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Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

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BMEMat(BioMedical engineering Materials) 2025年第1期3卷 158-181页

作者： Suparna Das Hirak Mazumdar Kamil Reza Khondakar Ajeet Kaushik Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for WomenHyderabadIndia School of Engineering and Technology Department of Computer Science and EngineeringAdamas UniversityKolkataIndia School of Technology Woxsen UniversityHyderabadIndia NanoBiotech Laboratory Department of Environmental EngineeringFlorida Polytechnic UniversityLakelandUSA

Machine learning(ML)and nanotechnology interfacing are exploring opportunities for cancer treatment *** improve cancer therapy,this article investigates the synergistic combination of Graphene Oxide(GO)‐based devices with ML *** production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the *** is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal ***,complicated biological data are analyzed using ML algorithms,which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular *** study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using *** modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery ***,tracking treatment outcomes in real time is made possible by ML algorithms,which permit adaptive modifications to therapy *** optimizing medication doses and delivery settings,the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy.

关键词： artificial intelligence cancer diagnosis graphene oxide machine learning real‐time monitoring sensing

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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UAV-Assisted Multi-Object Computing Offloading for Blockchain-Enabled Vehicle-to-Everything Systems

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computers, Materials & Continua 2024年第12期81卷 3927-3950页

作者： Ting Chen Shujiao Wang Xin Fan Xiujuan Zhang Chuanwen Luo Yi Hong School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer Science Qufu Normal UniversityRizhao276826China School of Information Science and Technology Beijing Forestry UniversityBeijing100083China

This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)*** to the presence of an eavesdropper(Eve),the system’s com-munication links may be *** paper proposes deploying an intelligent reflecting surface(IRS)on the UAV to enhance the communication performance of mobile vehicles,improve system flexibility,and alleviate eavesdropping on communication *** links for uploading task data from vehicles to a base station(BS)are protected by IRS-assisted physical layer security(PLS).Upon receiving task data,the computing resources provided by the edge computing servers(MEC)are allocated to vehicles for task *** blockchain-based computation offloading schemes typically focus on improving network performance,such as minimizing energy consumption or latency,while neglecting the Gas fees for computation offloading and the costs required for MEC computation,leading to an imbalance between service fees and resource *** paper uses a utility-oriented computation offloading scheme to balance costs and *** paper proposes alternating phase optimization and power optimization to optimize the energy consumption,latency,and communication secrecy rate,thereby maximizing the weighted total utility of the *** results demonstrate a notable enhancement in the weighted total system utility and resource utilization,thereby corroborating the viability of our approach for practical applications.

关键词： UAV intelligent reflecting surface vehicle to everything task offloading phase shift optimization

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Microstructure and magnetocaloric properties of melt-extracted SmGdDyCoAl high-entropy amorphous microwires

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Rare Metals 2024年第3期43卷 1234-1242页

作者： Shi-Jie Wei Hong-Xian Shen Lun-Yong Zhang Lin Luo Xin-Xing Tang Jian-Fei Sun Xiao-Qing Li School of Materials Science and Engineering Harbin Institute of TechnologyHarbin150001China Applied Materials Physics Department of Materials Science and EngineeringKTH-Royal Institute of Technology10044StockholmSweden

This paper presents a systematic investigation of the microstructure and magnetocaloric properties of melt-extracted Sm_(20)Gd_(20)Dy_(20)Co_(20)Al_(20)high-entropy *** fabricated wires exhibited an amorphous structure,and the temperature interval of the undercooled liquid AT was 45 *** microwires underwent a second-order magnetic transition from a ferromagnetic to a paramagnetic state near the Curie temperature(T_(C)=52 K),The maximum magnetic entropy change(-ΔS_M^(max)),the relative cooling power and the refrigeration capacity reached 6.34 J·kg^(-1)·K^(-1).422.09 J·kg^(-1)and 332.94 J·kg^(-1),respectively,under a magnetic field change of 5 *** addition,the temperature-averaged entropy changes with two temperature lifts(3 and 10 K)were 6.32 and 6.27 J·kg^(-1)·K^(-1),*** good magnetocalorie performance highlights the significant potential for the Sm_(20)Gd_(20)Dy_(20)Co_(20)Al_(20)microwires to be used as magnetic refrigerant materials in low-temperature region *** work will serve as a valuable reference for future investigations on low-temperature high-entropy magnetocaloric materials.

关键词： Melt-extraction High-entropy amorphous microwires Magnetocaloric effect Magnetic refrigeration

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A comprehensive systematic review of machine learning in the retail industry: classifications, limitations, opportunities, and challenges

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Neural Computing and Applications 2024年第4期37卷 2035-2070页

作者： Hassan, Dler O. Hassan, Bryar A. Department of Computer Science College of Science Charmo University Kurdistan Region Chamchamal Sulaimani46023 Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Erbil Iraq

Machine learning has profoundly transformed various industries, notably revolutionizing the retail sector through diverse applications that significantly enhance operational efficiency and performance. This comprehensive review examines the state-of-the-art machine learning applications in the retail sector from 2019 to 2024, focusing on supervised learning, unsupervised learning, and ensemble methods. It aims to identify and categorize recent machine learning applications in retail, evaluate the performance of machine learning algorithms, and determine the most suitable algorithms for specific retail use cases. This review article examines 56 studies and identifies 20 unique machine learning applications within the retail sector. This review also discusses the challenges and opportunities of implementing machine learning in retail, offering valuable insights to guide future research and enhance retail performance and customer satisfaction. The findings highlight the strengths and limitations of different machine learning methods, providing insights into their practical applications and future potential. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Ensemble methods Machine learning Retail Supervised learning Unsupervised learning

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Unraveling electrolyte solvation architectures for high-performance lithium-ion batteries

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science China(Technological sciences) 2024年第3期67卷 958-964页

作者： YANG MengHao SHI Zhe HE ZhiYuan WANG Dan School of Materials Science and Engineering Tongji UniversityShanghai 201804China Department of Nuclear Science and Engineering Massachusetts Institute of Technology CambridgeMassachuseis 02139USA School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081China

The design of advanced electrolytes hinges critically on a comprehensive comprehension of lithium-ion migration mechanisms within these electrochemical systems. Fluorination generally improves the stability and reduces the reactivity of organic compounds, making them potentially suitable for use in harsh conditions such as those found in a battery electrolyte. However,the specific properties, such as the solvation power, diffusivity, ion mobility, and so forth, would depend on the exact nature and extent of the fluorination. In this work, we introduce a theoretical framework designed to facilitate the autonomous creation of electrolyte molecular structures and craft methodologies to compute transport coefficients, providing a physical interpretation of fluoride systems. Taking fluorinated-1,2-diethoxyethanes as electrolyte solvents, we present and analyze the relationship between the electronic properties and atomic structures, and further correlate these properties to the transport coefficients, resulting in a good alignment with the experimental diffusion behaviors and Li-solvation structures. The insights derived from this research contribute to the methodological basis for high-throughput evaluation of prospective electrolyte systems, and consequently, propose strategic directions for the improvement of electrochemical cycle characteristics. This comprehensive exploration of the transport mechanisms enhances our understanding, offering avenues for further advancements in the field of lithium-ion battery technology.

关键词： Unraveling electrolyte solvation architectures for high-performance lithium-ion batteries

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Real-Time Challenges and Opportunities for an Effective Resource Management in Multi-cloud Environment

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SN computer science 2024年第2期5卷 1-8页

作者： Basha, H. Anwar Anilkumar, B.H. Swetha, G. Reddy, Raghavendra Manoli, Sunil Department of Computer Science and Engineering Rajalakshmi Institute of Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India R R Institute of Technology Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India

As the usage of multi-cloud setups grows, resource management will become a major concern. Because of the dynamic nature of these environments, as well as fluctuating workloads and service-level targets, an effective algorithm for real-time resource allocation and optimisation is required. The Adaptive Multi-Cloud Resource Management Algorithm (AMCRMA) is proposed in this study to handle the obstacles and capitalise on the benefits of resource management in a multi-cloud context. To ensure optimal resource utilisation, cost effectiveness, and service performance, AMCRMA incorporates real-time monitoring, workload prediction, and intelligent resource allocation. Experiment results show that AMCRMA is successful in adapting to changing situations and managing resources in real time, resulting in enhanced resource utilisation and overall performance in multi-cloud systems. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： AMCRMA Cloud federation Cloud orchestration Data management Intelligent resource allocation Resource management

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Contactless and Real-time Hand Gesture Recognition using Inductive Proximity Technique for Wrist-worn Wearables

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IEEE Sensors Journal 2025年第11期25卷 20474-20485页

作者： Lin, Pei-Jung Shih, Chi-Huang Weng, Tzu-Hsuan Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung411 Taiwan

This study proposes a contactless and real-time hand gesture recognition system suitable for smartwatches. The proposed system adopts inductive proximity sensing to collect Mechanomyography (MMG) signals induced by finger-based gestures above the wrist. After the signal processing and feature extraction stages, machine-learning models can be built for gesture classification. Compared with the Electromyography (EMG)-based method generally deployed in the forearm, inductive MMG applies an electromagnetic field to the body surface and is suitable for use on the wrist. Compared to the existing contact methods, such as EMG and accelerometer-based MMG, the inductive MMG method does not require contact with the body surface to collect muscle action signals and can avoid interference from environmental factors such as sweat and dust. The main contributions of this study are (1) a watch-type prototype with inductive MMG sensing technique, including hardware and firmware;(2) a lightweight and efficient signal processing mechanism that can capture the inductive signal characteristics of gestures for the above-mentioned embedded system (i.e., watch-type prototype);and (3) the tempo-spatial feature extraction method to improve the accuracy of gesture recognition. The experiment results show that for six common machine learning models, the gesture recognition accuracy of the proposed system is above 95%, with a maximum of 97.47%. In the briefing control application for verification purposes, the average accuracy of the inductive gesture recognition system can achieve 98.26%, and the average processing time is 14.37 ms. According to the experimental results, the inductive MMG system can provide a feasible gesture recognition solution for wrist-worn wearable devices. © 2001-2012 IEEE.

关键词： Firmware

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Cloud computing security: a taxonomy, threat detection and mitigation techniques

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International Journal of computers and Applications 2024年第5期46卷 348-361页

作者： Rani, Pinki Singh, Sukhdip Singh, Karanbir Computer Science and Engineering Department Deenbandhu Chhotu Ram University of Science and Technology Sonipat India Computer Engineering Department Seth Jai Parkash Polytechnic Yamuna Nagar India

Cloud Computing is a rapidly growing emerging technology in the IT environment. Internet-based computing provides services like sharing resources e.g. network, storage, applications and software through the Internet. Cloud customers can access these services according to their need and pay only for those services which they utilize. Almost every industrial sector like banking, retail, education, healthcare, manufacturing and businesses is adopting cloud techniques within their enterprise either privately or in sharing with other enterprises. However, many security issues must be resolved because they are a barrier in adopting this fast-growing technology. This paper reviews various threats on cloud computing like distributed denial of service attack, spoofing attacks (e.g. ARP Spoofing, IP Spoofing, DNS Spoofing), man-in-the middle attack, data loss, data breach, reused IP address attack, malicious insiders, insecure interfaces & APIs etc. These security issues prevent users from adopting cloud services. This study presents various detection and prevention methods to address cloud security concerns. The effectiveness of each strategy is then analyzed based on its attributes, benefits and drawbacks. Deep learning and machine learning algorithms produce more precise findings for both known and unidentified attack patterns. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： MapReduce

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Secure SSI framework using the optimized Orkey identity schema

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International Journal of Information technology (Singapore) 2024年第6期16卷 4007-4014页

作者： Pandu N, K. Kumar, A. Sasi Department of Computer Science and Engineering Srinivas University Institute of Engineering and Technology Srinivas University Department of Computer Science amd Engineering Yenepoya Institute of Technology VTU

In the era of digital transformation and increasing concerns regarding data privacy, the concept of Self-Sovereign Identity (SSI) has attained substantial recognization. SSI offers individuals greater control over their personal information and reduces reliance on centralized authorities for identity verification. However, existing SSI frameworks face challenges in terms of scalability, privacy, and competence. This paper introduces a noivel Secure SSI Framework based on the Optimized OrKey Identity Schema (OIS) for Inter-organizational business processes (IOBP). The Optimized OrKey Identity Schema optimizes data storage and retrieval, reducing the computational overhead associated with identity verification. The pigeon's communicative and mimicry behavior of pigeon-inspired optimization (PIO) as well as the flamingo's foraging and migratory patterns of the Flamingo Search Algorithm (FSA) served as the basis for the proposed Flamieon-inspired optimization method. A Verifiable Data Credential is introduced to combine the elements of verifiable credentials and the Blockchain method to improve the security and trustworthiness of sharing data and verification in a decentralized manner. The proposed OIS methods achieve a value of 4.00% for Anonymization, 9989.57 for Gas ratio, 7445.70 KB for Memory usage, 17.32% for Network hash rate, 5.07 ms for Transaction latency, and 41,234.82 ms for Transaction Time. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： BC technology Flamieon-inspired optimization OrKey identity schema Self-sovereign identity Verifiable data credential

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