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Future Trends in Smart Communities (ICFTSC), International Conference on

作者： Mahdiyeh Sadat Moosavi Jacob Williams Christophe Guillet Frédéric Merienne J. Cecil Michael Pickett Arts et Metiers Institute of Technology LISPEN HESAM Université Chalon-Sur-Saone France Department of Computer Science Oklahoma State University Stillwater Oklahoma United States of America LISPEN UBFC Université de Bourgogne Chalon-Sur-Saone Department of Computer Science Center for Cyber-Physical system Oklahoma State University Stillwater Oklahoma United States of America Department of Mechanical Engineering Oklahoma State University Stillwater Oklahoma United States of America

ISBN: (纸本)9798350334555

This paper discusses the key elements of a research study that focused on training an important procedure called “Endotracheal intubation” to novice students. Such a procedure is a virtual part of treating patients who are infected with the covid-19 virus. A virtual reality environment was created to facilitate the training of novice nurses (or nurse trainees) using the HTC Vive platform. The primary interaction with the virtual objects inside this simulation-based training environment was using the hand controller. However, the small mouth of the virtual patient and the necessity of utilizing both hands to pick up the laryngoscope and endotracheal tube at the same time (during training), led to collisions involving the hand controllers and hampered the immersive experience of the participants. A multi-sensory conflict notion-based approach was proposed to address this problem. We used “Haptic retargeting” method to solve this issue. And we compared the result of the haptic retargeting method with reference condtion. Initial Results (through a questionnaire) suggest that this Haptic retargeting approach increases the participants’ sense of presence in the virtual environment.

关键词： Training Smart cities Virtual environments Mouth Immersive experience Robot sensing systems Market research

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Detection of Liver Disease Using Machine Learning Approach

Detection of Liver Disease Using Machine Learning Approach

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Pravin Ramdas Kshirsagar Dhoma Harshavardhan Reddy Mallika Dhingra Dharmesh Dhabliya Ankur Gupta Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur Maharashtra India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Mumbai Maharashtra India Department of Mathematics and Statistics Manipal University Jaipur Jaipur Rajasthan India Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India

For more effective therapy, it’s critical to get an early diagnosis of liver illness. Due to the disease’s modest symptoms, it is a very difficult challenge for medical experts to forecast the disease in its early stages. Frequently, the symptoms show up only when it’s too late. This study uses machine learning techniques to enhance the detection of liver illness in an effort to solve this problem. The major goal of this study is to distinguish between liver patients and healthy people using classification algorithms. The prevalence of liver illness has been rising globally in the twenty-first century. According to the most recent survey data, the death rate from liver disease has increased by almost 2 million per year globally. 3.5% of deaths globally are caused by liver disease generally. As one of the most fatal diseases, chronic liver disease can be readily cured with early diagnosis and therapy. The lifespan of a patient with Chronic Liver Disease (CLD) would increase due to the rapid development of artificial intelligence (AI), including various machine learning algorithms like SVM, K-mean clustering, KNN, Random forest, Logistic regression, etc. The foundation of this research is the use of machine learning algorithms to predict liver disease. Pre-processing, feature extraction, and classification are just a few of the stages that go into predicting liver disease. In this study, a hybrid classification system is suggested for predicting liver illness.

关键词： Support vector machines Performance evaluation Machine learning algorithms Liver diseases Medical treatment Artificial neural networks Feature extraction

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Blockchain Topology Optimization Based on Node Clustering

Blockchain Topology Optimization Based on Node Clustering

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Peiyun Ran Yipeng Ji Mingsheng Liu Peng Zhao Shiyuan Yu Yongjian Huang Du Wang Zakirul Alam Bhuiyan Gang Li School of Cyberspace Security of Beihang University Beijing China School of Computer Science and Engineering of Beihang University Beijing China Shijiazhuang Institute of Railway Technology Shijiazhuang China The First Hospital of Hebei Medical University Shijiazhuang China Beijing Public Security Burea Beijing China HBIS Group Co. Ltd Shijiazhuang China Shijiazhuang Tieda Kexian Information Technology Co. Ltd Shijiazhuang China Department of Computer Information Sciences Fordham University New York USA Zhongke Zidong Information Technology (Beijing) Co. Ltd Beijing China

Since blockchain was born more than ten years ago, its performance optimization has always been a hot topic in the academic community. Since blockchain is essentially a distributed system, it needs to set a consensus mechanism to enable nodes scattered around to reach consensus and tolerate a certain number of faulty nodes. In addition, for each block to be linked, it needs to be verified by all nodes and approved by more than half of the nodes before it can be truly linked. All these will add up to a large delay. As the number of nodes in the blockchain system increases, this delay will seriously reduce the efficiency of the system in processing transactions and reduce the scalability of the blockchain. Therefore, based on the goals of optimizing the blockchain system, increasing the transaction per second(TPS), and improving the scalability, this paper has improved the topology of the blockchain, and designed the corresponding consensus mechanism and message propagation mechanism.

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A reliable color image encryption scheme based on a novel dual-wing hyperchaotic map

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Expert Systems with Applications 2025年 289卷

作者： Obaid, Mohammed Jabbar Neamah, Ammar Ali Shukur, Ali A. Pham, Viet-Thanh Grassi, Giuseppe Department of Cyber Security College of Sciences Al-Mustaqbal University Babylon51001 Iraq Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq Faculty of Electronics Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam Department of Engineering for Innovation University of Salento Lecce Italy

A symmetric image encryption algorithm based on a chaotic higher dimensional map is proposed to protect color images against unauthorized access. The algorithm employs a two-phase process: chaotic pixel shuffling for confusion and alterations of the pixel value via bitwise XOR for diffusion, both using pseudo-random sequences generated by the hyperchaotic map. The proposed chaotic map, initialized with conditions derived from a hash-like value generated from the input image pixels, creates cryptographic keys, ensuring enhanced security. Comprehensive security analysis demonstrates the algorithm's robustness, as evidenced by a key space of 2600, near-zero correlation coefficients, and entropy close to 8. Comparative evaluations confirm its superior performance over existing methods in terms of security, resistance to data loss, and efficiency. This work advances the field of image encryption using hyperchaotic dynamics for enhanced security and reliability. © 2025 Elsevier Ltd

关键词： Bifurcation (mathematics)

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

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computer Systems science & engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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A Review on Application of Deep Learning in Natural Language Processing

A Review on Application of Deep Learning in Natural Language...

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International Conference on Contemporary Computing and Informatics (IC3I)

Natural Language Processing (NLP) is a developing method utilized in building different sorts of Artificial Intelligence (AI) that is available in today’s time. More intellectual applications will tend to be a primary goal for ongoing and upcoming research. The requirement and desire for data-driven strategies for automatic semantic analysis have risen as a result of recent improvements in processing capacity as well as the accessibility of enormous several linguistic records. A boom in the applications throughout the previous several years of deep learning approaches has advanced the area of natural language processing. This review offers a succinct summary of deep learning architectures and techniques as well as a basic introduction to the area. Our goal is to develop a theoretical study of numerous sectors where NLP may have a significant impact and completely alter the situation with its automated approaches. Everyone is interested in investing in it since it is a hot issue. An in-depth investigation of NLP and its field is used to create these applications. Natural language processing (NLP) trends and its constituent parts are covered in the introduction to this article before it discusses applications for NLP, its emergence, and related issues.

关键词： Deep learning computer science Semantics Linguistics Market research Natural language processing Artificial intelligence

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Enhancing knowledge distillation via genetic recombination

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Applied Soft Computing 2025年 181卷

作者： Yangjie Cao Chuanjin Zhou Minglin Liu Weiqi Luo Xiangyang Luo School of Cyber Science and Engineering Zhengzhou University Zhengzhou 450002 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450001 China Guangdong Province Key Laboratory of Information Security Technology Guangzhou 510000 China School of Computer and Engineering Sun Yat-sen University Guangzhou 510000 China

Diverging from conventional knowledge distillation methods that solely emphasize improving the utilization of the teacher’s knowledge, this paper explores the generation of stronger student models within available knowledge. We first conceptualize the knowledge distillation process as a genetic evolution model. The student model is regarded as an independent individual, with its parameters representing the genes of that individual. These genes are partitioned into several alleles according to the architecture of the student model. Following that, we propose a universal strategy to enhance existing knowledge distillation methods by introducing genetic recombination. Prior to distillation, we initialize two independent identically distributed student models with different random seeds to obtain the first generation of genes. With each epoch of distillation, these genes evolve into the next generation. At specific generations, we randomly select one exchangeable allele from each of the two students for exchange. Our focus lies in determining the alleles to exchange and their corresponding exchange frequency (i.e., crossing-over value). This approach provides more choices and possibilities for subsequent evolution. Extensive experiments confirm the effectiveness of the strategy, demonstrating improvements across 12 distillation methods and 17 teacher–student combinations.

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Adaptive Decoding Mechanisms for UAV-enabled Double-Uplink Coordinated NOMA

arXiv

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arXiv 2022年

作者： Nguyen, Thanh Luan Kaddoum, Georges Do, Tri Nhu da Costa, Daniel Benevides Haas, Zygmunt J. Université du Québec MontréalQCH3C 1K3 Canada Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon The Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates The Department of Computer Science University of Texas at Dallas RichardsonTX75080 United States The School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States

In this paper, we propose a novel adaptive decoding mechanism (ADM) for the unmanned aerial vehicle (UAV)enabled uplink (UL) non-orthogonal multiple access (NOMA) communications. Specifically, considering a harsh UAV environment, where ground-to-ground links are regularly unavailable, the proposed ADM overcomes the challenging problem of conventional UL-NOMA systems whose performance is sensitive to the transmitter's statistical channel state information and the receiver's decoding order. To evaluate the performance of the ADM, we derive closed-form expressions for the system outage probability (OP) and system throughput. In the performance analysis section, we provide novel expressions for practical air-to-ground and ground-to-air channels, while taking into account the practical implementation of imperfect successive interference cancellation (SIC) in UL-NOMA. Moreover, the obtained expression can be adopted to characterize the OP of various systems under a Mixture of Gamma (MG) distribution-based fading channels. Next, we propose a sub-optimal Gradient Descent-based algorithm to obtain the power allocation coefficients that result in maximum throughput with respect to each location on UAV's trajectory. To determine the significance of the proposed ADM in nonstationary environments, we consider the ground users and the UAV to move according to the Random Waypoint Mobility (RWM) and Reference Point Group Mobility (RPGM) models, respectively. Accurate formulas for the distance distributions are also provided. Numerical solutions demonstrate that the ADM-enhanced NOMA not only outperforms Orthogonal Multiple Access (OMA), but also improves the performance of UAV-enabled UL-NOMA even in mobile environments. © 2022, CC BY.

关键词： Optimization

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A Review on Comparative study of 4G, 5G and 6G Networks

A Review on Comparative study of 4G, 5G and 6G Networks

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Pravin Ramdas Kshirsagar Dhoma Harshavardhan Reddy Mallika Dhingra Dharmesh Dhabliya Ankur Gupta Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur Maharashtra India Trainee Security Analyst JunoClinic Davman Technology Services Pvt. Ltd Mumbai Maharashtra India Department of Mathematics and Statistics Manipal University Jaipur Jaipur Rajasthan India Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India

In the previous several years, wireless telecommunications technology has seen a significant change. The term “generation” in the context of wireless communication typically describes to a shift in the essential characteristics of the assistance being offered, such as transmission technologies, bit rates, frequency bands, channel frequency bandwidth, and data transfer capacity. One of the most active technological fields that is expanding too quickly is the wireless age. There is a need to implement such technologies that can be integrated and adjusted in order to produce a more advanced and unified system because there are numerous superior technologies available. In this work, we provide a summary of several wireless telecommunications technologies, in particular, 4G, 5G, and 6G Networks, and a detailed comparison between them.

关键词： Wireless communication 6G mobile communication Satellites 5G mobile communication Web and internet services Market research Telecommunications

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Reversible data hiding in encrypted 3D mesh models via reference vertex circulation strategy

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Multimedia Tools and Applications 2025年 1-21页

作者： Liu, Jui-Chuan Chang, Ching-Chun Gao, Kai Chang, Chin-Chen Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan Information and Communication Security Research Center Feng Chia University Taichung407 Taiwan

As information technology has quickly developed, many different industries have emerged along with it. Fields using 3D models as their fundamental data formats are no exception. This study examines applying reversible data hiding techniques of encrypted 2D media to 3D mesh models for privacy protection purposes. Examining the unique characteristics of 3D models and how they differ from 2D media can offer us ways to innovate in more diverse directions using some state-of-the-art algorithms as the foundation to improve the privacy of the original mesh models. The proposed algorithm uses a circulated method to gradually diffuse the reference data set which is essential to be able to recover the original mesh model. It involves coordinate transformation, randomly and recursively selecting reference sets, and determining the embeddable capacities according to prediction error detection process on multi-MSBs. Furthermore, our proposed scheme compares the results of arithmetic coding and Huffman coding which are used to compress auxiliary data. Finally, auxiliary information used for message extraction and model recovery is embedded by simple bit substitution. Experimental results demonstrate that the proposed method achieves an average embedding rate of 49.08 bits per vertex (bpv), representing a significant improvement over the compared schemes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Encrypted 3D model Entropy coding Multi-MSB prediction Reversible data hiding

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