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7th International Conference on computer, Communication and Signal Processing, ICCCSP 2023

作者： Subramani, Shalini Selvi, M. Kumar, S. V. N. Santhosh Thangaramya, K. Anand, M. Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India School of Information Technology and Engineering Vellore Institute of Technology Vellore632014 India Computer Science and Engineering College of Engineering Guindy Anna University Chennai600025 India Department of Electronics and Communication Saveetha Engineering College Chennai602105 India

ISBN: (纸本)9783031398100

Internet of Things is made up of sensor nodes that can sense the given environment, gather the data from environment and then communicates the data to the sink node and the internet through wireless communication. As it follows the routing protocols used in Wireless Sensor Networks, the Denial of Service (DoS) attacks including selective forwarding and flooding are launched by the attackers through Routing Protocol for Low-Power and Lossy Network (RPL) attacks. Intrusion detection systems are able to handle the RPL based DoS attacks and can prevent the attackers from participation in the routing process. Therefore, we propose a new fuzzy temporal random forest algorithm for detecting the attacks more efficiently. In addition, a new intelligent agent based intrusion prevention system which uses fuzzy temporal inference rules for intrusion prevention is also proposed in this work. The proposed model has been compared with other machine learning algorithms such as decision trees, logistic regression, random forest, K-Nearest Neighbor and also the deep learning algorithm namely the Convolution neural networks. Based on the experimentations carried out, we could prove that the proposed algorithm provides higher intrusion detection accuracy, reduces the false positive rate and detection time when compared with the related work. © IFIP International Federation for Information Processing 2023.

关键词： Denial-of-service attack

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Accurate and explainable machine learning for the power factors of diamond-like thermoelectric materials

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Journal of Materiomics 2022年第3期8卷 633-639页

作者： Zhe Yang Ye Sheng Cong Zhu Jianyue Ni Zhenyu Zhu Jinyang Xi Wu Zhang Jiong Yang Materials Genome Institute Shanghai UniversityShanghai200444China School of Computer Engineering and Science Shanghai UniversityShanghai200444China School of Mechanics and Engineering Science Shanghai UniversityShanghai200444China

The application of machine learning(ML)-based methods to the study of thermoelectric(TE)materials is *** conventional ML algorithms can achieve high prediction performance,their lack of interpretability severely obstructs researchers from extracting material-oriented insights from ML *** this work,high ML-based prediction performance was achieved with respect to TE power factors(PFs),and the results were well understood by the SHapley Additive exPlanations(SHAP),a method to identify the correlations between targets and *** designed a robust PF prediction model for diamond-like compounds via a stacking technique,and the model achieved a coefficient of determination value above 0.95 on the test *** the SHAP analysis,the PFs were negatively correlated with electronegativity and positively correlated with the descriptor“volume per atom”based on the previously reported *** domain knowledge was adopted to understand these *** work shows that ML models can achieve high accuracy while exhibiting good interpretability,making them useful for materials scientists.

关键词： Machine learning Thermoelectric materials Interpretability Power factor

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IoT-Genomics Analysis for Personal Health with Cybergenomics and Cloud Technology 10

IoT-Genomics Analysis for Personal Health with Cybergenomics...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Prabakaran, T. Babu, A. Ramesh Hemalatha, S. Ramasamy, Jayabharathi Kovarasan, Rajesh Kambattan Elango, B. Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study combines the potential of wearable health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may be achieved by seamlessly integrating real-time health data collected from IoT-enabled wearables with individual genetic profiles. Using algorithms for machine learning, connections between genetic differences and instantaneous health responses may be uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can be used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has been done sheds light on the revolutionary potential of merging genetics with the Internet of Things to establish a new paradigm of preventative and individualized medical treatment. © 2024 IEEE.

关键词： Genome

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Modality Direct Image Contrast Enhancement for Liver Tumour Detection

Modality Direct Image Contrast Enhancement for Liver Tumour ...

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International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Amutha, S. Deepa, A.R. Joyal, S. School of Computer Science and Engineering SCOPE Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Electrical and Electronics Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9789819909803

Medical image analysis relies heavily on segmenting medical images. Numerous methods based on edge or area characteristics have been developed for segmenting medical images. These are reliant on the image’s quality. When detecting a region of interest, such as a lesion, on a CT or MRI scan, contrast is crucial (s). Manual histogram adjustment techniques are used for improving the contrast of image where contrast enhancement is based on cross-modality in a 2D histogram specification technique. Our proposed multi-modality guided histogram specification approach improves the liver CT images by MRI images for input data. The optimization scheme is used in our technique which is based on guided picture enhancement as well as image quality management. In a two-step process, the suggested Improved Supervised Contrast Enhancement Technique (ISCET) scheme makes use of both structural and contextual information. Applying a two-dimensional histogram definition while utilizing contextual data from the matching guiding image, such as a magnetic resonance image, is the initial step (MRI). The second stage involves using an optimization strategy that keeps the original image’s structural information while employing a structural similarity metric. The results show that the performance is improved by the increase in entropy and MIGLCM values. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality management

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Design of Dual band slot loaded Microstrip Patch Antenna for Wireless Applications 4

Design of Dual band slot loaded Microstrip Patch Antenna for...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Jenath, M. Prasanna, R. Sandhya, J. Priya, P. Banu Nivethika, S. Deepa Srm Institute of Science and Technology Department of Ece Kattankulathur Chennai India Sri SaiRam Engineering College Department of Ece Kattankulathur Chennai India Srm Institute of Science and Technology Department of Networking and Communication Kattankulathur Chennai India Vellore Institute of Science and Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508845

This work shows a dual band rectangular patch antenna with micro strip line. The suggested antenna has a dimension of size 33 x 27 mm2 and is constructed on a FR4 substrate. A dual band printed antenna is created by introducing wing shape slots and rectangular slots in the radiating element of the *** antenna is designed to operate at dual band of frequencies at 1.6/2.2 GHz bands for C band and Wi-Fi/WiMax applications. The results obtained for return loss is -11.88dB at 1.6 GHz with 3.5% bandwidth and - 13.12 dB at 2.2 GHz with 3.6% bandwidth. The suggested antenna also exhibits strong radiation properties and steady gains across the entire operating band spectrum. © 2024 IEEE.

关键词： Slot antennas

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A New Car Following Model Considering the Multi-headway Variation Forecast Effect 12th

A New Car Following Model Considering the Multi-headway Vari...

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12th International Conference on Green Intelligent Transportation Systems and Safety, 2021

作者： Kang, Yi-rong Yang, Shu-hong School of Transportation Engineering Guizhou Institute of Technology Guiyang550003 China School of Computer Science and Communication Guangxi University of Science and Technology Liuzhou545006 China

ISBN: (纸本)9789811956140

An extended car following model is presented by considering the effect of Multi-Headway Variation Forecast (MHVF) effect in the real world. The model’s linear stability criterion was obtained by employing the linear stability theory. Theoretical analysis result shows that the new consideration leads to the stabilization of traffic systems. By means of nonlinear analysis method, the modified Korteweg-deVries (mKdV) equation near the critical point was derived, thus the propagation behavior of traffic jam can be characterized by the kink-antikink soliton solution for the mKdV equation. Numerical simulation is carried out and its results is in good agreement with the aforementioned theoretical analysis. Both of them show that the MHVF effect can suppress the emergence of traffic jamming and stabilize the vehicular system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stability criteria

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Fuzz Testing of UAV Configurations Based on Evolutionary Algorithm 3rd

Fuzz Testing of UAV Configurations Based on Evolutionary Alg...

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3rd EAI International Conference on Blockchain Technology and Emerging Applications, BlockTEA 2023

作者： Ma, Yuexuan Yu, Xiao Li, Yuanzhang Zhang, Li Yan, Yifei Tan, Yu-An School of Computer Science and Technology Shandong University of Technology Shandong Zibo255049 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Department of Media Engineering Communication University of Zhejiang Zhejiang Hangzhou310018 China

ISBN: (纸本)9783031600364

With the widespread application of Unmanned Aerial Vehicle (UAV) technology, its security issues have also attracted much attention, among which the configuration attack against the UAV flight control system is one of the current research hotspots. Attackers always upload seemingly normal configuration combinations and cause an imbalance in the UAV state by exploiting configuration item verification vulnerabilities. This paper accumulates flight data through simulation, generates configuration combinations within the security range using differential evolution-based fuzz testing, uses neural networks to guide configuration item variants, and applies these configuration combinations to the AutoTest of UAV flight control systems. The experimental results show that the configuration combinations generated by fuzz testing can guide the UAV to course deviation, spin, crash and other unstable states;the code coverage and function coverage of the position and attitude code library base in the flight control system have also reached a high level. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Optimization

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EMD-TCN-TimeGAN: A Data Augmentation Model for Enhancing the Accuracy of Agricultural Product Price Prediction

EMD-TCN-TimeGAN: A Data Augmentation Model for Enhancing the...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Wang, Yinbing Wang, Xingfen Li, Libo Beijing Information Science & Technology University School of Computer Beijing China Beijing Information Science & Technology University Institute of Business Intelligence Beijing China Beijing Information Science & Technology University School of Management Science and Engineering Beijing China

ISBN: (纸本)9798350362480

Accurately predicting agricultural product prices is crucial for the stability of the agricultural market. However, due to the collection intervals of agricultural price data, which are typically daily, weekly, or monthly, the data volume is relatively small. This limitation affects the accuracy of deep learning models in price prediction. Data augmentation is an effective method to improve the accuracy of agricultural price predictions. This research proposes a novel data augmentation model, EMD-TCN-TimeGAN. Based on TimeGAN, this model has been significantly improved by replacing the RNN-based components with TCN, which enhances the model's ability to capture long-term dependencies. Additionally, the model incorporates an EMD module to decompose the original price series, thereby better capturing the intrinsic structural information of the time series and further improving the quality of the generated data. Experiments conducted on a collected corn dataset show that adding the augmented data to the training set of the prediction model and retraining it significantly improves the prediction accuracy. The coefficient of determination (R2) increased from 0.928831 to 0.968742, and the mean absolute error (MAE) decreased from 0.026023 to 0.016628. Comparisons with the TimeGAN model using RNN components validate the effectiveness of replacing the components with TCN;comparisons with TCN-TimeGAN validate the effectiveness of the EMD module in further improving prediction performance. The EMD-TCN-TimeGAN proposed in this study generates sequences that better preserve the temporal dynamics of the time series data, maintaining the temporal relationships between the original variables. This improves the accuracy of small-sample agricultural price predictions, which is of significant importance and benefit to agricultural production and related activities. © 2024 IEEE.

关键词： Prediction models

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Big Data Graph Node Importance Using Page Rank 1

Big Data Graph Node Importance Using Page Rank

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Roseline, R. Vasudevan, B. Clementking, A. Rani, S. Sivakumar, T. Srinivasan, C. PG Department of Computer Applications Cuddalore India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur India Karnataka Bengaluru India Department of BCA/IT Tamil Nadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Improving the efficiency and accuracy of finding important nodes in large networks is the goal of enhancing Big Data graph node significance analysis using PageRank. To deal with the massive and complicated data sets seen in big data settings, it is necessary to modify and enhance the PageRank algorithm. The objective is to provide a more efficient framework that can speed up computation and improve the accuracy of significance ranking in large-scale networks. To control the algorithm's scalability, it is necessary to use state-of-the-art computational methods, such as parallel processing and effective data structures. The expected result is a scalable and reliable system that can quickly and correctly analyze large networks, yielding useful insights for applications like recommendation systems, online search, and social network analysis. The focus is on making PageRank more feasible for use in large data settings while also increasing its computing efficiency and scalability. The Adjacency Matrix of the Graph in a sample of 5 nodes has a value ranging from 15 to 55 in numbers, according to results from synthetic graph data. In another instance of the same dataset, the Initial PageRank Scores range from 0.2-0.15 in numbers, and in yet another instance, the final PageRank Scores for nodes range from 0.177 to 0.242 in numbers. © 2024 IEEE.

关键词： Data accuracy

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Consistent and Softer Cross-modal Contrastive Learning for Text-based Person Retrieval 9

Consistent and Softer Cross-modal Contrastive Learning for T...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Cheng, Jingyu Su, Qirui Yang, Chenxu Lin, Tao Shanghai Institute of Technology School of Economics and Management Shanghai China Southwestern University of Finance and Economics School of Finance Chengdu China Nanjing Tech University School of Civil Engineering Nanjing China Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350376548

The Text-based Person Retrieval (TBPR) task aims to retrieve the target person's images through a given textual query. The primary challenge lies in aligning the text and image features in a fine-grained manner. Recent methods have obtained high performance via contrastive learning. However, the loosely aligned vanilla contrastive learning paradigm treats various negative samples equally, which is insufficient to achieve fine-grained cross-modal alignment and inevitably leads to sub-optimal performance. In this paper, we introduce a consistent and softer contrastive learning (CSCL) method to address these issues. Concretely, we first leverage two consistency constraints to mine finer modal representations: 1) cross-modal consistency between the mismatched image-text pairs, and 2) intra-modal consistency between all image-image or text-text pairs. Moreover, to alleviate the aligning ambiguity, we adopt an auxiliary image-text matching task and then build a softened contrastive learning objective. Extensive experiments demonstrate the effectiveness of our proposed CSCL framework on two classic benchmarks. © 2024 IEEE.

关键词： Contrastive Learning

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