute of Engineering Research Zaragoza50018 Spain

ISBN: (纸本)9798350324457

In contemporary email communication, the ever- expanding volume of digital correspondence has ushered in an era where big data plays a pivotal role in addressing the challenge of distinguishing between legitimate (ham) and unsolicited (spam) emails. The primary objective of this paper is the meticulous identification and establishment of criteria for the discrimination between ham and spam emails. To achieve this, the study harnesses data from three distinct datasets, aiming to identify common attributes shared across all emails, irrespective of their classification, while concurrently devising methodologies for precise spam detection. Central to this endeavor is the evaluation of the effectiveness of feature selection techniques, specifically Chi-Square and Pearson Correlation, in elevating the accuracy of email classification. The investigation extends to assessing how the combination of these feature selection techniques with the broader machine learning framework can be optimized. This optimization entails the application of diverse preprocessing techniques to the datasets, all designed to amplify the precision of email classification. Furthermore, this research scrutinizes the performance evaluation metrics employed in the assessment of email classifiers. By conducting comprehensive experiments, the study identifies optimal classifiers based on rigorous evaluation metrics. This contributes valuable insights to the toolkit of techniques for proficient email classification within the realm of big data analysis. © 2023 IEEE.

关键词： Artificial Neural Network Classification Data Pre-processing Decision Tree Email Spam Detection Feature Selection Machine Learning Naive Bayes

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An Internet of Things Assisted Autism Spectrum Disorder Prediction Using Deep Learning Principles with medical Image Processing Assistance

An Internet of Things Assisted Autism Spectrum Disorder Pred...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bindhu, A. Mol, A. Sruthi Ravi, T. Bodkhe, R.G. Safori, Amjad Omar Vijayalakshmi, S. Infant Jesus College of Arts and Science for Women Department of Computer Science Mulagumoodu India Sathyabama Institute of Science and Technology Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India R.M.K. Engineering College Department of Electronics and Communication Engineering Chennai India Zaraa University Faculty of Media Department of Journalism and Digital Media Zaraa 13110 Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Characteristics of the complex neuro-developmental condition Autism Spectrum condition (ASD) include impaired social communication and restricted, repetitive patterns of behavior. This study presents a novel approach for identifying ASD in young infants by integrating deep learning, Internet of Things (IoT) and medical image processing. The goal of this research is to create comprehensive and discriminative features by combining wearable sensor data with brain imaging and smart home environments. For the greatest results, early diagnosis is essential. The proposed method called IoT based Autism Predictive Framework (IoTAPF) has a high detection rate of 97.8% and is cross-validated against the current Learning based Autism Detection Strategy (LADS). The proposed approach aims at improving diagnostic specificity which helps in forecasting behaviors and thus pushing earlier interventions, providing better support for ASD individuals and their families. The paper discusses the design and implementation of the framework, including sensor integration, data management, and predictive modeling. Preliminary results from pilot studies are presented, demonstrating the framework's potential to improve diagnostic accuracy and support individualized care plans. The proposed solution represents a significant advancement in the application of IoT technology for early autism detection and management, paving the way for more proactive and data-driven approaches in the field of neuro-developmental disorders. © 2024 IEEE.

关键词： Diseases

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Fuzzy Multi-Criteria Decision Support System for the Best Anti-Aging Treatment Selection Process through Normal Wiggly Hesitant Fuzzy Sets

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computers, Materials & Continua 2024年第9期80卷 4947-4972页

作者： Daekook Kang Ramya Lakshmanaraj Samayan Narayanamoorthy Navaneethakrishnan Suganthi Keerthana Devi Samayan Kalaiselvan Ranganathan Saraswathy Dragan Pamucar Vladimir Simic Department of Industrial and Management Engineering Institute of Digital Anti-Aging Health CareInje UniversityGimhae-si50834Republic of Korea Department of Mathematics Bharathiar UniversityCoimbatore641046India Department of SocialWork SRMV Collge of Arts and ScienceCoimbatore641020India Department of Radiology Karpagam Medical College and HospitalCoimbatore641032India Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Department of Industrial Engineering and ManagementYuan Ze UniversityTaoyuan City320315Taiwan Department of Mechanics and Mathematics Western Caspian UniversityBakuAZ1001Azerbaijan Faculty of Transport and Traffic Engineering University of BelgradeBelgrade11000Serbia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea

This socialized environment among educated and developed people causes themto focusmore on their appearance and health,which turns them towards medical-related treatments,leading us to discuss anti-aging treatment methods for each age group,particularly for urban people who are interested in *** anti-aging therapies are used to address the alterations brought on by aging in human life without the need for surgery or negative *** anti-aging therapies such as microdermabrasion or dermabrasion,laser resurfacing anti-aging skin treatments,chemical peels,dermal fillers for aged skin,and botox injections are considered in this *** on the criteria of safety risk,investment cost,customer happiness,and side effects,the optimal alternative is *** a result,a NormalWiggly Hesitant Pythagorean Fuzzy Set(NWHPFS)is constructed and used in Multi-Criteria Decision-Making(MCDM)using traditional wavy mathematical *** entropy approach is utilized to determine weight values,and the Normal Wiggly Hesitant Pythagorean-VlseKriterijumska Optimizacija I Kompromisno Resenje(NWHPF-VIKOR)method is utilized to rank alternatives using MCDM *** analysis and comparative analysis were performed to ensure the robustness and reliability of the proposed *** smart final choice will undoubtedly assist Decision Makers(DM)in making the right judgments,and the MCDM approach will undoubtedly assist individuals in understanding the medicine.

关键词： Normal wiggly hesitant pythagorean fuzzy set hesitant pythagorean fuzzy set anti-aging treatment entropy VIse Kriterijumska Optimizacija I Kompromisno Resenje

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Enterprise Architecture Trends in the Digital Transformation Era

Enterprise Architecture Trends in the Digital Transformation...

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2023 International Seminar on Application for technology of Information and Communication, iSemantic 2023

作者： Mahendra, Irfan Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Computer Science Jakarta11480 Indonesia Nusa Mandiri University Faculty of Information Technology Jakarta11450 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40116 Indonesia University of Indonesia Faculty of Computer Science Jakarta16424 Indonesia

ISBN: (纸本)9798350339215

Enterprise Architecture (EA) is a solution to build alignment between business strategy and information technology in dealing with digital transformation that causes fundamental changes for companies and businesses. The purpose of this study is to examine the trends and progress of EA in responding to the challenges of the digital transformation era, which is carried out through SLRs using the PRISMA method. According to the research findings, implementing EA in businesses serves the following purposes: establishing clear objectives and strategies for the development of business and technology, achieving harmony between business and information technology, time and cost efficiency, and effectiveness in business development processes and information technology. Then, it was concluded that there are nine components of EA, namely, vision, mission, and strategic objectives;business architecture;data & information architecture;information system architecture;infrastructure and network architecture;security architecture;human resources architecture;governance, legal, and compliance;and standards & regulations. © 2023 IEEE.

关键词： Network architecture

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IoT Based Human Biometric System Using Alcohol Level Detection for Driver Security 1

IoT Based Human Biometric System Using Alcohol Level Detecti...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Sinha, Anurag Kumar, Nitish Ticku, Amrita Modi, Vyom Shah, Meghal Shrivastava, Kapil School of Computing and Information Sciences Ignou New Delhi India Lovely Professional University Department of Computer Science Punjab Phagwara144001 India Bharti Vidyapeeth's College of Engineering India India Faculty of Engineering Marwadi Education Foundation Gujarat Rajkot360006 India Charotar University of Science & Technology Department of Computer Science & Engineering Gujarat 388421 India Department of Computer Engineering and Applications GLA University Mathura India

ISBN: (纸本)9798350330915

In today's society, driving after intoxication has become a major factor in traffic accidents. It is common knowledge that operating a vehicle while intoxicated is a major contributing factor in a significant number of accidents. The effects of drunk driving go beyond the drunk driver;they also affect other drivers and passengers in the same vehicle. As a result, this growing threat needs to be addressed immediately. Our project's goal is to create a system that uses the Internet of Things (IoT) to find traces of alcohol in someone's breath and apply preventive measures in order to effectively address this issue. Although there are laws against drunk driving, it is difficult to enforce them widely because law enforcement cannot constantly be present on the roads to spot drunk drivers. This restriction increases the chance of accidents significantly. In order to reduce future risks, a trustworthy system that can recognize and discourage drunk drivers is urgently needed. Drunk driving deaths are increasing, so we need to figure out how to reduce these kinds of deaths. The breath analyzing sensor used in the suggested system is the MQ-3, which calculates the breath alcohol content of an individual. The amount of alcohol that is allowed in the blood in India is 0.03. The engine won't start if the person's blood alcohol content is higher than this limit, making it impossible for them to drive. Additionally, the system will send an alert, possibly through a Raspberry Pi, to a friend or relative, providing the exact location using GPS coordinates. This notification serves as a precautionary measure to keep others informed about the situation. Implementing such a system can contribute significantly to reducing accidents related to alcohol consumption. In conclusion, detectors like the one proposed in this project hold immense significance in preventing the loss of life and property caused by reckless driving under the influence of alcohol. © 2023 IEEE.

关键词： Internet of things

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Adaptive Real-Time Vehicle Usage Patterns with Cloud-Enabled Bayesian Networks

Adaptive Real-Time Vehicle Usage Patterns with Cloud-Enabled...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Blessy, Y.M. Amarnath, Raveendra N Antony Joseph Rajan, D. Dillibai, R. Aarthy, S.T. Ganeshbabu, T.R. R.M.K. Engineering College Department of Electornics and Communication Engineering Tamil Nadu Chennai India Mphasis Campbell Dr MelissaTX United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798331521394

In this research, we provide a new real-time method for managing vehicle utilization patterns using cloud-enabled Bayesian networks (BN). Traditional solutions often employ static models and batch processing to account for the ever-changing nature of vehicle utilization. The objective is to use cloud-enabled BN to improve real-time vehicle utilisation monitoring, optimise fleet management, and make better decisions. To improve the precision and responsiveness of consumption predictions, businesses are increasingly turning to cloud computing, enabling continuous, real-time data stream processing. To weather, traffic patterns, and historical data, among other elements that impact vehicle utilization, BN provide a strong foundation for modelling complicated connections. To increase the accuracy of its adaptive use pattern forecasts, the system updates probabilistic associations in real time using incoming data. Fleet managers may optimize resource allocation, improve service delivery, and achieve overall operational efficiency via incorporating cloud computing, enabling scalable and efficient processing. In metropolitan areas, where use patterns might change quickly, this method helps manage vehicle fleets more adaptively and responsively. It showcases the practicality and advantages of our method in simulations and case studies, showing how it can change the game for fleet management techniques. It highlights the revolutionary power of BN offered by the cloud in improving the efficiency and responsiveness of fleet operations. © 2025 IEEE.

关键词： Resource allocation

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Accurate Phase Detection for ZigBee Using Artificial Neural Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 2505-2518页

作者： Ali Alqahtani Abdulaziz A.Alsulami Saeed Alahmari Mesfer Alrizq Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 *** frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at ***,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less ***,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor ***,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ***-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification *** paper develops a neural network predictive detection method to enhance the performance of BPSK ***,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)***,Gaussian noise was injected into the BPSK simulation *** reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase *** evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score *** result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)*** addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.

关键词： Neural networks RNN BPSK phasedetection WPAN IEEE802.15.4 signal demodulation

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Robust multi-label classification via data reconstruction by neighborhood samples augmentation

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Multimedia Tools and Applications 2024年 1-26页

作者： Liu, Zhifeng Xi, Sitao Abeo, Timothy Apasiba Shen, Xiang-Jun Zhou, Conghua Song, Heping Li, Peiwang School of Computer Science and Communication Engineering JiangSu University Zhenjiang212013 China Faculty of Applied Science and Technology Tamale Technical University Tamale Ghana Jiangsu Yangjing Petrochemical Group Co.Ltd Yangjing China Nanjing University of Posts and Telecommunications Jiangsu Nanjing222000 China

In multi-label learning, traditional methods try to directly establish mapping functions between samples and their labels. However, such methods may suffer low classification performance due to inherent noise and incoherent representation of samples. Therefore, considering the correlation between samples and their neighbors, as they may share common feature semantics, a multi-label classification method via feature enhancement from neighborhood samples, referred to as DRNSA is proposed. In this method, we construct a Laplacian graph dynamically, by considering the distance of two samples in a projected low rank subspace. With this neighborhood selection strategy, we then build a multi-label classifier via feature enhancement from neighborhood samples. Different from past works that directly build classifiers from samples and their labels, we build a new enhanced data sample which is weighted by its semantically similar neighborhood samples. Thus, our method can obtain better robust subspace in very high noisy data representations. Experiments conducted on cal500, corel5k, corel16k1 and corel16k4 datasets, show a significant out-performance of our DRNSA method over multi-label classification methods, including MLSF, MLFE, BR, PLST, CSSP, CPLST, and FaIE. © The Author(s) 2024.

关键词： Noise abatement

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A Review of Wearable Devices for Affective Computing

A Review of Wearable Devices for Affective Computing

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Vilas S Machhi Apurva M Shah Computer Science & Engineering Department Faculty of Technology & Engineering The Maharaja Sayajirao University Of Baroda Vadodara India

Wearable technology is expanding rapidly in recent year. It is used in many applications in various domains, including affective computing. Affective computing is all about understanding and responding to human emotions. This paper will give an overview of a wearable device that was developed specifically for affective computing. It aims to increase the way we can understand user's emotions and allow for more personalized experiences based on their context. Multiple sensors and algorithms work together in this device to capture, analyze, and interpret our feelings through physical or behavioral signals. It can be worn as a smart-watch, wristband, or integrated into clothing or accessories. The device communicates wirelessly with a companion application installed on a smart-phone or a connected device, providing real-time feedback and insights about the user's emotional state.

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