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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sharma, Priya Dwivedi, Shweta Bhati, Nitesh Singh Kumar, Santosh Sharda University Dept. of Computer Science Greater Noida India Integral and Innovative of Sustainable Education College Dept. of Information Technology Lucknow India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Because smart devices are so common and have generated enormous volumes of data, a new strategy is required to handle the kinds of data known as big data in the modern world. Many people are quite concerned about security because of the size of big data. Therefore, encryption is the best way to secure data;nevertheless, typical procedures are insufficient to fully protect huge data sets. Therefore, specific, and effective security algorithm solutions are required to protect large amounts of data. It becomes problematic when a sizable amount of the data is not encrypted and there is uncertainty about the data. Therefore, a cross-layer security architecture is offered in this research to secure the important component of the data. Numerous data security approaches are employed to safeguard data, ensure that crucial information is not lost, and encrypt it using a robust encryption algorithm. Based on encryption time and throughput, a comparison is conducted between the cross-A-coin approach and the suggested framework. Throughput is used to calculate performance based on encryption time, followed by full encryption. © 2024 IEEE.

关键词： Big Data Cross-Layer Techniques Security Algorithm and Big Database Reliability

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A Method for Plant Diseases Detection Based on Transfer Learning and Data Enhancement 4

A Method for Plant Diseases Detection Based on Transfer Lear...

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4th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2022

作者： Liu, Jing Zhang, Guofeng Feng, Bin Hou, Yingkun Kang, Wendi Shen, Boyang Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China School of Information Science and Technology Taishan University Tai'an China

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

With the wide application of deep learning in the field of agriculture, how to quickly and accurately identify and process structured and unstructured monitoring data to support intelligent decision-making has become an important research direction in the field of intelligent agriculture. In this paper, the multiple diseases of wild and introduced flowers such as canker, gray mold, brown spot and leaf blight were taken as the research objects, and the disease samples were collected according to the growth cycle of flowers, so as to construct a data set of flower diseases and insect pests under natural scenes. Based on Faster-RCNN framework, a three-stage pest and disease detection model PD-IFRCNN is proposed, which integrates transfer learning and data enhancement technology. In order to ensure the effectiveness of the model, this paper verifies the influence of the disequilibrium of the disease category of the self-built data set on the cost of wrong classification from two different perspectives: category balancing and label balancing, and makes a comparative analysis with the SSD model that takes VGG16 as the feature to extract the network. Experiments show that the proposed method can adapt to the detection of diseases with different scales and multiple leaves in natural scenes, and has high recognition accuracy. The proposed method not only breaks the traditional manual detection method of plant diseases, but also provides a new research idea for the recognition of plant diseases. It fundamentally solves the problems such as the lack of pertinence of traditional disease control and the lack of timely control caused by the inconveniences of manual detection due to bad weather and poor operating environment. © 2022 IEEE.

关键词： Classification (of information)

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A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4861-4875页

作者： Yang, Shangshang Tian, Ye He, Cheng Zhang, Xingyi Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR Department of Computer Science University of Surrey Guildford U.K

It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.

关键词： Artificial neural networks Training Optimization Search problems Scalability Complexity theory Genetic algorithms Deep neural networks (DNNs) evolutionary algorithm (EA) genetic operator gradient descent sparsity

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Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering

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IEEE Access 2023年 11卷 60681-60692页

作者： Khan, Mohammad Ahmar Khan, Ghufran Ahmad Khan, Jalaluddin Anwar, Taushif Ashraf, Zubair Atoum, Ibrahim A. Ahmad, Naved Shahid, Mohammad Ishrat, Mohammad Alghamdi, Abdulrahman Abdullah Dhofar University College of Commerce and Business Administration Salalah211 Oman Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur522502 India Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China GLA University Department of Computer Engineering and Applications Uttar Pradesh Mathura281406 India AlMaarefa University College of Applied Sciences Department of Computer Science and Information Systems Riyadh13731 Saudi Arabia National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Shaqra University College of Computing and Information Technology Shaqra11961 Saudi Arabia

Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation. We consider the importance of each view by assigning the weight control factor. We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange's Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority. © 2013 IEEE.

关键词： Data mining

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Wireless Sensor Networks: Revolutionizing Explosive Detection 5

Wireless Sensor Networks: Revolutionizing Explosive Detectio...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sujith Kumar, P.S. Anand, C. Vasuki, N. Ramesh, V. Santhoshkumar, S.P. Sree Buddha College of Engineering Department of Computer Science and Engineering Kerala Alappuzha India Nandha College of Technology Department of Information Technology Erode India Government College of Engineering Department of Computer Science and Engineering Erode India School of Computing Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India

ISBN: (纸本)9798331518097

One of the important technologies for protecting civilian life from terrorist attacks is explosive detection techniques. The global incidence of terrorist activities has been considerably reduced by the development of explosive detection techniques, which has been made possible by ongoing technology improvements. Wireless sensor networks (WSNs) are one of these developments that has become an innovative tool for explosives detection. This work explores recent empirical research on the application of WSNs to explosives detection and includes statistical analysis of the investigations. Wireless sensor network with compatible specialized sensors is required for effective explosive detection. Three main wireless sensor system aspects that are relevant to explosives detection are covered in this research. It first looks at how scalable wireless sensors are in terms of explosives detection technologies. In the second section, it looks at the mobility and communication capabilities of WSNs and sensors in explosives detecting systems. Finally, it highlights potential hazards and weaknesses and addresses the crucial topic of data security inside wireless sensor networks used for explosive detection. The study also suggests incorporating drone assisted detection as a novel way to improve the coverage and effectiveness of explosives detection systems, adding to the continuous advancement of security technologies meant to shield civilian populations from terrorist threats. © 2024 IEEE.

关键词： Terrorism

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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DDoSNet: A Deep Learning Model for detecting Network Attacks in Cloud computing 4

DDoSNet: A Deep Learning Model for detecting Network Attacks...

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4th International Conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Srilatha, Doddi Thillaiarasu, N. School of Computing and Information Technology Reva University Bengaluru560064 India Sreenidhi Institute of Science and Technology Department of Cse Hyderabad501301 India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Cloud services are the major target for malicious attacks as more organizations and individuals shift to the cloud. When DDoS assaults pose one of the most significant dangers to Internet users and cloud services, this is especially important. As a result of DDoS assaults, cloud services cannot transmit optimum network infrastructure and sensitive information. Due to the flexibility of cloud computing, DDoS mitigation techniques in the cloud are significantly different from those used in conventional networks. When it comes to DDoS assaults, this article examines their influence on cloud resources and proposes a realistic intrusion detection system (IDS) referred to as DDoSNet. To evaluate models, this research study utilizes the CICIDS 2017 dataset. The intelligence approach of particle swarm optimization (PSO) is applied for feature selection. The optimal features are given input to the deep neural network (DNN) model to classify the given data as malicious or benign. The accuracy rate, recall, precision, and F1-score metrics are considered for model evaluation. The experimental results show an accuracy of 099.81%, precision of 099.77%, recall of 099.89% & F1 score of 099.83%. The presented system outperforms the conventional machine learning models. Hence, this model imparts huge confidence in providing security to cloud services. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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Simulating a Finite-State Markov Chain for Rayleigh Block-Fading Channel based on its Mathematical Model, and Practical Representation 25

Simulating a Finite-State Markov Chain for Rayleigh Block-Fa...

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25th International Arab Conference on information technology, ACIT 2024

作者： Attar, Hani Ababneh, Jafar Al-Hihi, Mohammad Rachini, Ali Solyman, Ahmed Zarqa University Faculty of Engineering Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan Erlangen Germany Department of Computer Science and Information Technology Jounieh Lebanon Glasgow Caledonian University School of Computing Engineering and Built Environment Glasgow United Kingdom

ISBN: (纸本)9798331540012

In the proposed paper, simulating the Finite State Markov Chain (FSMC) to be implemented as Rayleigh Block-fading channel. Indeed, FSMC is regarded as a practical fading channel because of its random choice for future transmission probability. Accordingly, this paper introduces the FSMC technology with the theoretical limits and equations;moreover, the uplink and downlink Rayleigh Block-fading channel scenarios are also investigated and supported by the simulation set-up. The results for the Rayleigh fading channel for the Long-Term Evaluation-Advanced network were obtained as an example of the channel's attitude. The findings demonstrate that FSMC provides such reliable results and is suitable for use as a practical fading channel, particularly in terms of burst error behavior. © 2024 IEEE.

关键词： Markov chains

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Software Security Measurements: A Survey

Software Security Measurements: A Survey

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2022 International Conference on Intelligent technology, System and Service for Internet of Everything, ITSS-IoE 2022

作者： Almogahed, Abdullah Omar, Mazni Zakaria, Nur Haryani Alawadhi, Abdulwadood Taiz University Faculty of Engineering and Information Technology Department of Software Engineering Taiz Yemen Universiti Utara Malaysia School of Computing Sintok06010 Malaysia University of Science and Technology Faculty of Engineering and Computing Department of Computers and Information Technology Yemen

ISBN: (数字)9781665459983

ISBN: (纸本)9781665459983

Security metrics for software products give a quantifiable assessment of a software system's trustworthiness. Metrics can also help detect vulnerabilities in systems, prioritize corrective actions, and raise the level of information security within the business. There is a lack of studies that identify measurements, metrics, and internal design properties used to assess software security. Therefore, this paper aims to survey security measurements used to assess and predict security vulnerabilities. We identified the internal design properties that were used to measure software security based on the internal structure of the software. We also identified the security metrics used in the studies we examined. We discussed how software refactoring had been used to improve software security. We observed that a software system with low coupling, low complexity, and high cohesion is more secure and vice versa. Current research directions have been identified and discussed. © 2022 IEEE.

关键词： Network security

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Fog based Edge Learning using IoT: Concepts, Challenges and Applications 4

Fog based Edge Learning using IoT: Concepts, Challenges and ...

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4th International Conference on Smart Systems and Inventive technology, ICSSIT 2022

作者： Sindhu, V. Sudhakaran, Pradeep Anitha, G. Mahendiran, P.D. Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu India Department of Information Technology Sri Shakthi Institute of Engineering and Technology Coimbatore India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India

ISBN: (纸本)9781665401180

In innovation world, fog and edge computing is extremely near the client which means making the processing capacities extremely nearer to the customer or user. The mushrooming of IoT devices and usage is increasing day by day. Huge amount of data will be generated by these connected devices. Thus, there is an indispensable need to process such heavy volume of data in a quick and efficient manner. In request to meet the above standards. In this paper the research challenges of fog and Edge computing, the role of fog computing in IoT, the working model and benefits of edge computing are given in a novel approach after a de tailed review, study and scrutiny. This enables and paves way to the researcher for a smoother and sustainable research scope in the fog and edge computing environment. © 2022 IEEE

关键词： Fog computing

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