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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Wu, Yuhan Dong, Siyuan Zhou, Yi Zhao, Yikai Fu, Fangcheng Yang, Tong Niu, Chaoyue Wu, Fan Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350322279

In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users' privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem - key-value set secure aggregation - that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously. © 2023 IEEE.

关键词： Vectors

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Sentimental Insight 2.0: Exploring the Frontiers of Opinion Mining with Deep Learning 2

Sentimental Insight 2.0: Exploring the Frontiers of Opinion ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Murugan, S.Karthick Jayachandran, S. Mageshwari, G. Latha, J. Naveen, G. R. M. D. Engineering College Kavaraipettai Department of Computer Science and Engineering Tamilnadu Thiruvallur India Srm Institute of Science and Technology Department of Computer Science and Application Chennai Ramapuram India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Tiruvallur India R. M. K. Engineering College Department of Computer Science and Engineering Tiruvallur Tamilnadu Kavaraipettai India Madha Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350382952

'Sentimental Insight 2.0' emerges as revolutionary research using deep learning techniques in response to the increasing demand for more complex sentiment analysis in the age of information abundance. Traditional sentiment analysis methods sometimes fail to accurately capture the particulars of emotions, sarcasm, and context, demanding a more advanced technique. The existing system's limitations include its inability to detect minor sentiment variations and its vulnerability to noisy and distorted data. The proposed system integrates cutting-edge deep learning architectures such as recurrent neural networks (RNNs), extended short-term memory networks (LSTMs), and transformer models into an extensive structure. The result showcases the advanced sentiment analysis, outperforming existing systems with 94% accuracy, 93% precision, 91% recall, and 93% F1 score, demonstrating superior computational efficiency and emotion classification across diverse sentiments. In the information-centric world, the study's results significantly expand sentiment analysis skills, providing a vital resource for applications in social media analytics, consumer feedback, and market sentiment prediction. © 2024 IEEE.

关键词： Sentiment analysis

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Cyber-Crime and Digital Forensics: Challenges Resolution

Cyber-Crime and Digital Forensics: Challenges Resolution

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Singh, Arpita Singh, Nilu Singh, Sanjay K. Nayak, Sandeep K. AIIT Amity University Lucknow India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Computer Science and Engineering Andhra Pradesh Guntur India Integral University Department of Computer Science & Application Lucknow India

ISBN: (纸本)9798350348217

In this technology-friendly era, technology and science govern the world so much. No matter what sector it is, here or somewhere all sectors depend on technology. This technical dependency helps all sectors to grow fast but also invites criminals to take advantage. Criminal cases which are based on technology are getting to hike drastically day by day. Cybercrime is now these days one of the key problems of mankind. There are so many organizations from different sectors which are facing down because of cyber-crime, so many individuals/private or governments facing problems due to cyber-crimes. Digital/computer forensics is a new pattern to find cyber-crimes. Digital forensic science requires new forensic tools and technology to overcome such kinds of crimes. In this paper, the authors discussed different scenarios of cybercrime and the crime rate of different states of India. In addition, the paper is focused on some preventive measures to reduce the risk of cybercrime and will also analyze statistical data provided by government organizations and forecast cybercrime for the next few years. © 2023 IEEE.

关键词： Cybersecurity

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MiniNet: Dense squeeze with depthwise separable convolutions for image classification in resource-constrained autonomous systems

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ISA Transactions 2023年 132卷 120-130页

作者： Tseng, Fan-Hsun Yeh, Kuo-Hui Kao, Fan-Yi Chen, Chi-Yuan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Information Management National Dong Hwa University Hualien Taiwan Computer Science and Engineering Department of National Sun Yat-sen University Kaohsiung804 Taiwan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan Department of Computer Science and Information Engineering National Ilan University Yilan Taiwan

In recent years, artificial intelligence (AI) has been developed vigorously, and a great number of AI autonomous applications have been proposed. However, how to decrease computations and shorten training time with high accuracy under the limited hardware resource is a vital issue. In this paper, on the basis of MobileNet architecture, the dense squeeze with depthwise separable convolutions model is proposed, viz. MiniNet. MiniNet utilizes depthwise and pointwise convolutions, and is composed of the dense connection technique and the Squeeze-and-Excitation operations. The proposed MiniNet model is implemented and experimented with Keras. In experimental results, MiniNet is compared with three existing models, i.e., DenseNet, MobileNet, and SE-Inception-Resnet-v1. To validate that the proposed MiniNet model is provided with less computation and shorter training time, two types as well as large and small datasets are used. The experimental results showed that the proposed MiniNet model significantly reduces the number of parameters and shortens training time efficiently. MiniNet is superior to other models in terms of the lowest parameters, shortest training time, and highest accuracy when the dataset is small, especially. © 2022 ISA

关键词： Convolution

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Unleashing the Power of Big Data: A Comprehensive Analysis and Future Directions 6

Unleashing the Power of Big Data: A Comprehensive Analysis a...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sharma, Annu Kumar, Gaurav Farooq, Mansoor Gupta, Manish Kumar Raj, Saranya Rai, Ashok Kumar RajaRajeshwari College of Engineering Department of Computer Science & Application Karnataka Bengaluru India Indian Institute of Science Education and Research Bhopal Department of Computer Science & Engineering Madhya Pradesh Bhopal India University of Kashmir Department of Management Studies Jammu & Kashmir India Madan Mohan Malaviya University of Technology Department of Information Technology & Computer Application UP Gorakhpur India Dronacharya Group of Institutions Department of Data Science & Engineering Uttar Pradesh Greater Noida India Madan Mohan Malaviya University of Technology Department of Computer Science & Engineering UP Gorakhpur India

ISBN: (纸本)9798350304480

Big Data (BD) has emerged as a transformative force, offering unprecedented opportunities for organizations to extract valuable insights and drive informed decision-making. This research paper presents a comprehensive analysis of the current state of BD, explores its potential applications across industries, and outlines future directions for maximizing its benefits. This paper examines the key components of BD, including data collection, storage, processing, and analytics. It investigates the technologies and methodologies that enable efficient handling of large volumes of structured and unstructured data. Additionally, it explores the impact of cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) on the growth and utilization of BD. Overall, this research paper provides a comprehensive analysis of BD, its current applications, challenges, and future directions. It serves as a valuable resource for researchers, practitioners, and policymakers seeking to unlock the full power of BD and harness its benefits across diverse domains. © 2023 IEEE.

关键词： Analytics artificial intelligence Big data Internet of Things Processing

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Pruning Optimization of Deep Learning Model Based on Generalization Estimation

Pruning Optimization of Deep Learning Model Based on General...

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Artificial Intelligence, Deep Learning and Neural Networks (AIDLNN), International Conference on

作者： Xuanzhe Li Wenjun Ke Wantao Wen Shaotong Liu Xiaojuan Sheng School of Computer Science and Engineering Southeast University Beijing Institute of Computer Technology and Application

ISBN: (数字)9798331520816

ISBN: (纸本)9798331520823

Existing research has shown that deep learning models contain considerable redundancy, requiring compression optimization to eliminate unnecessary parameters and improve computational efficiency. In model compression through pruning, many existing methods often use loss values to guide the identification of sub-network models. However, low training loss does not necessarily indicate good generalization ability. Consequently, reducing model parameters while preserving strong generalization performance has become a key area of research. Addressing the challenge of ensuring strong generalization performance of submodels after optimization through deep learning model pruning, we propose a channel-structured generalization estimation model. The model architecture incorporates Fully Connected Neural Networks (FCNN) and Gated Recurrent Units (GRU) as modules for channel feature extraction. Additionally, we introduce a differentiable pruning algorithm based on generalization estimation. Experimental results demonstrate the capability of the channel-structured generalization estimation model in predicting the generalization gap via regression. Furthermore, the proposed differentiable pruning algorithm effectively safeguards the generalization capability of sub-models. We conduct pruning experiments on NiN-structured convolutional neural network models on two datasets, CIFAR-10 and SVHN. Comparative experimental results demonstrate the effectiveness of this approach: achieving a 0.72% accuracy improvement over the original model on CIFAR-10; and a 0.12% accuracy improvement over the base model on SVHN while reducing FLOPs by 50.2%, validating the effectiveness of the proposed approach.

关键词： Deep learning Training Accuracy Computational modeling Neural networks Estimation Logic gates Prediction algorithms Convolutional neural networks Optimization

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Exploratory Data Analysis using Autoviz for Machine Learning Classification Problem

Exploratory Data Analysis using Autoviz for Machine Learning...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Praveen Gujjar, J. Kotiyal, Arnav Prasanna Kumar, H.R. Devadas, Raghavendra M Jahan, Afsar Department of Computer Science and Engg. Dehradun India Faculty of Management Studies Bengaluru India Visvesvaraya Technological University PESITM Dept. of Information Science & Engg. Shivamogga India Gitam School of Technology Department of Computer Science and Engineering Gitam University Bengaluru India Department of Computer Application Dehradun India

ISBN: (纸本)9798350376470

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and depth of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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An Enhanced Explicit Port Forwarding Solution for Improving Video Delivery over Software-Defined Networks

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Journal of Internet Technology 2023年第2期24卷 313-321页

作者： Ke, Chih-Heng Lo, Yu-Wen Chen, Yeong-Sheng Chen, Hung-Pai Department of Computer Science and Information Engineering National Quemoy University Taiwan Master of Information Technology and Application National Quemoy University Taiwan Department of Computer Science National Taipei University of Education Taiwan The Graduate School of Arts and Humanities Instruction National Taiwan University of Arts Taiwan

In order to improve video delivery, an Enhanced Explicit Port Forwarding (EEPF) solution is proposed. Data are divided into ordinary packets and video packets using a packet identification mechanism. Traditional mechanisms transmit packets through routing table lookup. As the size of the routing table often becomes excessively large, this lookup can be time-consuming. While the EEPF solution directly includes the transmission path in the video packet, which can reduce the transmission time. And by setting header flags/fields in video packets, the transmission can be further optimized. For example, to reduce the amount of duplicate data to be transmitted for broadcast packets are not replicated until they arrive at the destination switch;to avoid packet loss caused by disconnection, the transmission path can be monitored and rerouting can be initiated immediately in the event of disconnection;to increase the chance of successful delivery, packets can be replicated before they are transmitted via a path with high packet loss risk;and to prevent video packets from being discarded, the switch queue status can be monitored and managed. The testing results have shown that the EEPF solution can effectively shorten the time of video packet transmission and improve the quality of transmitted video. © 2023 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Packet loss

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3EDANFIS: Three Channel EEG-Based Depression Detection Technique with Hybrid Adaptive Neuro Fuzzy Inference System

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Recent Patents on engineering 2023年第6期17卷 32-48页

作者： Mahato, Shalini Paul, Sanchita Goyal, Nishant Mohanty, Sachi Nandan Jain, Sarika Ranchi India Department of Computer Science and Engineering Birla Institute of Technology Ranchi Mesra India Central Institute of Psychiatry Ranchi Kanke India VIT-AP University Andhra Pradesh Amaiavati India Department of Computer Application National Institute of Technology Kurukshetra Haryana India

Background: Depression is a mental disorder that often negatively impacts the actions and feelings of the affected person. No laboratory tests are available to detect and properly diagnose depression. Presently, the detection of depression is done based on standardized question-naires like the Diagnostic and Statistical Manual of Mental Disorders-fifth edition (DSM-V) and Hamilton Depression Rating Scale (HAM-D), which are subjective. Objective: The purpose of the study is to propose a framework for more accurate detection of depression from EEG signals using only three channels, which makes the system portable as well as efficient. Methods: In this study, we propose a classification model using EEG signal with the help of an Adaptive Neuro Fuzzy Inference System optimized by a nature-inspired algorithm. The proposed model is efficient, accurate, and portable as the features are extracted from only three channels, namely, Fp1, Fp2, and Fz. The three Data Channel (3EDANFIS) Adaptive Neuro Fuzzy Inference System (ANFIS) for detection of depression as well as three variants of Hybrid ANFIS-Adaptive Neuro Fuzzy Inference System-Genetic Algorithm (ANFIS-GA), Adaptive Neuro Fuzzy Inference System-Particle Swam Optimization (ANFIS-PSO) and Adaptive Neuro Fuzzy Inference System-Firefly Algorithm (ANFIS-FA) has been analyzed in this study. The features extracted are delta, theta, alpha, and beta and their corresponding sub-bands delta1, delta2, theta1, theta2, al-pha1, alpha2, beta1, and beta2. Genetic Algorithm (GA), Particle Swam Optimization (PSO), and Firefly Algorithm (FA) are all nature-inspired metaheuristic algorithms that are used to optimize ANFIS by adapting the premise and consequent parameters. Results: The analysis showed that the GA and FA performed equally well in optimizing ANFIS with the highest accuracy of 83.33% using delta1 power as well as delta power. The overall accuracy of the ANFIS-GA was found to be higher than that of the ANFIS-PSO, ANFIS-FA, and

关键词： Genetic algorithms

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P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

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Intelligent Automation & Soft Computing 2023年第1期35卷 553-566页

作者： Ayman Altameem Ramesh Chandra Poonia Ankit Kumar Linesh Raja Abdul Khader Jilani Saudagar Department of Computer Science and Engineering College of Applied Studies and Community ServicesKing Saud UniversityRiyadh11533Saudi Arabia Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Engineering and Applications GLA UniversityMathuraUPIndia Department of Computer Application Manipal University JaipurRajasthan303007India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Data clustering is crucial when it comes to data processing and *** new clustering method overcomes the challenge of evaluating and extracting data from big *** or categorical data can be *** clustering methods favor numerical data clustering and ignore categorical data *** recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering ***,these algorithms could not use the concept of categorical data for *** that,suggestions for expanding traditional categorical data processing methods were *** addition to expansions,several new clustering methods and extensions have been proposed in recent *** is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster *** paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster *** parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also *** results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.

关键词： ROCK K-means algorithm clustering approaches unsupervised learning K-histogram

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