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33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Lai, Ting-Hsu Liu, Tsung-Jung Liu, Kuan-Hsien National Chung Hsing University Department of Electrical Engineering Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324112

Crowd counting is an important cornerstone in tasks related to crowd analysis. In recent years, there has been a significant rise in the development of deep learning methods, particularly those based on Convolutional Neural Networks (CNN), in the field of image processing and achieved amazing results. However, there are some challenges waiting to be solved in the current crowd counting task: large-scale variations in crowd sizes and interference from the background. Both of them will lead to poor prediction results. Therefore, we propose a scale selection module to deal with the scale variation problem in images. And for background interference, we proposed an attention module to reduce the interference of background information. Moreover, we evaluated our model on four commonly used datasets and compared the performance with other state-of-the-art methods to demonstrate the competitiveness of our approach. The source code and pretrained models are available at https://git;***/TimLai0307/SSN. © 2023 IEEE.

关键词： Convolutional neural networks

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A Novel Approach Based on Hybrid Algorithm for Energy Efficient Cluster Head Identification in Wireless Sensor Networks

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computer Systems science & engineering 2022年第10期43卷 259-273页

作者： C.Ram Kumar K.Murali Krishna Mohammad Shabbir Alam K.Vigneshwaran Sridharan Kannan C.Bharatiraja Department of Electronics and Communication Engineering Dr.N.G.P.Institute of TechnologyCoimbatore641048India Department of Electronics and Communication Engineering Vignan’s Institute of Information TechnologyDuvvadaVisakhapatnam530049India Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJizan45142Kingdom of Saudi Arabia Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering JKK Munirajah College of TechnologyErode638506India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data *** clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster *** major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor *** proposed model is a hybridization of Glowworm Swarm Optimization(GSO)and Artificial Bee Colony(ABC)algorithm for the better identification of cluster *** performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35%better value and reduction of time complexity upto 1.46%.Above all,the proposed model is 16%ahead of alive node count when compared with the existing methodologies.

关键词： Wireless sensor network cluster cluster head hybrid model glowworm swarm optimization artificial bee colony algorithm energy consumption

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Machine Vision for Measurement Accuracy in Industrial Automation 15

Machine Vision for Measurement Accuracy in Industrial Automa...

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

作者： Ezhilarasan, G. Singh, Vaishali Upadhyay, Ramakant Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Himachal Pradesh India Department of Computer Science Engineering Sanskriti University Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

Commercial automation has been a powerful device for enhancing productivity, decreasing charges, and increasing performance. In particular, system vision systems have enabled the implementation of advanced automation answers, allowing accurate and rapid quality measurements and inspections. This paper examines the cutting-edge nation of the use of machine imaginative and prescient systems in industrial automation, highlighting the opportunities they provide in phrases of increasing dimension accuracy. It examines the specific sorts of device vision structures to be had, their advantages and boundaries, as well as their ability for improving the exceptional of business automation structures. It also addresses the challenges that ought to be overcome to make certain full adoption of such structures, as well as the strategies for fixing these challenges, inclusive of the improvement of progressed algorithms and laptop imaginative and prescient additives, and training applications for industrial engineers. This paper affords a comprehensive evaluation of associated studies and enterprise standards, permitting readers to gain a better expertise of the development of machine vision systems in industrial automation and to become aware of possibilities to improve business dimension accuracy. © 2024 IEEE.

关键词： Office automation

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Age and Gender Recognition using Deep Learning Technique 3

Age and Gender Recognition using Deep Learning Technique

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Patel, Margi Singh, Upendra Indore Institute of Science and Technology Department of Computer Science and Engineering Indore India Shri G. S. Institute of Technology and Science Department of Information Technology Indore India

ISBN: (纸本)9781665464871

Gender classification is popular because it includes information about male and female social activities. Faces make it difficult to derive gender-discriminating visuals. Gender classification is based on looks. Automatic gender classification is popular because genders include rich social information. Classification has grown increasingly important in many industries. In a conservative society, gender classification can be usedin certain contexts. Identifying gender type is crucial to keeping extremists out of safe locations, especially in sensitive areas. A similar technique is utilized in female-only railway carriages, gender-specific marketing, and temples. Biometrics debates gender classification from facial pictures. Traditional ways categorize hand-crafted features globally and locally. These gender-identification systems need subject knowledge and are ineffective. Human gender identification is easy, but machines struggle. We listed numerous gender classification pre-processing approaches, such as contrast and brightness normalization. To create a gender and age classification framework Deep Belief Networks employs Shifted Filter Responses to identify features. The suggested model achieves 98% and 99% accuracy on the benchmark dataset. © 2023 IEEE.

关键词： Convolutional neural networks

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Hybrid Routing Techniques for Location Privacy in IoT-Enabled Wireless Sensor Healthcare Networks

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SN computer science 2024年第8期5卷 1-10页

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Ambedka Beedi Karnataka Bengaluru 560001 India Department of Information Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Bengaluru 560064 India

Ensuring patient privacy is critical for healthcare systems reliant on the Internet of Things. While prior work has addressed patient privacy extensively, a comprehensive end-to-end strategy protecting patients’ location and data privacy under the assumption that system entities are untrusted has yet to be developed. In response, this paper proposes a novel hybrid Deep Q-learning Neural Network (DQ-NN) technique tailored to Source Location Privacy (SLP) integrated with Blockchain technology for routing in Internet of Things (IoT)-enabled Wireless Sensor Networks (WSNs). To overcome the shortcomings of the existing systems, the study offers extremely random routing paths between the source and sink nodes. The plan uses strategically selected phantom nodes to transmit packets to the sink node randomly, ensuring the paths are incredibly confusing to the enemy. To attain maximum privacy and optimize energy consumption, the suggested method employs a hybrid DQNN model to select optimal paths among multiple paths generated. Blockchain integration adds an extra layer of security to the routing network. The results of the simulation show that the proposed scheme outperforms other schemes by offering a more extended safety period, optimized energy, and enhanced privacy for the healthcare environment. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain for privacy Hybrid phantom routing Location privacy Neural network solutions Wireless sensor network privacy

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Transfer Learning Algorithm Design for Feature Transfer Problem in Motor Imagery Brain-computer Interface

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China Communications 2022年第2期19卷 39-46页

作者： Yu Zhang Huaqing Li Heng Dong Zheng Dai Xing Chen Zhuoming Li School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001 China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001 China School of Electrical Computer and Energy EngineeringArizona State UniversityTempeArizona85281 USA

The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal and the changes of the experimental environment make the feature distribution of the testing set and training set deviates,which reduces the classification accuracy of *** this paper,we propose a Kullback–Leibler divergence(KL)-based transfer learning algorithm to solve the problem of feature transfer,the proposed algorithm uses KL to measure the similarity between the training set and the testing set,adds support vector machine(SVM)classification probability to classify and weight the covariance,and discards the poorly performing *** results show that the proposed algorithm can significantly improve the classification accuracy of the testing set compared with the traditional algorithms,especially for subjects with medium classification ***,the algorithm based on transfer learning has the potential to improve the consistency of feature distribution that the traditional algorithms do not have,which is significant for the application of MI-BCI.

关键词： brain-computer interface motor imagery feature transfer transfer learning domain adaptation

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Citrus Leaf Disease Prediction: Deep Feature Fusion Perspective 5th

Citrus Leaf Disease Prediction: Deep Feature Fusion Perspect...

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5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Palei, Shantilata Lenka, Rakesh Kumar Behera, Santi Kumari Sethy, Prabira Kumar Nanda, Sandeep Barik, Rabindra Kumar Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar751003 India Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Sambalpur768018 India Electronics Sambalpur University Odisha Sambalpur768019 India Computer Science and Engineering Sanskriti University Uttar Pradesh Mathura281401 India School of Computer Applications Kalinga Institute of Industrial Technology Odisha Bhubaneswar751024 India

ISBN: (纸本)9783031609343

Citrus disease is a major problem of economic and productive loss in agriculture worldwide. Crops are affected by uneven climatic conditions, which leads to lower agricultural yields. This will affect the world’s agricultural economy. This paper proposes an image recognition method based on machine learning (ML) and deep learning (DL) feature fusion techniques for citrus leaf disease prediction. The three most popular CNN models were considered. The CNN models were AlexNet, vgg16, and vgg19. The features of these CNN models were extracted and fed into the support vector machine (SVM) to classify citrus leaf diseases. These classification models were evaluated in terms of performance metrics calculation. Subsequently, the multilayer feature fusion technique was adapted to the best-performing CNN models. Here, vgg16 performed the best among AlexNet, vgg16, and vgg19. Then, the different combinations of feature fusion of vgg16 are adapted. These fused features were fed into the SVM for citrus leaf disease classification. The feature fusion of ‘fc6 plus fc7’ of vgg16 with SVM yielded the best results;that is, the maximum values of accuracy, sensitivity, specificity, precision, FPR, F1 score, MCC, and Kappa coefficient were 97%, 97%, 98.09%, 97.06%, 1.58%, 95.34%, 84.4%, and 86.79% respectively. This method is very much helpful to the farmer as well as to the customers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Citrus fruits

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A Secure Communicating While Jamming Approach for End-to-End Multi-Hop Wireless Communication Network

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Chinese Journal of Electronics 2024年第3期33卷 833-846页

作者： Xiao MA Dan LI Liang WANG Weijia HAN Nan ZHAO School of Physics and Information Technology Shaanxi Normal University Beijing Aerospace Science and Industry Century Satellite Hi-tech Co. Ltd.Xi'an Branch School of Computer Science Shaanxi Normal University School of Telecommunications Engineering Xidian University

With the rapid development of wireless communications,cellular communication and distributed wireless network are fragile to eavesdropping due to distributed users and transparent ***,to adopt bigger transmit power at a given area to interfere potential eavesdroppers not only incurs huge energy waste but also may suppresses regular communication in this *** this end,we focus on secure communication in multi-hop wireless communication network,and propose two communicating while jamming schemes for secure communication in presence of potential eavesdroppers for the narrow band and broad band point-to-point(P2P) systems respectively with the aid of artificial noise transmitted by a chosen cooperative ***,to achieve the end-to-end(E2E) multi-hop secure communication,we devise the secure network topology discovering *** constructing a proper network topology with at least one proper node as the cooperative interferer in each hop,and then propose the secure transmission path planning scheme to find an E2E secure transmission route from source to destination,*** on the wireless open-access research platform demonstrate the feasibility of the proposed ***,simulations results validate that the proposed schemes can achieve better performance compared with existing methods in both the P2P communication case and E2E multi-hop communication network scenario.

关键词： Wireless communication Network topology Law Simulation Wireless networks Noise Spread spectrum communication

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Evaluation and Implementation of Optimal Classification Algorithms for Credit Card Fraud Detection 2

Evaluation and Implementation of Optimal Classification Algo...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Sampath Kini, K. Pushpakumar, R. Senthamil Selvan, R. Praveenkumar, G.D. Basi Reddy, A. Nitte Deemed To be University Department of Computer Science and Engineering Karkala India Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Information Technology Avadi India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India Kongu Engineering College Department of Computer Technology Erode India School of Computing Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798350364699

Nowadays, the credit card industry is more concerned about credit card fraud than any other problem. The main idea is to examine optional techniques used for fraud detection and to identify the different types of credit card fraud. Numerous strategies exist for lowering the prevalence of credit card fraud, each tailored to the specific threats faced by banks and the credit card industry. Credit card fraud prevention is the primary goal of implementing these measures. Existing methods have several unresolved issues that cause legitimate credit card customers to be mistakenly labelled as fraudulent. The amount of fraud in the financial sphere may be inferred from this paper's emphasis on integrating the best classification algorithm out of four distinct methods. © 2024 IEEE.

关键词： Credit card fraud faced by bank Financial sphere mistakenly labelled Primary Goal

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Enhanced face recognition using rank-level fusion with multi-feature vectors and interval type-3 fuzzy set 3

Enhanced face recognition using rank-level fusion with multi...

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3rd IEEE Applied Signal Processing Conference, ASPCON 2023

作者： Ghosh, Manas Sing, Jamuna Kanta Rcc Institute of Information Technology Department of Computer Application Kolkata India Jadavpur University Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350325881

In the near past, interval type-3 fuzzy set theory has been introduced as a more effective means of addressing uncertainty in decision-making than its fuzzy set counterparts. This paper suggests an approach to face recognition which integrates multiple feature vectors and interval type-3 fuzzy sets for fuzzy rank-level fusion. Use of multiple feature vectors resolves inter-class resemblances while incorporating interval type-3 fuzzy membership function in generating fuzzy rank reduces the uncertainty due to intra-class divergences. The output of the classifier is regarded as confidence factor. For each feature vector, the fuzzy ranks are generated by formulating an interval type-3 fuzzy membership function for each of top-ordered H classes based on confidence factors utilizing all training feature vectors and those of the training samples of the respective class. Multiple fuzzy ranks, built upon interval type-3 fuzzy set, are generated by employing of multiple feature vectors. To determine the final score of a class, summation of the fuzzy rank complements is combined with the complement of arithmetic mean of respective classifier's output. Final scores are evaluated to conclude test image's class. Two publicly accessible face databases are used to evaluate the method and it has shown that the solution, advocated here, exhibits an improved effectiveness over its existing related counterparts. © 2023 IEEE.

关键词： Face recognition

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