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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Islam, Md Shafiqul Pias, Mustakim Musully Tasnim, Nishat Hashan, Ronok Uddin, Jalal Al Mahmud, Tarek Hasan Islamic University Department of Electrical and Electronic Engineering Bangladesh Islamic University Department of Information and Communication Technology Bangladesh Islamic University Department of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798331529765

The ability to identify and acknowledge roadway signs is vital for the development of autonomous vehicle technology and the safety of the road. This article describes a successful strategy for identifying traffic indications using enhanced Convolutional Neural Networks (CNNs). The system evaluates various external factors, including variations in shape, colour, and size, as well as light and weather. Using convolutional neural networks (CNNs) to extract attributes and analyze road marking correlations, the proposed approach first collects images using cameras mounted on vehicles. The CNN model learns to distinguish between several types of traffic signs by training on an enormous dataset of annotated images, enhancing its precision and detection durability. The model becomes adaptive without autonomous feature acquisition, which eliminates the demand for human extracting features. With the ability to expand, the system may be enhanced perpetually as more labelled data becomes available. Autonomous driving and road safety technologies stand to profit significantly from this technology, which takes advantage of deep learning's ability to deliver an accurate and trustworthy solution for identifying traffic signs. © 2024 IEEE.

关键词： Convolutional neural networks

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Psychoacoustic model-driven spectral subtraction for monaural speech enhancement

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International Journal of Speech technology 2023年第4期26卷 963-979页

作者： Upadhyay, Navneet Department of Electronics and Communication Engineering The LNM Institute of Information Technology Jaipur302 031 India

In this paper, we investigate a psychoacoustic model-driven spectral subtraction framework for enhancement of noisy speech. In the proposed framework, the noisy speech spectrum is separated into six distinct and unevenly frequency-spaced subbands as per the psychoacoustic model of the human hearing system, and spectral over-subtraction is applied independently in each subband. The noise in each subband is estimated using an adaptive noise estimator that does not require a speech pause tracker. To compute and update the noise, the noisy speech power is adaptively smoothed using a smoothing factor controlled by a posterior SNR. The performance of the proposed framework is evaluated using SNR, segmental SNR (SegSNR), and PESQ scores for a variety of non-stationary and stationary noise environments at varying SNR levels. The experimental results show that the proposed framework outperforms various up-to-date speech enhancement technologies on three extensively used objective metrics assessments and speech spectrograms. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Speech enhancement

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Exciton-polaritons in a 2D hybrid organic-inorganic perovskite microcavity with the presence of optical Stark effect

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Chinese Physics B 2024年第3期33卷 551-555页

作者： Kenneth Coker 郑楚媛 Joseph Roger Arhin Kwame Opuni-Boachie Obour Agyekum 张伟利 School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Electrical and Electronic Engineering Ho Technical UniversityHo 00233Ghana Telecommunication Engineering Department Kwame Nkrumah University of Science and TechnologyKumasi 00233Ghana

This study investigates the properties of exciton-polaritons in a two-dimensional(2D)hybrid organic-inorganic perovskite microcavity in the presence of optical Stark *** both steady and dynamic state analyses,strong coupling between excitons of perovskite and cavity photons is revealed,indicating the formation of polaritons in the perovskite ***,it is found that an external optical Stark pulse can induce energy shifts of excitons proportional to the pulse intensity,which modifies the dispersion characteristics of the polaritons.

关键词： exciton-polaritons perovskite microcavity optical Stark effect

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Enhancing the Capability of Single Error Correction and Double Error Detection Method 2

Enhancing the Capability of Single Error Correction and Doub...

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2nd International Conference on information and communication technology, ICICT 2024

作者： Sadi, Muhammad Sheikh Molla, M.D. Peyal Asraf, Amanullah Ali, Md. Liakot Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798331508227

This paper proposes a new approach to tolerate double-bit soft errors. Soft error is becoming a matter of concern for computer systems. Critical systems and systems operated in electronic hostile environments are at high risk due to the sudden occurrence of soft errors. This paper proposes a new method for correcting double-bit soft errors in an n-bit dataword where n can be any integer number. The most efficient part of this paper is the temperate use of redundant bits. In this paper, the XOR method is used along with the SECDED (Single Error Correction - Double Error Detection) code to enhance the capability of SECDED toward double-bit error correction. This paper corrects all two-bit errors and can also correct almost every type of triple-bit error. ©2024 IEEE.

关键词： Errors

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Predictive Modeling Techniques of Social Dynamics in Multilayer Social Networks: A Survey 4th

Predictive Modeling Techniques of Social Dynamics in Multila...

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Jaya Krishna, Raguru Vamshi Krishna, B. Gopalakrishnan, T. Anagha, P. Kumar Sharma, Vijay Prasad Sharma, Devi Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer and Communication Engineering and Department of Information Technology Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819736898

Various techniques involved in analyzing social media data have become an important aspect of research. There is a need for efficient techniques to explore huge amounts of data for information prediction like product rating, political election prediction, viral marketing, and information cascading, etc. Nowadays multilayer social networks offer huge platforms for getting opinion reviews and recommendations about any user-interested topic. In multilayer social networks, multiple users are connected and the relationship between them is represented in different layers indicating different connections and interactions. Network structure is more complex and the social interactions between the individuals are dynamic in nature. This study reviews a thorough overview of multilayer social network predictive modeling methodologies and examines contemporary multilayer social network algorithms that have been published in the literature. In addition, we also discussed various key issues and algorithms to address the issues. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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Harnessing Bio-Inspired Optimization and Swarm Intelligence for Energy-Aware TinyML in IoT 7

Harnessing Bio-Inspired Optimization and Swarm Intelligence ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kalyanakumar, P. Pandian, S. Srinivasa Boopalan, S. Jesintha, D. Kani Krishnan, R. Santhana Muthu, A. Essaki Velammal College of Engineering And Technology Department of Information Technology Madurai India Scad College of Engineering And Technology Department of Computer Science And Engineering Cheranmahadevi India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India

ISBN: (纸本)9798350359299

This research investigates the integration of bio-inspired optimization and swarm intelligence principles with TinyML for the development of energy-aware Internet of Things (IoT) devices. A novel model algorithm, termed "BioSwarmML,"is introduced and evaluated against existing algorithms through comprehensive simulation analyses employing suitable metrics. The proposed framework aims to enhance energy efficiency in IoT applications by leveraging the collective intelligence derived from bio-inspired optimization and swarm behaviors. The "BioSwarmML"algorithm is designed to draw inspiration from natural processes, incorporating bio-inspired optimization techniques such as genetic algorithms, simulated annealing, and evolutionary strategies. Concurrently, swarm intelligence principles are integrated to emulate decentralized and self-organizing behaviors observed in biological systems. This amalgamation aims to optimize the energy consumption of TinyML models on IoT devices, facilitating sustainable and adaptive learning processes. Simulation analyses involve a comparative study with established algorithms, evaluating BioSwarmML based on metrics such as energy consumption, accuracy, and latency. The results demonstrate the efficacy of the proposed algorithm in achieving superior energy efficiency while maintaining competitive performance in terms of accuracy and responsiveness. The comparison sheds light on the advantages of BioSwarmML in energy-aware TinyML applications, showcasing its potential for widespread adoption in IoT ecosystems. This research contributes to the advancement of energy-efficient IoT systems by introducing a novel algorithmic paradigm that aligns with international journal standards. The proposed "BioSwarmML"model showcases a promising avenue for enhancing sustainability in TinyML-driven IoT applications, offering a valuable addition to the existing body of knowledge in the field. © 2024 IEEE.

关键词： Internet of things

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A secured internet of robotic things (IoRT) for long-term care services in a smart building

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Journal of Supercomputing 2023年第5期79卷 5276-5290页

作者： Chang, Shih-Hao Hsia, Chih-Hsien Hong, Wei-Zhi Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Ilan University Yilan Taiwan Department of Business Administration Chaoyang University of Technology Taichung Taiwan Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan

Long-term care refers to any support, both medical and non-medical, provided to the elderly with a chronic illness or disability due to physical or mental conditions. Since the cost of long-term care insurance is not inexpensive, low-cost devices and sensors can be used to create medical assistance systems to reduce human maintenance costs. The requirement of security and privacy under healthcare information protection is a critical issue for internet of medical things (IoMT) data transmission. In this paper, we designed an IoMT security robot for a long-term care system. The goal of this IoMT security robot is to provide secure transmission of the residents’ private information. It is composed of three layers, namely, collection, encryption, and transmission. The function of the IoMT security robot is to first collect data from the patient or the elderly, then provide efficient data encryption, and deliver secured data transmission mechanisms to send the valuable data to the cloud. This IoMT security robot also has a server authentication mechanism, and a support IoT and IoMT devices inspection function. Our evaluation results showed that even when we utilized a low power consumption device like Raspberry Pi, AES algorithm achieved an encrypt and decrypt of 100–100 K bytes under 9 ms, which is a lot better than ECC, which takes about 104 ms. Further, we found that the AES only takes 0.00015 s to decrypt 100 Bytes data, which is way faster than the ECC algorithm, which takes 0.09 s. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Cryptography

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Cumulative histogram as a feature selection technique for anomaly detection

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Multimedia Tools and Applications 2025年第12期84卷 10095-10107页

作者： Nassar, Mostafa Salama, Rania A. Saleeb, Adel A. El-bahnasawy, Nirmeen A. Ahmed, Hossam Eldin H. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology Faculty of Computers and Information Suez University Suez Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia

The enhancement of Intrusion Detection Systems (IDS) is required to ensure protection of network resources and services. This is a hot research topic, especially in the presence of advanced intrusions and attacks. This paper provides a comparison between Distributed Cumulative Histogram (DCH) as a Feature Selection (FS) technique, information Gain Ratio (IGR) FS and wrapper-based FS in terms of accuracy and Root Mean Square Error (RMSE). The utilization of DCH of the traffic instances in normal and attack cases allows us to compare the traffic charts. We can observe the difference between effective features and less effective ones. We verify the feasibility of using DCH as an FS technique in the field of anomaly detection with just six selected features giving more accurate results with most classifiers compared to the IGR and wrapper-based FS. We applied our experiments on the modern UNSW dataset with the WEKA simulation platform that contains a group of classification, feature reduction and selection techniques. © The Author(s) 2024.

关键词： Anomaly detection

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3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

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computer Modeling in engineering & Sciences 2024年第2期138卷 1365-1384页

作者： Dun Cao Jia Ru Jian Qin Amr Tolba Jin Wang Min Zhu College of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time *** modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is *** paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

关键词： Internet of vehicles road networks 3D road model structure recognition GIS

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ICRM: An intelligent citation recommendation mechanism based on BERT and weighted BoW models

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10135-10150页

作者： Chang, Chih-Yung Yang, Yu-Ting Zhang, Qiaoyun Lin, Yi-Ti Roy, Diptendu Sinha Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan Department of English Tamkang University New Taipei Taiwan Department of Computer Science and Engineering National Institute of Technology Shillong India

With the field of technology has witnessed rapid advancements, attracting an ever-growing community of researchers dedicated to developing theories and techniques. This paper proposes an innovative ICRM (Intelligent Citation Recommendation Mechanism), designed to automate the process of suggesting the appropriate number of citations for individual brackets within a document. The proposed ICRM comprises three phases: Coarse-grained Weighted Bag of Word (WCBW), Fine-grained SciBERT (FSB) and Citation Adjustment phases. Firstly, the WCBW phase employs TF-IDF to extract keywords from both target and candidate documents, forming vectors that capture word significance along with metadata like authorship, keywords, and titles. It aims to identify relevant papers from a database, serving as initial candidates for each bracket. Secondly, the FSB phase employs the SciBERT model to assess the similarity between candidate documents and the local context around brackets, enhancing the precision of recommendations. It refines this selection by analyzing candidate-document relationships within the proximity of the brackets. Lastly, the Citation Adjustment phase tackles overlapping citations and ensures that recommended citation numbers align with user-defined criteria, resolving issues of imbalance. The simulation results demonstrate that the proposed ICRM outperforms existing models significantly in terms of precision, recall and F1-score. © 2024 - IOS Press. All rights reserved.

关键词： Artificial intelligence

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