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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Inzimam Mir Aadil Ankita Agarwal Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

The vehicular-to-car (V2V) communique networks have enabled a new generation of urban transportation phase. Vehicular conversation technology can offer scalability, dependability, and accuracy in various vehicle offerings. Even though a few modern studies exist on predicting traffic behavior in vehicular networks, only some are primarily based on an analytical method., this paper presents a better analytical method primarily based on the Fuzzy Adaptive Cluster-based total model (fact) for visitor prediction in V2V networks. The truth-based total version includes an extensive fuzzy-common sense-based device to degree the continued conduct and site visitors for a selected target node and then extrapolate the conduct and site visitors for the upcoming frames. Moreover, the model also contains a k-method algorithm to prepare the visitor's statistics into clusters for analysis. The effects of the simulation indicate that the proposed truth-based version is capable of addressing greater precisely and accurately the site visitors' prediction in dynamic site visitor's conditions, attaining higher performance metrics in terms of prediction accuracy, latency, and jitter as compared to techniques, like k-Nearest Neighbor (KNN), assist Vector Machines (SVM), and Multi-Layer Perceptron (MLP). The model has primary importance for its capability as it should be degree and expects V2V site visitors in V2V communications.

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Blockchain-Enabled Smart Contracts: An Evolutionary Algorithm Approach for Automated Financial Agreement 15

Blockchain-Enabled Smart Contracts: An Evolutionary Algorith...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sekhar, J Chandra Prasad, Arun B. Kanade, Shantanu Kanade, Anuradha Dhilipan, C. Bala, B Kiran Nri Institute of Technology Guntur India Institute of Law Nirma University Ahmedabad India Symbiosis International University Symbiosis School for online and Digital Learning Maharashtra Pune India Mit World Peace University School of Computer Science and Application Maharashtra Pune India Jain Deemed to be University Cms Business School Bangalore India College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350370249

Blockchain technology has revolutionized various industries by introducing decentralized and secure systems. Smart contracts, self-executing contracts with the terms of the agreement directly written into code, represent a significant advancement in automating and enhancing the efficiency of financial agreements. This paper proposes an evolutionary algorithm approach to further optimize the implementation of smart contracts on the blockchain for automated financial agreements. The evolutionary algorithm, inspired by the principles of natural selection and genetic algorithms, aims to enhance the adaptability and performance of smart contracts over time. Through a continuous process of selection, crossover, and mutation, the algorithm evolves the codebase of smart contracts to better suit the dynamic and evolving nature of financial markets. This approach not only improves the efficiency of automated financial agreements but also enables smart contracts to adapt to changing market conditions, regulatory requirements, and user preferences. Furthermore, the security implications and challenges associated with the evolutionary algorithm are examined, along with potential strategies for mitigating risks. In conclusion, the integration of evolutionary algorithms with blockchain-enabled smart contracts represents a promising avenue for advancing the automation of financial agreements. This approach not only addresses the challenges of adaptability and optimization but also contributes to the ongoing evolution of blockchain technology in the realm of decentralized finance. The findings of this research have implications for financial institutions, regulators, and developers seeking to leverage the full potential of smart contracts in an ever-changing financial landscape. © 2024 IEEE.

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Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features

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Genomics and Informatics 2024年第1期22卷 1-10页

作者： Meng, Chaolu Hou, Yongqi Zou, Quan Shi, Lei Su, Xi Ju, Ying College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot China School of Computer Science Inner Mongolia University Hohhot China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Department of Spine Surgery Changzheng Hospital Naval Medical University Huangpu District No. 415 Fengyang Road Shanghai China Foshan Women and Children Hospital Foshan China School of Informatics Xiamen University Xiamen China

In protein identification, researchers increasingly aim to achieve efficient classification using fewer features. While many feature selection methods effectively reduce the number of model features, they often cause information loss caused by merely selecting or discarding features, which limits classifier performance. To address this issue, we present Rore, an algorithm based on a feature-dimensionality reduction strategy. By mapping the original features to a latent space, Rore retains all relevant feature information while using fewer representations of the latent features. This approach significantly preserves the original information and overcomes the information loss problem associated with previous feature selection. Through extensive experimental validation and analysis, Rore demonstrated excellent performance on an antioxidant protein dataset, achieving an accuracy of 95.88% and MCC of 91.78%, using vectors including only 15 features. The Rore algorithm is available online at http://112.124.26.17:8021/Rore. © The Author(s) 2024.

关键词： Dimensionality reduction Protein classifier Protein sequence Robust feature

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ARCS-R: Mission Critical Combined Reliability and Cybersecurity Systems engineering Analysis

ARCS-R: Mission Critical Combined Reliability and Cybersecur...

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Annual Symposium on Reliability and Maintainability (RAMS)

作者： Douglas L. Van Bossuyt Nikolaos Papakonstantinou Britta Hale Ryan Arlitt Srinivasa Rao Palatheerdham Department of Systems Engineering Naval Postgraduate School Docent VTT Technical Research Centre of Finland Department of Computer Science Naval Postgraduate School Masters in Cyber Security University of South Brittany Lorient

This paper explores how reliability analysis and cyber-security analysis can be combined using Artificial Intelligence and Machine Learning (AI/ML), and Large Language Models (LLM) to produce a continuously updated resilience analysis. This is achieved by modeling both the hardware and software of the system, and employing LLMs and AI/ML to continuously search for new software vulnerabilities and feed that information into continuously updating resilience models. A case study of a drone is presented that demonstrates the promise of the proposed method. It is expected that using the proposed method, named Assessment for Risk in Cybersecurity and Safety - Resilience (ARCS-R), will reduce failure rate of mission-critical cyber-physical systems by reducing the likelihood of a potential initiating event causing a prolonged degradation in system performance that impacts system resilience.

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Stream Attention Based U-Net for L3DAS23 Challenge 48

Stream Attention Based U-Net for L3DAS23 Challenge

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Honglong Fu, Yanjie Li, Junjie Ge, Meng Wang, Longbiao Qian, Xinyuan Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin China The Chinese University of Hong Kong Shenzhen China Co. Ltd Tianjin China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9781728163277

Machine learning applications of 3D audio are gaining increasing interest in recent years. In this paper, we propose a stream attention based U-Net to remove background noise and reverberation based on ICASSP Signal Processing Grand Challenge 2023: L3DAS23 Challenge1 Audio-only track task1 3D Speech Enhancement. Results show that proposed method achieves superior performance than the official baseline model. © 2023 IEEE.

关键词： 3D Speech enhancement L3DAS23 Challenge

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Enhancing Facial Expression Recognition with a Directional Attentive Neural Network

Enhancing Facial Expression Recognition with a Directional A...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Jagendra Singh Nitin Arvind Shelke Palak Masson Rishi Agrawal Amit Sharma Amit Parashar School of Computer Science Engineering & Technology Bennett University Greater Noida India Raj Kumar Goel Institute of Technology Greater Noida India Department of Computer Engineering & Applications GLA University Mathura India School of Computer Application Lovely Professional University Phagwara India Basic Science & Humanities-Chemistry Pranveer Singh Institute of Technology Kanpur India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

The proposed DANN was developed for better recognition of facial expression. The proposed DANN combines the attention and directional filters, which filter out features that focus on the informative facial regions while capturing the directional patterns in the facial queue. The average accuracy after benchmarking all the 10 experiment trials was in the very high level with the proposed DANN on datasets combining CK+, FER2013, and RAF-DB, with an average accuracy of 88.04%. The model showed a good trade-off between precision and recall: its average weighted F1 score was 0.881, while the average precision and recall were 0.883. This implies that the model will maximize the identification of true positive samples while, at the same time, minimizing that of false positives and false negatives. All these results are indicative of the robustness and reliability of the DANN architecture in classifying the facial expressions correctly across the different datasets and different experimental settings. The results suggest the high potential of DANN in furthering the state-of-the-art facial expression recognition with prosperous affective computing, human-computer interaction, and psychology.

关键词： Weight measurement Affective computing Accuracy Filters Face recognition Neural networks Psychology computer architecture Predictive models Robustness

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Finite Buffer Queueing System Performance Study with Multi Heterogeneous Fog Architecture 19

Finite Buffer Queueing System Performance Study with Multi H...

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19th Orissa Information Technology Society International Conference on Information Technology, OCIT 2021

作者： Dutta, Arijit Pani, Luina Misra, Chinmaya Roy, Ruben School of Computer Science Engineering KIIT Deemed To Be University Bhubaneswar India School of Computer Application KIIT Deemed To Be University Bhubaneswar India Government College of Engineering And Leather Technology Department of IT Kolkata India

ISBN: (纸本)9781665416641

When it comes to new and developing applications that require high computation and low latency, fog computing is an attractive option to consider for implementation. The peripheral network devices are able to share idle resources and work together to complete fog computing tasks. It's up to the task publishers to decide how to distribute computer jobs that take into account transmission line quality and energy utilization. To urge these gadgets to engage in computational offloading, effective stimuli approaches are needed to stimulate them. Three heterogeneous servers with varying service rates are investigated using a finite constant buffer queue. Depending on a Poisson distribution with a mean and service times, the jobs are exponentially spread by distinct mean values There is a limit to how many jobs can be held in a queue while the servers are busy. It's assumed that Because of this, jobs are considered lost if they arrive after a buffer has reached capacity. If there is no client in queue, the next customer will be presumed to be in the first server if there is no customer in line. In the case of routers, this can be seen in their different speeds when serving packets on the network. © 2021 IEEE.

关键词： Fog computing

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The Open Source Threat Intelligence Relational Dataset and Its Optimal Implementation

The Open Source Threat Intelligence Relational Dataset and I...

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Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Yaru Yang Junyu Li Ronghua Zhang Abhishek Pratap Sah Amit Yadav Asif Khan Department of Computer and Software Chengdu Neusoft University Chengdu China School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Threat intelligence provides a platform for cybersecurity engineers for attack traceability, which provides substantial knowledge database logs to defend against future security threats. Threat intelligence relationship extraction based on deep learning solves the challenge of threat knowledge construction to a certain extent but still faces problems such as lack of open-source datasets and the inability of the model to accurately correlate threat entities with potential relationships. Therefore, for cybersecurity research work, this paper designs a threat ontology, constructs the threat relationship dataset TreatRE by remote supervision, and opens this dataset. The dataset contains 12000 utterances and 12 threat relations from 500 CTIs, and it performs well in multiple relation models trained on deep learning methods. Meanwhile, we propose a multisensory attention-based threat intelligence relationship extraction method MAtt, which combines location perception, self-attention perception, and neuronal memory perception to further improve the threat relationship extraction effect. Experimental results show that the trained model based on TreatRE can more accurately extract the knowledge objects and their relationships described in threat intelligence. An accuracy score of 95.4% can be obtained using the MAtt method, which is 3.48% more than the best baseline compared with the same type of relationship extraction model.

关键词： Deep learning Knowledge engineering Technological innovation Databases Buildings Asia Ontologies

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Enhanced License Plate Detection using YOLOv8n: An Approach for Efficient Vehicle Identification

Enhanced License Plate Detection using YOLOv8n: An Approach ...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Vishesh Chandok Sudhanshu Maurya Kashish Mirza Ajay Kumar Yadav Sneha Thomas Varghese Rachit Garg Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science & Engineering Graphic Era Hill University Bhimtal Campus Uttarakhand India Department of Computer Application United Institute of Management Prayagraj India School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

An essential feature of the intelligent transportation system is the automatic detection and recognition of license plate information. The e-payment systems for parking and toll collection in traffic control security could be utilized with this application. Numerous algorithms have been created for License Plate Recognition (LPR) and Identification, each with pros and cons depending on the circumstances. computer vision has increased in terms of techniques and developments since deep learning emerged and became popular in several artificial intelligence domains. It is one of the most efficient ways to monitor traffic signals, tax paid for parking, and police monitoring through Automatic LPR. Although it, as a technology, is widely used and developed, much more must be done to improve the system's accuracy. The current science community has excelled in efficiency and problem-solving in recent years. Along with the You Only Look Once (YOLO), targeted at the object detection area and applied to this study, our primary goal is to summarize and analyze several methods and advancements in LPR in the deep learning age.

关键词： YOLO Deep learning Adaptation models Accuracy Surveillance Traffic control Real-time systems Safety Security License plate recognition

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Recursive CNN Model to Detect Anomaly Detection in X-Ray Security Image 2

Recursive CNN Model to Detect Anomaly Detection in X-Ray Sec...

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2nd International Conference on Innovative Practices in Technology and Management, ICIPTM 2022

作者： Senthil Kumar, R. Syed Musthafa, A. Balaji, A. Singh, Gurpreet Kumar, Ashok Manikandaprabu, P. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Computer Science And Engineering Chennai India M. Kumarasamy College Of Engineering Department Of Information Technology Tamil Nadu Karur India Rajalakshmi Institute Of Technology Department Of Electronics And Communication Engineering Chennai India Sant Baba Bagh Singh University Department Of Computer Science And Application Punjab Jalandhar India Banasthali Vidyapith Department Of Computer Science Banasthali India Sona College Of Technology Department Of Mechanical Engineering Tamilnadu India

ISBN: (纸本)9781665466431

To address the issue of contraband scale difference in the identification of X-ray pictures during security inspection, we upgrade the Faster RCNN network and propose a multi-channel region proposal network (MCRPN). Multi-layer feature extraction is achieved using the complementarily of distinct levels of convolution features in visual semantics, and the richer semantic components of VGG16 high-level layers and the shallower edge features of low-level layers are fused;To construct a multi-scale contraband detection network, the multi-scale candidate target regions are mapped to the corresponding feature maps;dilated convolutions are introduced into the multi-channel, and a multi-branch dilated convolutions module (DCM) is designed to increase the Receptive field and thus enhance features at different scales. On the self-created data set SIXray OD, the enhanced algorithm achieves an average detection accuracy of 84.69 percent and a test performance improvement of 6.28 percent over the original network. Additionally, the testing findings indicate that the enhanced algorithm's recognition accuracy has been increased to a considerable level. © 2022 IEEE.

关键词： Convolution

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