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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Yadav, Surendra Dwivedi, Rakesh Kumar Gobi, N. Vivekananda Global University Department of Computer Science & Application Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

this study appears to use self-supervised transfer mastering for sturdy scientific photo classes. Switch getting to know is a powerful approach for enhancing the accuracy of deep mastering fashions in scientific imaging. This paper investigates using self-supervised getting-to-know techniques for scientific picture classes within characteristic-based procedures. By leveraging self-supervised schooling strategies, consisting of contrastive mastering, distributed representations, clustering, pseudo-venture gaining knowledge of, and self-supervised multi-undertaking gaining knowledge of, the proposed technique can learn representations that are extra sturdy to the area shift of various clinical imaging datasets. Experiments performed on an extensive x-ray and ultrasound snapshots dataset reveal that the proposed approach affords extra improvement in type accuracy compared to traditional feature-primarily based techniques. © 2024 IEEE.

关键词： classification clustering greater representations training

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Accelerating local SGD for non-IID data using variance reduction

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Frontiers of computer Science 2023年第2期17卷 73-89页

作者： Xianfeng LIANG Shuheng SHEN Enhong CHEN Jinchang LIU Qi LIU Yifei CHENG Zhen PAN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China Ant Financial Services Group Hangzhou 310000China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China

Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and ***,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence *** this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous *** extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence ***,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite *** from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical *** conduct experiments on three machine learning *** experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.

关键词： distributed optimization variance reduction local SGD federated learning non-IID data

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Deep Learning-Based Plant Leaf Disease Detection Using Scaled Immutable Feature Selection Using Adaptive Deep Convolutional Recurrent Neural Network

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SN computer Science 2023年第5期4卷 1-11页

作者： Jayashree, S. Sumalatha, V. Department of Computer Application Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Computer Science Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

Agriculture provides food and raw materials that are the bedrock of all nations. It is very essential to humans as a source of food. Plant diseases are major factors that affect the yield and quality of the plant. Digital image processing can determine plant diseases. Prediction of plant diseases has been implicated in the early stages of the agricultural sector. Thus, existing image process techniques employed plant disease features manually which is inefficient and results in poor recognition accuracy. This is often time-consuming and can result in poor precision and reproducibility. To tackle this issue, we introduce a proposed Hyper-Spectral Immutable Scaled Feature Selection (HSISFS) using Adaptive Deep Convolutional Recurrent Neural Network (ADCRNN) for leaf disease identification. It starts by pre-processing and scaling the region of plant leaf features to normalize the image pixel by reducing the noisy image based on Sampling Subset Feature Filtering (S²F²). Then, Ada Boosting Region-Based Segmentation (ABRS) technique employs for segregating leaf region parts. Based on the segmentation, the image analyzes the nearest maximum feature weight using Spider Optimization-Based Maximum Features Weight (SO-MFW). After that, the HSISFS method selects the plant leaf disease’s finest features. Finally, the ADCRNN algorithm with softmax logical activate function categorizes the leaf disease based on the finest features. Thus, the proposed produced high classification and detection accuracy, precision, recall, and F-measure performance, with a low false rate. The classification accuracy performance results are superior than the existing methods. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Classification Feature selection Filtering Image processing Plant leaf disease RNN Segmentation

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Evolution of AVS video coding standards: twenty years of innovation and development

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Science China(Information Sciences) 2022年第9期65卷 50-73页

作者： Siwei MA Li ZHANG Shiqi WANG Chuanmin JIA Shanshe WANG Tiejun HUANG Feng WU Wen GAO Institute of Digital Media School of Computer Science Peking University Bytedance Inc. Department of Computer Science City University of Hong Kong CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China

Ever since the founding of the Audio and Video Coding Standard(AVS) Workgroup of China in 2002, it has been dedicated to advancing and innovating the digital audio-video industry with highly efficient and economical encoding/decoding technologies. Three representative generations of video coding standards have been finalized and published, consistently improving the coding performance in the past two *** series of AVS standards establish solid foundations for ubiquitous video applications in the areas including acquisition, coding, production, delivery, integrated system, public service, general screen content, and mixed reality media. Along with the standardization process, an extensive amount of studies have been carried out on efficiency-aware designation, algorithm optimization, and hardware implementation of these innovative video coding techniques. This paper explains how those developed techniques provide a lasting impact on the video coding community, extensively, technologically, and systematically. In particular, we provide a comprehensive survey of the three generations of the standards, and timely and in-depth summarize the efforts of the AVS video coding standards in the twenty years. The rate-distortion performance comparisons,in particular in terms of the 8K ultra-high-definition(UHD) contents, reflect the elegant design of the state-of-the-art AVS3 standards. We have also elaborated on a variety of well-established and promising applications, including commercial level real-time 8K encoder, high-frame-rate decoder chip for cell phone,and live streaming solution for sports. In addition, the China Central Television(CCTV) of China Media Group(CMG), the state television of China, has officially launched the first 8K broadcasting channel(CCTV-8K) since 2021 using AVS3. Given the significant success realized by the AVS standards, it is envisioned that a new era of 8K UHD video is arriving.

关键词： video coding AVS standards video applications intelligent coding

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Understanding the Limitations of Authentication Protocols Employed by Existing Information Security Models for Networked applications

Understanding the Limitations of Authentication Protocols Em...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shree, Ritu Kumar, Vivek Prabhu, A. Vivekananda Global University Department of Computer Science & Application Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

The potential to comfortable systems and networks from unauthorized get right of entry is essential for any virtual community. Authentication protocols are a cornerstone to protecting such structures from malicious actors and imposing facts security. Despite the myriad of authentication protocols, however, there are boundaries to their efficacy in nicely securing digital networks. Authentication protocols, even those strongly encouraged through protection professionals, have recognized flaws and weaknesses which can be exploited by means attackers-resulting in violations of confidentiality, integrity, and availability of machine and application facts. Further, authentication protocols can be too hard to apply and cumbersome to maintain, main customers to bypass them-setting structures at risk and exposing them to capability breaches. This paper intends to research the strengths and weaknesses of authentication protocols and endorse techniques to maximize their effectiveness while deliberating applicability, usability, practicality, and cost. We can inspect adequate controls that can be used to bolster authentication protocols hired in existing facts protection models, as well as talk about the need for advanced authentication protocols and verification techniques. Authentication protocols are used in records protection fashions to offer cozy get right of entry to networked applications. Those protocols have to offer sturdy identification assurance and securely authenticate each gets the right of entry to try. This summary discusses the restrictions of the authentication protocols presently employed by current statistics protection fashions. Authentication protocols rely on cryptography and get admission to manipulate mechanisms to ensure that the most straightforward authenticated users gain access to sources. A few of the authentication protocols currently in use, including fundamental username and password authentication, are often a problem to assault due to thei

关键词： authentication community exploited implementation protocols

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Enhancing Automated Industrial Guiding Through computer Vision-Based Detection of Centerlines and Patterns

Enhancing Automated Industrial Guiding Through Computer Visi...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumari, Neeraj Ojha, Ananta Goyal, Manish Kumar Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This paper gives a method for enhancing automatic industrial guiding via pc vision-based detection of centerlines and patterns. The principal intention of this painting is to utilize vision algorithms to offer a reliable steering system for commercial robots. The technique combines numerous vision-primarily based detection techniques that allow you to come across the favored center lines and patterns. The consequent steerage device is then examined with actual robot experiments for validation. Moreover, a novel metric for measuring the accuracy of the detection device is also proposed. The effects show that the proposed technique gives a dependable answer for automatic commercial guiding. This paper discusses an automated business steerage technique, which leverages laptop imagination and is prescient to become aware of centerlines and patterns in business additives. This method uses a Convolutional Neural community (CNN) to discover those strains and patterns. To carry out detection, a CNN is educated on a fixed of photographs containing centerlines or styles and corresponding ground reality that is manually annotated. The effects of the CNN are used to guide robots to their favored function and become aware of element edges. © 2024 IEEE.

关键词： algorithms commercial experiments misalignments robots

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CLAACS-IOD: Certificate-embedded lightweight authentication and access control scheme for Internet of Drones

CLAACS-IOD: Certificate-embedded lightweight authentication ...

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作者： Sadhukhan, Dipanwita Ray, Sangram Dasgupta, Mou Rodrigues, Joel J. P. C. Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla India Department of Computer Application National Institute of Technology Raipur Raipur India Qingdao China Instituto de Telecomunicações Covilhã Portugal

In recent era, the unmanned aerial vehicles (UAVs) commonly known as drones has emerged as a one of the most significant and promising tools which has demonstrated its wide range of implementations variating from commercial domain to the field of defense due to its distinct capabilities such as inspection, surveillance, precision and so forth. Internet connected drones provides a propitious trend that boosts the flying safety, and service qualities of the UAVs where numerous low-altitude drones winged in different flying regions for executing a precise task such as gathering the real-time information from the unuttered environment to be interpretated by users. Nevertheless, the open-access insecure communications in hostile environment, the issues like safety and confidentiality threats, various security concerns such as the leakage of flying courses, identities, position, and gathered data by the drones are upstretched. To address these security concerns, access control mechanism provides a potential service in terms of authentication and key agreement for securing the communication between the individual drones within their respective flying regions. This article introduces a robust, efficient, lightweight, and privacy preserving ECC integrated access control approach by employing digital certificate with considering the high dynamicity and mobility of the drones. The designing of this proposed approach, that is, CLAASC-IoD is influenced by the aim of inter-drone and drone-to-ground station communication in the IoD paradigm. The detailed analysis of security using probabilistic random oracle model as well as simulation using well-accepted security verification tool AVISPA and comparative performance evaluation supports the claim of robustness, effectiveness, and proficiency. © 2023 John Wiley & Sons Ltd.

关键词： Internet of things

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LSB based Image Steganography with Password-Protected SMTP Transmission 2

LSB based Image Steganography with Password-Protected SMTP T...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Prakash, Mangali Bhanu Kumar, E. Ravi Anitha, Banavath Bansilal, Boda Mukkamall, Babu Reddy Sanjana, Gudipati Pranaya Vardhaman College Of Engineering Dept. of Information Technology Hyderabad India Krishna University Machilipatnam Dept. of Computer Application Machilipatnam India

ISBN: (纸本)9798350388916

Picture steganography plays a crucial role in secure communication by discreetly altering images for various purposes such as data authentication, privacy protection, and watermarking. However, traditional methods often lack resistance to manipulation. To address this, we propose a solution that merges automated password generation with the Least Significant Bit (LSB) approach. This method involves embedding text within images and automatically generating an encryption password to safeguard data security. By employing Simple Mail Transfer Protocol (SMTP) credentials, encrypted photos can be directly sent to recipients, significantly expediting the process. This automated approach eliminates the necessity for separate transfers of the image and decryption password, thereby enhancing simplicity and security. This new technology seamlessly integrates encryption, ensuring a trustworthy and secure communication channel. With this innovative solution, users can transmit sensitive information confidently, knowing that it is shielded from unauthorized access or manipulation. By combining the strengths of automated password generation, LSB steganography, and SMTP transmission, our approach provides a robust framework for safeguarding digital communication in various context. © 2024 IEEE.

关键词： Authentication Protocol

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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Accurate Diagnosis by Magnetic Resonance Imaging Using Deep Learning

Accurate Diagnosis by Magnetic Resonance Imaging Using Deep ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Rengarajan, A. Ahmed, Zahid Pandey, Rajendra P. Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

deep mastering has revolutionized the field of scientific imaging, providing practical affected person prognosis competencies. A current example is deep studying for Magnetic Resonance Imaging (MRI) acquisition, reconstruction, segmentation, and interpretation. Deep mastering-primarily based strategies leverage convolutional neural networks (CNNs) to automatically classify and section not unusual disease areas in MRI scans, allowing particular and correct prognosis. Those computerized strategies can potentially conquer the bottleneck of the complex work-intensive guide segmentation and visualization procedure. Moreover, they can provide an extra complete assessment of complex sicknesses. Through incorporating both the semantic and spatial information of image information, the overall performance of deep-gaining knowledge of-based totally structures for MRI evaluation has dramatically progressed. Moreover, CNN-based total MRI segmentation has proven promise in targeted and effective remedies for numerous diseases, including mind tumors, stroke, and dementia. The demonstration of superior effects in segmentation and classification tasks, in terms of accuracy and efficiency, shows the potential of deep learning-based techniques as an effective device within the automation of MRI analysis. © 2024 IEEE.

关键词： classification demonstration performance prognosis visualization

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