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2023 IEEE Conference on Technologies for Sustainability, SusTech 2023

作者： Baker, Nermeen Abou Stehr, Jonas Handmann, Uwe Ruhr West University of Applied Sciences Computer Science Department Bottrop Germany Technical University Dortmund Dortmund Electronic Engineering Department Germany

ISBN: (纸本)9798350345483

Many of us have defective electrical devices in storerooms or drawers. Recycling electrical and electronic waste (E-waste) allows for reusing the original materials rather than mining new ones, especially if they are considered Rare Earth Elements. It is critical to separate and sort E-waste before recycling, which can be economically viable for products containing valuable metals and materials. Identification of the device and all related information will greatly support pre-dismantling. This work proposes a pipeline and automated system to identify the device using several techniques: artificial intelligence for device classification into device type, brand and model, multi-sensors for identifying internal components like Infrared or X-ray, a web crawler for creating image datasets using keywords from search engines, and a web scraper for retrieving device specifications, like dimensions, battery type, visual data, available colours, etc. © 2023 IEEE.

关键词： Electronic Waste

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Optimizing Software Defect Classification with Locally Linear Embedding Techniques 71

Optimizing Software Defect Classification with Locally Linea...

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71st Annual Reliability and Maintainability Symposium, RAMS 2025

作者： Salboukh, Fatemeh Saki, Hesam Fiondella, Lance Department of Applied Science and Engineering University of Massachusetts - Dartmouth 285 Old Westport Road North DartmouthMA02747 United States Department of Computer Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Massachusetts - Dartmouth 285 Old Westport Road North DartmouthMA02747 United States

ISBN: (纸本)9798350367744

Software defect classification is crucial for enhancing the quality and reliability of software. This research explores the integration of Locally Linear Embedding (LLE) into the preprocessing stages of classification models to improve accuracy. LLE is particularly effective as it transforms complex, nonlinear data into a simpler, linear format. This transformation is instrumental in reducing the complexity of the data, facilitating a more straightforward analysis and potentially improving the predictive accuracy of classifiers. For this study, we used the KC2 dataset from the PROMISE repository and applied several feature selection techniques, including Correlation-Based Feature Selection, Gain Ratio, Sequential Forward Selection, and K-nearest neighbors. After selecting the most informative features, LLE was employed to linearize this data, aiming to enhance the classifiers’ performance by presenting them with data that is easier to interpret and analyze. The impact of LLE was evaluated by comparing the outcomes of five different classifiers, such as Random Forest, Gradient Boosting, Support Vector Machines, Non-Linear Support Vector Machines, and Naive Bayes, on both the original and the LLE-processed datasets. Our results demonstrated an average improvement of 4.5% in accuracy and 4% in F1-Score across the classifiers with the LLE-processed data, confirming the effectiveness of transforming nonlinear data into a linear space for enhancing software defect classification. © 2025 IEEE.

关键词： Software quality

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CosineRecom: A KNN-Based Movie Recommendation System Using Cosine Similarity 4

CosineRecom: A KNN-Based Movie Recommendation System Using C...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Singh, Mandeep Mishra, Prafull Aggarwal, Nitin Kumar, Vinay Sharma, Pramod Kumar Yadav, Ritesh Bennett University School of Computer Science Engineering and Technology Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Mfps Supervisor Oracle Development Database Admin Data Science PA United States Kiet Group of Institutions Department of Csit UP Ghaziabad India Kiet Group of Institute Department of Applied Science UP Ghaziabad India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350364729

Information overload is a major problem for many people who use the internet. This is because there is so much information available that it can be hard to know where to start. Filtering tools, like the Recommender System, are needed to solve this problem. These tools give users access to relevant information and let them customise their search results based on their liking. The Collaborative Filtering Recommendation System finds the group of active users who are your closest neighbours. This study shows many ways to use the supervised machine learning algorithm (KNN) method for the Movie recommendation System with different similarity measures, such as the cosine metric, Squared Error Similarity, Linear Correlation Coefficient, and Baseline Predictor Correlation. For the movie recommender system, these many changes to the KNN algorithm were tested on real data from the given dataset and compared based on accuracy measures such as the number of matching pairs and different types of error like mean absolute error, squared mean error, and squared root mean error. By making changes to the web browser, this recommendation system processing tool could be used as a substitute in the real world. © 2024 IEEE.

关键词： Collaborative filtering

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Prediction of room temperature in Trombe solar wall systems using machine learning algorithms

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Energy Storage and Saving 2024年第4期3卷 243-249页

作者： Seyed Hossein Hashemi Zahra Besharati Seyed Abdolrasoul Hashemi Seyed Ali Hashemi Aziz Babapoor Petroleum Systems Engineering Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSK S4S 0A2Canada Department of Chemical Engineering University of Mohaghegh ArdabiliArdabil 13131-56199Iran Department of Chemistry School of ScienceAlzahra UniversityTehran ***Iran Computer Engineering Gachsaran Oil and Gas Exploitation CompanyGachsaran ***Iran Architectural Engineering Shiraz Islamic Azad UniversityShiraz ***Iran

A Trombe wall-heating system is used to absorb solar energy to heat *** parameters affect the system performance for optimal *** study evaluated the performance of four machine learning algorithms—linear regression,k-nearest neighbors,random forest,and decision tree—for predicting the room temperature in a Trombe wall *** accuracy of the algorithms was assessed using R^(2)and root mean squared error(RMSE)*** results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance,with R^(2)and RMSE values of 1 and *** contrast,linear regression and decision tree showed weaker *** findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems,enabling informed design decisions to enhance energy efficiency.

关键词： Trombe wall Solar energy Thermal storage wall Machine learning algorithms

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Synergies of Federated Learning and Blockchain for Patient-Centric Sustainable Next Generation Healthcare through Distributed Medical Internet of Things 4

Synergies of Federated Learning and Blockchain for Patient-C...

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4th International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Manna, Sarbajit Sarkar, Arindam Khan, Mohammad Zubair Noor, Ayman Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Belur Math West Bengal Howrah711202 India Department of Computer Science and Information Taibah University Madinah42353 Saudi Arabia Department of Computer Science College of Computer Science and Engineering Taibah University Madinah42353 Saudi Arabia

ISBN: (纸本)9781839539954

This study investigates how blockchain and Federated Learning (FL) may work together to create a patient-centered, long-lasting healthcare ecosystem. The paper presents an innovative method for improving data privacy, security, and interoperability within healthcare systems by utilizing the distributed Medical Internet of Things (MIoT). The next generation of healthcare services will be revolutionized by the synergistic combination of FL and blockchain, enabling a decentralized framework for enhanced patient care and data-driven decision-making. Centralized Machine Learning (ML)-based methods lack security of patient’s sensitive medical information as all information of patient are trained in central server. To tackle with this problem, this paper offers amalgamation of FL and blockchain technology which enables training of ML models collectively by making use of distributed MIoT data, enhances the accuracy and also guarantees privacy of patient’s personal medical record. To preserve privacy over patient’s personal medical record, blockchain technology is used. The proposed approach offers a lot of benefits: (1) To predict disease more perfectly and effectively, this paper utilizes AdaBoost, Decision Tree (DT), Extra Tree Classifier (ETC), Linear Discriminant Analysis (LDA) and FL algorithm. (2) Each algorithm is tested against different data splitting, different cross validation and cross validation with feature selection techniques, hyperparameter tuning for model optimization and finding the best model. (3) The proposed methodology ensures collective learning among several healthcare organizations without sending patient’s private medical records and thus safeguarding privacy. (4) Accuracy, Markedness (MK), False Omission Rate (FOR), Negative Likelihood Ratio (NLR), Prevalence Threshold (PT) are calculated to choose the best algorithm and also to show how well the FL algorithm works. (5) The paper is unique as the best algorithm in terms of different model evalua

关键词： Federated learning

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Leveraging Information-Centric Edge Computing in Private 5G Cellular Networks via the W3C Web of Things 22

Leveraging Information-Centric Edge Computing in Private 5G ...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Romann, Jan Damer, Hugo Hakim Gias, T. M. Rayhan Bergmann, Olaf University of Applied Sciences Emden/Leer Faculty of Technology Department of Electrical Engineering and Computer Science Constantiaplatz 4 Emden26723 Germany

ISBN: (纸本)9798331508050

In this paper, we present an architecture for the deployment of an edge computing system based on Named Data Networking (NDN) as part of a private 5G network. Our proposed architecture integrates IP-based and non-IP-based components using the W3C Web of Things (WoT) standards, including a Thing Description Directory (TDD). Our results highlight the potential of both edge computing and WoT concepts for private 5G deployments but also reveal several standardization gaps that need to be closed to make effective use of the new technologies. © 2025 IEEE.

关键词： Internet of things

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Advanced Computational Modeling for Brain Tumor Detection:Enhancing Segmentation Accuracy Using ICA-Ⅰ and ICA-Ⅱ Techniques

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computer Modeling in engineering & sciences 2025年第4期143卷 255-287页

作者： Abdullah A Asiri Toufique A.Soomro Ahmed Ali Faisal Bin Ubaid Muhammad Irfan Khlood M.Mehdar Magbool Alelyani Mohammed S.Alshuhri Ahmad Joman Alghamdi Sultan Alamri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Artificial Intelligence and Cyber Futures Institute Charles UniversityBathurstNSW 2795Australia Department of Electronic Engineering The University of LarkanoLarkana75660Pakistan Electrical Engineering Department Sukkur IBA UniversitySukkur65200Pakistan Computer Science Department Sukkur IBA UniversitySukkur65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajran61441Saudi Arabia Department of Radiological Sciences College of Applied Medical ScienceKing Khalid UniversityGuraigerAbha62521Saudi Arabia Radiology andMedical Imaging Department College of AppliedMedical SciencesPrince Sattam bin Abdulaziz UniversityKharj11942Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesTaif UniversityTaif21944Saudi Arabia

Global mortality rates are greatly impacted by malignancies of the brain and nervous ***,Magnetic Resonance Imaging(MRI)plays a pivotal role in detecting brain tumors;however,manual assessment is time-consuming and susceptible to human *** address this,we introduce ICA2-SVM,an advanced computational framework integrating Independent Component Analysis Architecture-2(ICA2)and Support Vector Machine(SVM)for automated tumor segmentation and ***2 is utilized for image preprocessing and optimization,enhancing MRI consistency and *** Fast-MarchingMethod(FMM)is employed to delineate tumor regions,followed by SVM for precise *** on the Contrast-Enhanced Magnetic Resonance Imaging(CEMRI)dataset demonstrates the superior performance of ICA2-SVM,achieving a Dice Similarity Coefficient(DSC)of 0.974,accuracy of 0.992,specificity of 0.99,and sensitivity of ***,themodel surpasses existing approaches in computational efficiency,completing analysis within 0.41 *** integrating state-of-the-art computational techniques,ICA2-SVM advances biomedical imaging,offering a highly accurate and efficient solution for brain tumor *** research aims to incorporate multi-physics modeling and diverse classifiers to further enhance the adaptability and applicability of brain tumor diagnostic systems.

关键词： Brain image segmentation MR brain enhancement independent component analysis brain tumor

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An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

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computer Systems science & engineering 2023年第3期44卷 2457-2468页

作者： Mahesh B.Shelke Jeong Gon Lee Sovan Samanta Sachin N.Deshmukh G.Bhalke Daulappa Rahul B.Mannade Arun Kumar Sivaraman Department of Computer Science and Information Technology Dr.Babasaheb Ambedkar Marathwada UniversityAurangabadMaharashtra431004India Division of Applied Mathematics Wonkwang University460Iksan daeroIksan SiJeonbuk54538Korea Department of Mathematics Tamralipta MahavidyalayaTamlukWest Bengal721636India Department of Electronics and Telecommunication Engineering AISSMSCOEPuneMaharashtra411001India Department of Information Technology Govemment College of EngineeringAurangabadMaharashtra431005India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

In today’s digital world,millions of individuals are linked to one another via the Internet and social *** opens up new avenues for information exchange with *** analysis(SA)has gotten a lot of attention during the last *** analyse the challenges of Sentiment Analysis(SA)in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which wefirst produced an annotated dataset composed of Marathi text acquired from microblogging websites such as *** also choose domain experts to manually annotate Marathi microblogging posts with positive,negative,and neutral *** addition,to show the efficient use of the annotated dataset,an ensemble-based model for sentiment analysis was *** contrast to others machine learning classifier,we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation(cv),outcomes as 97.77%,f-score is 97.89%.

关键词： Sentiment analysis machine learning lexical resource ensemble classifier

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Complementary memtransistors for neuromorphic computing: How, what and why

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Journal of Semiconductors 2024年第6期45卷 64-80页

作者： Qi Chen Yue Zhou Weiwei Xiong Zirui Chen Yasai Wang Xiangshui Miao Yuhui He School of Integrated Circuits Huazhong University of Science and TechnologyWuhan 430074China Department of Electrical and Computer Engineering University of CaliforniaSan DiegoUSA Dipartimento di Elettronica e Informazione and *** Politecnico di MilanoMilanoItaly Department of Applied Physics Hong Kong Polytechnic UniversityHong KongChina

Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic *** the other side,it is known that the complementary metal-oxide-semiconductor(CMOS)field effect transistors have played the fundamental role in the modern integrated circuit ***,will complementary memtransistors(CMT)also play such a role in the future neuromorphic circuits and chips?In this review,various types of materials and physical mechanisms for constructing CMT(how)are inspected with their merits and need-to-address challenges *** the unique properties(what)and poten-tial applications of CMT in different learning algorithms/scenarios of spiking neural networks(why)are reviewed,including super-vised rule,reinforcement one,dynamic vision with in-sensor computing,*** exploiting the complementary structure-related novel functions,significant reduction of hardware consuming,enhancement of energy/efficiency ratio and other advan-tages have been gained,illustrating the alluring prospect of design technology co-optimization(DTCO)of CMT towards neuro-morphic computing.

关键词： complementary memtransistor neuromorphic computing reward-modulated spike timing-dependent plasticity remote supervise method in-sensor computing

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Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

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computer Modeling in engineering & sciences 2024年第12期141卷 2185-2223页

作者： Hamad Naeem Amjad Alsirhani Faeiz MAlserhani Farhan Ullah Ondrej Krejcar Faculty of Informatics and Management Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec Kralove50003Czech Republic Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Cybersecurity Center Prince Mohammad Bin Fahd UniversityAl JawharahKhobarDhahran34754Saudi Arabia

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network *** protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT *** study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data *** rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning *** using the BAT algorithm enhances dataset *** deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data *** predictions form a meta-feature set for a subsequent meta-learner,which combines model *** classifiers validate meta-learner features for broad algorithm *** comprehensive method demonstrates high accuracy and robustness in IoMT intrusion *** were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen *** results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

关键词： Cyberattack ensemble learning feature selection intrusion detection smart cities machine learning BAT augmentation

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