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arXiv 2021年

作者： Nikoletseas, Sotiris Raptis, Theofanis P. Raptopoulos, Christoforos Institute of Informatics and Telematics National Research Council Pisa Italy Department of Computer Engineering and Informatics University of Patras Patras Greece Computer Technology Institute and Press "Diophantus" Patras Greece

Wireless energy transfer is an emerging technology that is used in networks of battery-powered devices in order to deliver energy and keep the network functional. Existing state-of-the-art studies have mainly focused on applying this technology on networks of relatively strong computational and communicational capabilities (wireless sensor networks, ad-hoc networks);also they assume energy transfer from special chargers to regular network nodes. Different from these works, we study how to efficiently transfer energy wirelessly in populations of battery-limited devices, towards prolonging their lifetime. In contrast to the state-of-the-art, we assume a much weaker population of distributed devices which are exchanging energy in a "peer to peer" manner with each other, without any special charger nodes. We address a quite general case of diverse energy levels and priorities in the network and study the problem of how the system can efficiently reach a weighted energy balance state distributively, under both loss-less and lossy power transfer assumptions. Three protocols are designed, analyzed and evaluated, achieving different performance trade-offs between energy balance quality, convergence time and energy efficiency. © 2021, CC BY-NC-ND.

关键词： Energy balance

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Predicting Carpark Prices Indices in Hong Kong Using AutoML

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computer Modeling in engineering & Sciences 2023年第3期134卷 2247-2282页

作者： Rita YiMan Li Lingxi Song Bo Li M.James C.Crabbe Xiao-Guang Yue Sustainable Real Estate Research Center Hong Kong Shue Yan UniversityHong Kong999077China Chakrabongse Bhuvanarth International Institute for Interdisciplinary Studies Rajamangala University of Technology Tawan-OkBangkok10400Thailand Zhongyuan Region Company Jinke Property Group Co.Ltd.Zhengzhou450000China Wolfson College Oxford UniversityOxfordOX26UDUK Institute of Biomedical and Environmental Science&Technology University of BedfordshireLutonLU13JUUK School of Life Sciences Shanxi UniversityTaiyuan030006China Department of Computer Science and Engineering European University CyprusNicosia1516Cyprus

The aims of this study were threefold:1)study the research gap in carpark and price index via big data and natural language processing,2)examine the research gap of carpark indices,and 3)construct carpark price indices via repeat sales methods and predict carpark indices via the *** researching the keyword“carpark”in Google Scholar,the largest electronic academic database that coversWeb of Science and Scopus indexed articles,this study obtained 999 articles and book chapters from 1910 to *** confirmed that most carpark research threw light on multi-storey carparks,management and ventilation systems,and reinforced concrete *** most common research method was case *** price index research,many previous studies focused on consumer,stock,press and futures,with many keywords being related to finance and *** indicated that there is no research predicting carpark price indices based on an AutoML *** study constructed repeat sales indices for 18 districts in Hong Kong by using 34,562 carpark transaction records from December 2009 to June ***’s carpark price was about four times that of Yuen Long’s carpark price,indicating the considerable carpark price differences inHong *** research evidenced the features that affected the carpark price indices models most:gold price ranked the first in all 19 models;oil price or Link stock price ranked second depending on the district,and carpark affordability ranked third.

关键词： Carpark repeat sales index AutoML Hong Kong natural language processing tokenization

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ML_BrainDetection: An Intelligent Model for Brain Tumor Identification Using Machine Learning

ML_BrainDetection: An Intelligent Model for Brain Tumor Iden...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Mohamed S Farag Ibrahim A Gomaa Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Brain tumors are one of the greatest causes of death worldwide. Due to that, early diagnosis and classification of the tumor would surely help increase the chance of survival for patients worldwide. However, classifying and identifying brain tumors requires certain tools, including using Magnetic Resonance Imaging (MRI) to detect brain tumors. Moreover, many researchers in the past have also built up computer-Aided Diagnosis (CAD) systems to aid radiologists in their purpose of detecting brain abnormalities and tumors effectively. In this research, extracted multi-modal features like texture, morphological, entropy-based, Scale Invariant Features are proposed, and consequently, certain robust machine learning techniques, which are k-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes, as well as the Logistic Regression classifier were used to analyze the two data-sets included in the paper, detect tumors found in the brain, not to mention, examining their results, which have then shown the highest accuracy when using the Logistic regression classifier in both data sets, with the Support Vector Machine (SVM) classifier following it, which further support the argument made in the paper.

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%; Naive Bayes: 83%.

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AI in Industry: Real-World Applications and Case Studies

TechRxiv

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TechRxiv 2023年

作者： Rahmaniar, Wahyu Maarif, Alfian ul Haq, Qazi Mazhar Iskandar, Muchammad Edo Institute of Innovative Research Tokyo Institute of Technology Kanagawa226-8503 Japan Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55166 Indonesia International Bachelor Program in Informatics and Computer Science Yuan Ze University Taoyuan320315 Taiwan Ezy Industries Jakarta Indonesia

Artificial intelligence (AI) has advanced rapidly and is becoming a cornerstone technology that drives innovation and efficiency in various industries. This paper examines the real-world application of AI in multiple sectors, including healthcare, finance, agriculture, retail, energy, and automotive. Several case studies are described to understand better the practical applications, results, and challenges of implementing AI. While many industries have reaped enormous benefits from AI, inherent challenges include data privacy, the potential for bias, and the continuing demand for skilled labor. This comprehensive review aims to provide AI application insights to professionals and researchers. Thus, as AI grows, there may be challenges and avenues for future research. © 2023, CC BY.

关键词： Agriculture

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia computer Science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods

SKIN_ML: An Efficient Approach for Skin Cancer Detection Usi...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier.

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AI based Technologies for International Space Station and Space Data 11

AI based Technologies for International Space Station and Sp...

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11th International Conference on System Modeling and Advancement in research Trends, SMART 2022

作者： Pant, Piyush Rajawat, Anand Singh Goyal, S.B. Potgantwar, Amol Bedi, Pradeep Raboaca, Maria Simona Constantin, Neagu Bogdan Verma, Chaman School of Computer Sciences and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Sandip Institute of Technology and Research Center Sandip University Nashik India Galgotias University Deapartment of Cse Greater Noida India National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania University of Iasi Department of Power Engineering Gheorghe Asachi' Technical Iasi700050 Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

Billions of galaxies, stars, solar systems, planets, and other undiscovered mysterious objects are present in the continuously expanding space. Humans took a giant step forward when the International Space Station was deployed into Earth's lower orbit for a better understanding of space. This research focuses of the Artificial Intelligence based technologies that are deployed in the International Space Station and potential models. The study proposes some Machine Learning models and technologies that could be deployed in the International Space Station to increase its efficiency and provide security to the crew. Powerful and trending Machine Learning/Deep Learning Algorithms like ANN and Clustering algorithms are suggested by the paper to get insights from the data gathered from the space and to promote the Industry Automation. A detailed explanation of the requirement, operation, and construction of NASA's 'Robonauts' designed for the International Space Station is discussed in the research. The paper also put light on the ATLAS, an asteroid detecting system. The methods for providing medical aid to the crew, debris and its influence, analyzing data and extracting insight from space research data using machine learning are also highlighted. Investigation on how technologies used in space exploration could be used in the ISS to improve its performance and an overview of some of the existing AI-based technologies deployed in the International Space Station (ISS) is discussed. © 2022 IEEE.

关键词： Machine learning

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Correction to: Concept Design Evaluation of Sustainable Product–Service Systems: A QFD–TOPSIS Integrated Framework with Basic Uncertain Linguistic Information

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Group Decision and Negotiation 2024年第3期33卷 513-514页

作者： Yang, Qiang Chen, Zhen-Song Zhu, Jiang-Hong Martínez, Luis Pedrycz, Witold Skibniewski, Mirosław J. School of Economics and Management Chongqing Jiaotong University Chongqing China Research Center of Integrated Customs-Port Logistics & Shipping Development Chongqing China School of Civil Engineering Wuhan University Wuhan China College of Traffic and Transportation Chongqing Jiaotong University Chongqing China Department of Computer Science University of Jaén Jaén Spain Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Civil and Environmental Engineering University of Maryland College Park USA Chaoyang University of Technology Taichung Taiwan Institute for Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Ibrahim Abd Elatif Gomaa Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%.

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