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Millimeter Wave Massive MIMO Heterogeneous Networks Using Fuzzy-Based Deep Convolutional Neural Network (FDCNN)

Intelligent Automation & Soft Computing 2023年第4期36卷 633-646页

作者： Hussain Alaaedi Masoud Sabaei Department of Computer Engineering at Computer Networks and Architecture Amirkabir University of TechnologyTehran***Iran

Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented *** narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile *** this research work,the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is *** high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output(MIMO)which is utilized in a hyperdense environment called heterogeneous networks(HetNet).The optimization problem which arises while maximizing the Mean Opinion Score(MOS)is analyzed along with the QoE(Quality of Experience)metric by considering the Base Station(BS)powers in addition to the needed Quality of Service(QoS).Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic *** a deep reinforcement learning framework is developed for tackling the same opti-mization *** this work,a Fuzzy-based Deep Convolutional Neural Net-work(FDCNN)is proposed in addition to a Deep Reinforcing Learning Framework(DRLF)for extracting the features of highly correlated *** investigational results prove that the proposed method yields the highest satisfac-tion to the user by increasing the number of antennas in addition with the small-scale antennas at the base *** proposed work outperforms in terms of MOS with multiple antennas.

关键词： Multiple-input and multiple-output quality of experience quality of service(qos) fuzzy-based deep convolutional neural network

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Application of machine learning techniques to predict patient’s satisfaction of indoor environmental quality in Jordanian hospitals

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13673-13681页

作者： Ali, Hikmat H. Abdullah, Malak Wedyan, Musab Department of Architecture Jordan University of Science and Technology Irbid Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan

The indoor environment is an integral part of the hospital design since it impacts patients’ health, well-being, and healing process. Although the machine learning approach has been widely adopted in many fields, limited studies applied the machine learning approach to indoor environmental quality (IEQ) in hospitals and the impact of the room type on patients’ satisfaction with IEQ. Accordingly, the current study aims to bridge this gap using the machine learning approach. The research used mixed design methods to assess indoor environmental quality. The data was gathered using self-reported data and field monitoring of environmental parameters inside patients’ rooms. King Abdullah University Hospital (KAUH) was used to represent hospitals in Jordan. Machine learning proceeded in several stages, starting from prepossessing, training the algorithms, and testing the results. The experiments were conducted with the same dataset for training and testing and evaluated using the same classification metrics using Python programming language. Besides, the most important features were conducted using Random Forest regardless of room type, in each room type, and each category of IEQ between room types. The present findings confirmed a variation in the essential features. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Hospitals

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EarLock: Personal Authentication System for Hearables Using Sound Leakage Signals

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6183-6196页

作者： Amesaka, Takashi Watanabe, Hiroki Sugiura, Yuta Sugimoto, Masanori Shizuki, Buntarou Keio University Department of Information and Computer Science Kanagawa Japan Future University Hakodate Department of Media Architecture Hokkaido Japan Hokkaido University Graduate School of Information Science and Technology Hokkaido Japan the University of Tsukuba Department of Computer Science Ibaraki Japan

Earphone-type wearable devices, also known as "hearables,"will have many functions in the future. Some of those functions will require authentication of the wearer for access to the user's privacy information or settlement of payments. In this study, we propose a new personal authentication system for hearables called EarLock. EarLock authenticates the wearer by acquiring and analyzing ear canal and auricle shape information using sound leakage from the device. The system can be implemented using a speaker and external microphone that are highly compatible with hearables. We implemented three prototype devices and investigated EarLock's authentication performance under various practical scenarios, including walking conditions, noisy environments, and situations with object interference. Experimental results showed that the in-ear, open-ear, and bone-conduction devices achieved balanced accuracy (BAC) scores of 87.2-93.7%, 83.4-94.7%, and 85.9-90.0%. © 2025 IEEE.

关键词： Information leakage

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A Secure and Efficient Information Authentication Scheme for E-Healthcare System

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computers, Materials & Continua 2023年第9期76卷 3877-3896页

作者： Naveed Khan Jianbiao Zhang Ghulam Ali Mallah Shehzad Ashraf Chaudhry Faculty of Information Technology Beijing University of TechnologyBeijing100124China Department of Computer Science Shah Abdul Latif UniversityKhairpur66111Pakistan Department of Computer Engineering Faculty of Engineering ArchitectureNisantasi UniversityIstanbul34398Turkey

The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)*** cellular network consists of several towers installed at appropriate locations within a smart *** cellular towers can be utilized for various tasks,such as e-healthcare systems,smart city surveillance,traffic monitoring,infrastructure surveillance,or sidewalk *** is a primary concern in data broadcasting,particularly authentication,because the strength of a cellular network’s signal is much higher frequency than the associated one,and their frequencies can sometimes be aligned,posing a significant *** a result,that requires attention,and without information authentication,such a barrier cannot be ***,we design a secure and efficient information authentication scheme for IoT-enabled devices tomitigate the flaws in the e-healthcare *** proposed protocol security shall check formally using the Real-or-Random(ROR)model,simulated using ProVerif2.03,and informally using pragmatic *** comparison,the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.

关键词： IoT-enable device e-healthcare authentication edge computing

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

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computers, Materials & Continua 2024年第9期80卷 4325-4340页

作者： Zhaojie Sun Linlin Xing Longbo Zhang Hongzhen Cai Maozu Guo Department of Computer Science and Technology Shandong University of TechnologyZibo255000China Department of Agricultural Engineering and Food Science Shandong University of TechnologyZibo255000China Department of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China

Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this *** a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or ***,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification *** the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word ***,we use a biaffine predictor to assist in predicting the labels of word pairs for relation *** model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous ***,we evaluated our model on two publicly accessible *** experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal *** the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal *** model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.

关键词： Deep learning biomedical joint extraction triple classification multi-granularity 2D convolution

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HybridGAD: Identification of AI-Generated Radiology Abstracts Based on a Novel Hybrid Model with Attention Mechanism

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computers, Materials & Continua 2024年第8期80卷 3351-3377页

作者： TugbaÇelikten Aytug Onan Department of Software Engineering Faculty of TechnologyManisa Celal Bayar UniversityManisa45140Turkey Department of Computer Engineering Faculty of Engineering and ArchitectureIzmir KatipÇelebi UniversityIzmir35620Turkey

Class Title:Radiological imaging method a comprehensive overview *** GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent advances in the *** and Methods:This paper provides an overview of conventional radiography digital radiography panoramic radiography computed tomography and cone-beam computed *** recent advances in radiological imaging are discussed such as imaging diagnosis and modern computer-aided diagnosis ***:This paper details the differences between the imaging techniques the benefits of each and the current advances in the field to aid in the diagnosis of medical ***:Radiological imaging is an extremely important tool in modern medicine to assist in medical *** work provides an overview of the types of imaging techniques used the recent advances made and their potential applications.

关键词： Generative artificial intelligence AI-generated text detection attention mechanism hybrid model for text classification

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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Effects of Adopting Industry 4.0 on a Manufacturing Plant 11

Effects of Adopting Industry 4.0 on a Manufacturing Plant

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Waghanna, Pranav Reddy, Akash Deshpande, Sarthak Chavan, Siddhant Jaiswal, Vishal R. Naranje, Vishal Be It Department of Pune Institute of Computer Technology Pune India It Department of Pune Institute of Computer Technology Pune India School of Engineering Architecture and Interior Design Amity University Dubai United Arab Emirates

ISBN: (纸本)9798350350357

In the era of Industry 4.0, Smart Manufacturing is reshaping how things are made. At the heart of it is the Industrial Internet of Things (IIoT), where smart sensors and connections create a digital system for factories. This paper highlights how IIoT facilitates real-time data exchange, aiding quick decision-making and creating adaptable manufacturing setups. Cyber-Physical Systems (CPS) integration is also discussed, showing how the line between physical and digital elements in manufacturing is getting blurred. Big Data Analytics is another game-changer, supporting data-driven decisions and predictive maintenance. The paper also digs into Artificial Intelligence and Machine Learning, showing how they automate tasks and continuously learn, improving manufacturing processes. Real-world applications are examined, including Digital Twins, advanced robotics like collaborative robots (Cobots), 3D printing, and smart supply chain management. While Smart Manufacturing offers numerous benefits, it's not without challenges. The survey talks about security and privacy concerns, emphasizing the need for strong cybersecurity. Interoperability, ensuring different technologies work seamlessly together, and addressing the workforce skills gap are also explored. The paper ends with examples of successful implementations and looks at future trends, such as the impact of 5G, blockchain in supply chain management, human augmentation, and the push for sustainability in Smart Manufacturing. This survey is a practical guide for anyone interested in understanding the current state and where Smart Manufacturing is headed in the world of Industry 4.0. © 2024 IEEE.

关键词： Cyber Physical System

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Forecasting and managing urban futures: machine learning models and optimization of urban expansion

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Asian Journal of Civil Engineering 2024年第6期25卷 4673-4682页

作者： Abid, Mohammed Talib Aljarrah, Njood Shraa, Tamer Alghananim, Haneen Marouf Department of Architecture College of Engineering Al-Nahrain University Baghdah Iraq Computer Sciences Faculty of Information Technology and Computer Sciences Ajloun National University Ajloun Jordan Faculty of Science and Information Technology Cyber Security Irbid National University Irbid Jordan Faculty of Engineering Architecture Department Ain Shams University Cairo Egypt

This paper presents a novel method for predicting and controlling urban growth by combining convolutional neural networks (CNN) with Spider Monkey Optimization (SMO) to efficiently use their combined capabilities. This strategy utilizes sophisticated machine learning algorithms to forecast urban development patterns, while optimization technologies enhance these forecasts by including sustainable urban design ideas. Integration plays a crucial role in tackling the difficulties presented by fast urbanization, including issues related to land use, resource administration, and environmental sustainability in the fields of civil and architectural engineering. The study employs large datasets to assess the efficacy of CNN models in detecting urban expansion patterns and enhances these models with optimization scenarios encompassing land use, traffic flow, electricity efficiency, water resources, and waste management, all with the goal of enhancing urban sustainability. The findings demonstrate a significant improvement in the precision of predictions and the ability to maintain positive results, highlighting the crucial role of integrating machine learning and optimization in urban planning methodologies. This method not only provides urban planners and politicians with strong and effective tools for making decisions but also creates opportunities for implementing creative and sustainable solutions for urban growth. The results provide a substantial contribution to the existing urban planning literature by demonstrating the actual use of these technologies in regulating urban expansion. This will stimulate more study in this rapidly evolving subject. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks Machine learning Optimization techniques Spider monkey optimization Sustainable urban planning Urban expansion

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