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Generative AI for Energy Harvesting Internet of Things Network: Fundamental, Applications, and Opportunities

IEEE Internet of Things Magazine 2025年第3期8卷 72-80页

作者： Xie, Wenwen Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Dobre, Octavia A. Jilin University China The College of Computing and Data Science Nanyang Technological University Singapore The University of Hong Kong China Memorial University The Faculty of Engineering and Applied Science Canada

Internet of Things (IoT) devices are typically powered by small-sized batteries with limited energy storage capacity, requiring regular replacement or recharging. To reduce costs and maintain connectivity in IoT networks, energy harvesting technologies are regarded as a promising solution. Notably, due to its robust analytical and generative capabilities, generative artificial intelligence (GenAl) has demonstrated significant potential in optimizing energy harvesting networks. Therefore, we discuss key applications of GenAl in improving energy harvesting IoT networks in this article. Specifically, we first review the key technologies of GenAI and the architecture of energy harvesting IoT networks. Then, we show how GenAI can address different problems to improve the performance of the energy harvesting IoT networks. Subsequently, we present a case study of unmanned aerial vehicle (UAV)-enabled data col-lection and energy transfer. The case study shows distinctively the necessity of energy harvesting technology and verify the effectiveness of GenAI-based methods. Finally, we discuss some import-ant open directions. © 2018 IEEE.

关键词： Costs Generative AI Reviews Energy exchange Data collection Autonomous aerial vehicles Batteries Energy harvesting Internet of Things Battery management systems Energy storage

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A Dyad-Based Mobile System to Support Older Adults and Informal Caregivers 14

A Dyad-Based Mobile System to Support Older Adults and Infor...

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2024 IEEE 14th Annual computing and Communication Workshop and Conference, CCWC 2024

作者： El Salti, Tarek Sykes, Edward R. Scrivo, Jordan Plaza, Brandon Mun, Vladislav Faculty of Applied Science and Technology Sheridan College School of Applied Computing OakvilleON Canada OakvilleON Canada

ISBN: (纸本)9798350360134

Despite a gradual rise in health care expenditures due to population aging between the Year 2020 and 2060 in several countries, the overall increase will contribute to additional GDP share (e.g., 1.3 percentage points in the EU). To potentially reduce costs, ongoing developments of mobile Health (mHealth) applications is essential which provide digital health care using mobile devices. However, there is no dyadic mHealth architecture (i.e., between caregivers and their older adults) exclusively utilizing smartphones, aimed at supporting older adults' well-being through regular follow-ups for their caregivers. In response, we developed a novel dyad-based architecture, including mobile applications for caregivers to check on their older adults' physical activities (e.g., walking). Our system is not only considered mobile but also highly accessible due to the pervasiveness of smartphones. This work in progress also included pilot usability study analysis for the user interface and experience design. Among the findings, participants mentioned that the proposed system strongly supports them in their caregiving for their older adults. This study will serve as a precursor for future usability studies that will include wearables. © 2024 IEEE.

关键词： Smartphones

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Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

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Computers, Materials & Continua 2025年第2期82卷 2931-2949页

作者： Hairani Hairani Triyanna Widiyaningtyas Didik Dwi Prasetya Afrig Aminuddin Department of Electrical Engineering and Informatics Faculty of EngineeringUniversitas Negeri MalangMalang65145Indonesia Department of Computer Science Universitas BumigoraMataram83127Indonesia Department of Computer Graphic and Multimedia Faculty of ComputingCollege of Computing and Applied SciencesUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia

An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t

关键词： SMOTE modification Clustering-SMOTE manhattan distance

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An efficient feature selection and classification system for microarray cancer data using genetic algorithm and deep belief networks

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Multimedia Tools and Applications 2025年第8期84卷 4393-4434页

作者： Lawrence, Morolake Oladayo Jimoh, Rasheed Gbenga Yahya, Waheed Babatunde Department of Computer Science Faculty of Computing & Applied Sciences Baze University Abuja Nigeria Department of Computer Science Faculty of Communication & Information Sciences University of Ilorin Kwara State Ilorin Nigeria Department of Statistics Faculty of Physical Sciences University of Ilorin Kwara State Ilorin Nigeria

Cancer is one of the most devastating health conditions in the world. In the diagnosis and treatment of the various forms of cancer illness, studies have shown that early detection of the cancer by clinical methods usually takes a considerable lengthy time. This informs searching for an alternative non-clinical diagnosis of cancer cases using microarray technology. Therefore, this study develops an efficient feature selection and classification method for high-dimensional microarray cancer data by combining Genetic Algorithms (GA) and Deep Belief Networks (DBN). The study employed a GA for selecting the most informative gene biomarker and DBN for the classification of biological samples. In a Monte Carlo experiment, the simulated and real-life microarray datasets were partitioned into 95% training and 5% test samples at 100 to 1000 epochs. The classifier was constructed using the training datasets while its efficiency was assessed on the test sample using the Misclassification Error Rate, Sensitivity, Specificity, and Receiver Operating Characteristic Analysis. The GA and DBN were implemented using Caret and Deepnet (R statistical packages). The proposed GADBN method was implemented using simulated and real-life datasets, which yielded out-of-bag average classification accuracy of 98.8% and 93.1% respectively. The proposed GADBN outperformed the other existing classifiers;and the GADBN method was more efficient with a smaller Misclassification Error Rate (MER) for Leukaemia 1 (0.18), Prostate 1 (0.35) and Prostate 3 (0.09) datasets than some of the existing methods under various performance indices considered. The proposed model is a powerful and effective instrument for identifying useful features in microarray cancer data and classifying the cancer types accordingly. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

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Data Intelligence 2024年第2期6卷 320-343页

作者： Sergei Romanov Abel Soares Siqueira Jonathan de Bruin Jelle Teijema Laura Hofstee Rens van de Schoot Applied Data Science Department of Information and Computing ScienceFaculty of ScienceUtrecht University Netherlands eScienceCenter AmsterdamNL Department of Research and Data Management Services Information Technology ServicesUtrecht UniversityUtrechtthe Netherlands Department of Methodology and Statistics Faculty of Social and Behavioral SciencesUtrecht University

Active learning can be used for optimizing and speeding up the screening phase of systematic *** simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training *** paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud *** provide a technical explanation of the proposed cloud architecture and its *** addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM *** analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing *** parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

关键词： Active learning Systematic review Simulation study Multiprocessing Cloud architecture

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Whale Optimization Algorithm Strategies for Higher Interaction Strength T-Way Testing

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Computers, Materials & Continua 2022年第10期73卷 2057-2077页

作者： Ali Abdullah Hassan Salwani Abdullah Kamal Z.Zamli Rozilawati Razali Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Computing College of Computing and Applied SciencesUniversiti Malaysia PahangPekan26600PahangMalaysia

Much of our daily tasks have been computerized by machines and sensors communicating with each other in *** is a reasonable risk that something could go wrong because there are a lot of sensors producing a lot of *** testing(CT)can be used in this case to reduce risks and ensure conformance to *** existing metaheuristic-based solutions aim to assist the test suite generation for combinatorial testing,also known as t-way testing(where t indicates the interaction strength),viewed as an optimization *** previous research,while helpful,only investigated a small number of interaction strengths up to t=*** lightweight applications,research has demonstrated good fault-finding ***,the number of interaction strengths considered must be higher in the case of interactions that generate large amounts of *** to resource restrictions and the combinatorial explosion challenge,little work has been done to produce high-order interaction *** this context,the Whale Optimization Algorithm(WOA)is proposed to generate high-order interaction *** ensure that WOA conquers premature convergence and avoids local optima for large search spaces(owing to high-order interaction),three variants of WOA have been developed,namely Structurally Modified Whale Optimization Algorithm(SWOA),Tolerance Whale Optimization Algorithm(TWOA),and Tolerance Structurally Modified Whale Optimization Algorithm(TSWOA).Our experiments show that the third strategy gives the best performance and is comparable to existing state-of-thearts based strategies.

关键词： Software testing optimization problem swarm intelligence algorithm combinatorial optimization IoT

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ISoTU: Enhancing Campus Connectivity through a Mobile-Based Social Network Application 1

ISoTU: Enhancing Campus Connectivity through a Mobile-Based ...

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1st International Conference on Innovative Engineering sciences and Technological Research, ICIESTR 2024

作者： Maulid, Hariandi Gozali, Alfian Akbar Hidayatulloh, Indra Kurniawan, Rahmad Satria Miftafarid, Danah Telkom University Faculty of Applied Science Bandung Indonesia Glasgow Caledonian University Department of Computing Glasgow United Kingdom

ISBN: (纸本)9798350348637

In contemporary educational settings, fostering effective social interaction within campus communities poses a significant challenge. The need for a platform that facilitates meaningful connections and communication among students and faculty is increasingly evident, especially in large university environments where personal interactions can often be *** this need, the paper presents "iSoTU,"a mobile-based social network application designed for the Intelligent Campus initiative. iSoTU aims to bridge the gap in campus connectivity by offering features for interest-based networking, streamlined communication, and sharing of campus events and activities, all tailored specifically for the university *** application underwent rigorous testing, including the alpha and beta phases. Alpha testing, which included sanity checks, core apps quality assessments, and code quality testing, revealed that iSoTU functions efficiently, with a minor security issue related to multi-device login capabilities due to Firebase's policies. Importantly, beta testing showed that iSoTU effectively facilitates student interactions. It aids in finding new friends, enhancing student communication, and encouraging the sharing of activities. A significant outcome was that 84.14% of users strongly agreed that iSoTU met their expectations, underscoring its potential as a valuable tool for enhancing campus connectivity. © 2024 IEEE.

关键词： Educational Technology Intelligent Campus Mobile Application Social Community Social Network

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An EfficientNet to Classify Monkeypox-Comparable Skin Lesions Using Transfer Learning

An EfficientNet to Classify Monkeypox-Comparable Skin Lesion...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Hossen, Md. Rifat Alfaz, Nazia Sami, Adnan Tanim, Sharia Arfin Bin Sarwar, Talha Islam, Md. Kamrul Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science Dhaka Bangladesh College of Computing and Applied Science Universiti Malaysia Pahang Faculty of Computing Pekan Malaysia

ISBN: (纸本)9798350346473

Monkeypox is an infectious illness caused by the DNA-based monkeypox virus, which has raised public health concerns due to its rapid transmission to over 50 countries. Direct physical interaction with infected humans or infected animals is the main reason behind the spread of this virus. The appearance of skin problems such as smallpox and rashes are the most frequently reported symptoms of this virus. Since cases of monkeypox are increasing rapidly around the world, stopping the spread of this zoonosis by providing early diagnosis and treatment is crucial before the emergence of a pandemic similar to COVID-19. In this study, we aim to propose a transfer learning-based approach using the EfficientNet-B0 architecture to identify monkeypox subjects by using skin lesion image samples. However, distinguishing monkeypox from other comparable infectious skin illnesses like chickenpox and measles is challenging. Therefore, additionally, this study identifies other diseases that also cause blisters and rashes on the skin, like chickenpox, and measles. During the data distribution phase, 5-fold cross-validation is used to validate the work's reliability by assuring that every sample is utilized for training and validation. For the evaluation of the model's classification performance, accuracy and loss are recorded for each training epoch. Moreover, precision, recall, F1-score, and confusion matrix are generated upon completion of the model training. This proposed approach is experimented on a public dataset and has shown remarkable performance by providing an overall 96.53% classification accuracy, 96.57% precision, 96.53% recall, and 96.52% F1-score. © 2023 IEEE.

关键词： Viruses

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Existence of three solutions to a p(z)-Laplacian-Like Robin problem

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Annali dell'Universita di Ferrara 2024年第4期70卷 1375-1388页

作者： El Ouaarabi, Mohamed Moujane, Noureddine Melliani, Said Fundamental and Applied Mathematics Laboratory Faculty of Sciences Aïn Chock Hassan II University Casablanca 20100 Morocco Applied Mathematics and Scientific Computing Laboratory Faculty of Science and Technics Sultan Moulay Slimane University Beni Mellal 23000 Morocco

This paper deals with the existence of solutions for a Robin boundary problem involving the p(z)-Laplacian-Like operator. Using Ricceri’s variational method, we prove the existence result of at least three nontrivial solutions of the considered problem in the framework of double weighted generalized Sobolev space. © The Author(s) under exclusive license to Università degli Studi di Ferrara 2024.

关键词： Existence p(z)-Laplacian-Like operator Partial differential equations Ricceri’s variational methods Robin boundary problem Weight function

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Inquiry-Based Education Technological Application Model in Distance Learning 24

Inquiry-Based Education Technological Application Model in D...

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2024 10th International Conference on Education and Training Technologies, ICETT 2024

作者： Lam, Ming Sum Shao, Jiahui Zhou, Ruizhe Yau, Peter Chunyu Wong, Dennis Clixells Hong Kong Faculty of Applied Sciences Macao Polytechnic University China School of Computing Science University of Glasgow United Kingdom

ISBN: (纸本)9798400717895

The teaching strategy has been continuously improved, and the traditional direct teaching method has gradually shifted towards student-centered learning method, which place the focus of the classroom on the student. Inquiry based learning is one of the approaches in student-centered learning, which could be divided into 5 phases: Engagement, Exploration, Explanation, Elaboration and Evaluation. Students construct their knowledge by repeating these five steps through conducting experiments, observation and making hypotheses. This paper proposed methods to combine technology with different innovative approaches at each phase in inquiry-based learning to facilitate the teaching and learning process in the lesson. © 2024 ACM.

关键词： Contrastive Learning

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