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2024 International Conference on computer Network Security and Software Engineering, CNSSE 2024

作者： Zhou, Yue Wu, Wenlong Information Engineering Department Anhui Industry Polytechnic Anhui Tongling244000 China Graduate School La Consolacion University Philippines Bulacan Malolos3000 Philippines Computer Science College Guangzhou College of Applied Science and Technology Guangdong Zhaoqing526072 China

ISBN: (纸本)9781510680234

The load balancing strategy in the traditional network architecture system is limited to the limitations of the closed operating system of the network device and cannot effectively meet various communication environments. In SDN, a new network architecture with open programming, the load balancing system architecture of the SDN data center is designed. The traffic classification module is used to distinguish large and small flows. A load balancing mechanism based on the improved ant colony optimization is proposed for the forwarding of large flows. A weighted ECMP load balancing mechanism is used for the forwarding of small flows. The improved ant colony optimization considers the difference in link load status and optimizes the parameters of the ant colony optimization. Through experimental simulation and comparison with traditional load balancing algorithms ECMP and DLB algorithms, the advantages of the optimized ant colony optimization in terms of delay jitter, average link utilization and other indicators are verified. © 2024 SPIE.

关键词： Ant colony optimization

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Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

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computers, Materials & Continua 2022年第1期70卷 2031-2047页

作者： A.Sheryl Oliver P.Suresh A.Mohanarathinam Seifedine Kadry Orawit Thinnukool Department of Computer Science and Engineering St.Joseph’s College of Engineering600119ChennaiIndia Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Faculty of Engineering Karpagam Academy of Higher EducationCoimbatore641021India Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and ***,these methods suffer from biased results and inaccurate detection of the ***,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT *** proposed methodology works on the basis of two stages such as pre-processing and ***,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT *** collected images are then preprocessed using Gaussian *** filter can be utilized in the removal of unwanted noise from the collected CT scan ***,the preprocessed images are sent to prediction *** this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed *** proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed ***,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or *** projected method was implemented in MATLAB and the performances were evaluated through statistical *** proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)*** results established the supremacy of the proposed model.

关键词： Deep learning deep dense convolutional neural network covid-19 CT images chimp optimization algorithm

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Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

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computers, Materials & Continua 2022年第11期73卷 2541-2554页

作者： K.Vijaya Kumar E.Laxmi Lydia Ashit Kumar Dutta Velmurugan Subbiah Parvathy Gobi Ramasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Christ UniversityBangalore560029India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of ManagementMoscow109542Russia

Recently,big data becomes evitable due to massive increase in the generation of data in real time ***,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,*** paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data *** AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT *** AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast *** addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature ***,softmax layer is applied to perform classification *** effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

关键词： Object detection tracking convolutional neural network inception v2 image annotation

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Evaluation of the Robustness, Transparency, Reliability and Safety of AI Systems 9

Evaluation of the Robustness, Transparency, Reliability and ...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Mahilraj, Jenifer Pandian, Manivel Subbiah, Muthuraman Kalyan, S. Vadivel, R. Nirmala, S. NPR College of Engineering Department of Computer Science and Engineering Tamilnadu India University of Technology and Applied Sciences Muscat Oman BVRIT Hyderabad College of Engineering for Women Bachupally Dept of Basic Sciences and Humanities-Management Studies Hyderabad India HKBK College of Engineering Department of Computer Science and Engineering Karnataka Bangalore India Sri Eshwar College of Engineering Department of Computer Science and Engineering TamilNadu Coimbatore India

ISBN: (纸本)9798350397376

Advances in artificial intelligence have been promising in recent years. AI-related activities such as international conferences, academic research, and technological challenges have proliferated over the globe. The technologies and applications are always being improved and expanded in the modern age. In addition, humans' studies, job, and personal lives have been greatly improved by the ongoing development of intelligent devices. Efforts have been dedicated to identify some of these issues and to present policymakers with possible remedies, which focus on reconciling the valid expectations of AI in terms of resilience and interpretability with the existing scientific environment of AI on these aspects. In this paper, the individual aims are to present an objective picture of the current environment of Artificial Intelligence (AI), emphasizing on the issues of robustness and explanability, transparency, reliability and safety. AI's present cybersecurity, security, and information security problems are discussed in detail, as well as the scientific approaches that are now being developed in the AI world to address these risks. © 2023 IEEE.

关键词： Reliability

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IoT Technologies for Precision Agriculture: A Survey 6

IoT Technologies for Precision Agriculture: A Survey

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Pyingkodi, M. Thenmozhi, K. Karthikeyan, M. Nanthini, K. Wilfried Blessing, N.R. Ajay Martin, A. Deepak, P.V. Jegan, K. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanthi College Department of Computer Science Bengaluru India University of Technology and Applied Sciences Department of IT Salalah Oman

ISBN: (纸本)9781665410281

Precision agriculture is gaining popularity in both commercial and research and development applications recent days. Plant management systems are constantly evolving, taking into account real-time or offline data collecting and processing in order to fully account for the geographical and temporal fluctuations of plant and soil components. This step can be achieved by lowering energy use, reducing the use of chemicals, fertilizers, and greenhouse gases (GHG), while simultaneously increasing output. To address the food and fibre needs of a rapidly rising population, sustainability is critical. With this in mind, the goal of this study is to not only present a complete picture, but also to elucidate the context by highlighting major scientific research and applications related to optoelectronics proximate sensors. Is to attempt to create a 360-degree vision. Agriculture. The reviews are divided into categories based on the most common sensor types and applications (grayscale / RGB imaging, Visible Near Infrared (VisNIR) and NIR, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Vegetation Index (NDRE) sensors, stereo). That's precisely the Point that the Vision, thermography, composite sensors, phenotypic sensors, and other technologies are being developed. Two distinct chapters on the use and development of specialised analytical methodologies, as well as term map analysis to visually identify major clusters based on certain subject areas are studied. © 2022 IEEE.

关键词： Remote sensing

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Sensor Based Smart Agriculture with IoT Technologies: A Review 12

Sensor Based Smart Agriculture with IoT Technologies: A Revi...

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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Pyingkodi, M. Thenmozhi, K. Nanthini, K. Karthikeyan, M. Palarimath, Suresh Erajavignesh, V. Kumar, G. Bala Ajith Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanthi College Department of Computer Science Bengaluru India University of Technology and Applied Sciences Department of It Salalah Oman

ISBN: (纸本)9781665480352

The IoT is a new technology trend used in almost every area thing, when connected to the internet and to each other, when you connect to the internet or interconnect, your entire system will be smarter. We have used IoT in all areas of our lives, including smart cities, smart homes, and smart retail. Much more. From 9.6 billion by 2050, agriculture needs to deliver even faster to meet this type of demand. This is possible with the latest technology, especially the IoT. The IoT enables labour free farms. Not only can it be used for large-scale agriculture, but it can also be used for livestock, greenhouse management, and agricultural land management. The most significant tool for the IoT is the sensor. A sensor is a device that collects important data that is interpreted to obtain the required analysis. The important objective of sensors are used to determine the soil's physical qualities and the environment. The main applications of sensors are control and supervise, safety, alarm, diagnostics, and analytics. Sensors make innovative agriculture more effective and trouble-free. In agriculture, the sensor is mainly used for measuring, measuring NPK (Nitrogen, Phosphorus, Potassium) levels, and detecting disease and soil moisture content. The main solution to this problem is smart farming, which modernizes traditional farming practices. This paper narrates the role of IoT application in smart agriculture. Smart farming is also known as precision farming hence it uses accurate information to draw outcomes. It demonstrates the different sensors, applications, challenges, strengths and weaknesses that support the IoT and agriculture. © 2022 IEEE.

关键词： Precision agriculture

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Application of Nanotechnology in Neural Growth Support System 2

Application of Nanotechnology in Neural Growth Support Syste...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Saha, Debasmita Rama Devi, G. Naga Ponnusamy, Sivakumar Pandit, Jyotsna Jaiswal, Sushma Bhuyan, Pranjit Kumar University of North Bengal Department of Computer Science West Bengal India CMR Institute of Technology Department of Computer Science & Engineering Hyderabad India Srm Institute of Science and Technology Department of Computer Science and Engineering Modinagar India Krishna Engineering College Department of Applied Science Ghaziabad India Bilaspur India Kaliabor College Department of Chemistr Nagaon India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Carbon nanotubes, having their capacity to transmit electric impulses, contact with neurons, & simulate the neuronal surroundings, have been appealing possibilities for the creation of scaffolding that could facilitate neuronal growth and division. The researchers created biodegradable composite scaffolding made up of inter carbon nanotubes disseminated in the poly-L-lactic acid substrate that could promote the growth and development of human brain cells. The researchers also created synthesized peptides generated from the L1 and LINGO1 enzymes to mimic the guidance inputs of the neuronal environment. Such peptides may greatly impact neural conversion, which would be enhanced synergistically by a conjunction of a nanocomposite scaffolding & also proteins, implying a model of the presence of long-neuronal growth factor implantation. © 2022 IEEE.

关键词： Carbon nanotubes

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Load Balancing in Cloud Computing based on Ant Colony Optimization and Crow Search Algorithm

Load Balancing in Cloud Computing based on Ant Colony Optimi...

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International Conference on Advanced Communication technology (ICACT)

作者： Amar N. Alsheavi Xingfu Wang Wei Zhao Ammar Hawbani Naji Alhusaini Ibrahim Abdulrab Ahmed A.S. Ismail School of Computer Science and Technology University of Science and Technology of China Hefei China School of Computer Science and Technology Anhui University of Technology Anhui China School of Computer Science Shenyang Aerospace University Shenyang China School of Computer and Information Engineering Chuzhou University Chuzhou China Computer Department Applied College Najran University Najran Saudi Arabia College of Life Sciences and Oceanography Shenzhen University Shenzhen China Faculty of Science Zagazig University Zagazig Egypt

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

With the rapid increase in cloud services and the increasing shift toward them, balancing the cloud load has become a critical research issue. The increasing demand from customers for technology services worldwide is largely due to its direct impact on performance quality. Therefore, to provide better service quality, it is essential to consider reducing response time and cost in load balancing. This paper addresses the load-balancing problem by optimizing response time and computing cost in cloud computing systems. Specifically, we first formulated the load balancing problem in cloud computing mathematically by designing an objective function that optimizes response time and cost, including constraints to ensure task assignment to a single virtual machine (VM) and resource limits are not exceeded. To solve this optimization problem, we proposed a hybrid algorithm, ACOCSA, that combines Ant Colony Optimization (ACO) and Crow Search Algorithm (CSA). We implemented the ACOCSA algorithm, evaluated the response time, cost, and load balancing metrics, and found that our algorithm performed better in terms of response time and computational cost, and showed good fairness among VMs.

关键词： Measurement Cloud computing Ant colony optimization Costs Processor scheduling Heuristic algorithms Quality of service Load management Virtual machines Time factors

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IoT, 6G and Digital Twin For Smart Campus 4

IoT, 6G and Digital Twin For Smart Campus

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4th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2023

作者： Pexyean, Tanapeak Saraubon, Kobkiat Nilsook, Prachyanun Division of Information and Communication Technology for Education Faculty of Technical Education King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Computer Science and Information Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350330618

Smart Campus This research aims to present the use of the Internet of Things and smart technologies that work with 6G networks to intelligently improve the energy management capabilities of campuses. Whether it's personal. Teachers and students, including those with disabilities, have a better quality of life. Within the university, energy consumption data is collected. Organizations to manage energy management systems Reduce energy consumption Reduce management by using technology to manage and reduce campus costs, transparently monitor energy consumption and use of campus educational equipment, especially cost and expense management, and sustainable energy use. Control usage data, energy consumption results, and estimate budget allocation related to educational institutions. IoT and Cloud Computing are also integrated into 6G networks embedded in state-of-the-art devices for use in environmental management and intelligent energy management, as well as creating new energy models and management that can solve future emergencies with external on-premises virtual energy control anytime, anywhere. Smart Building Management with twin technologies helps to intelligently manage and control indoor energy systems, such as automation for temperature control in buildings. Air conditioner energy consumption control system Electricity consumption control in lighting system This saves energy and increases the usability of buildings within smart campuses. ©2023 IEEE.

关键词： Energy utilization

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Clisp: A Robust Interactive Segmentation Framework for Pathological Images

Clisp: A Robust Interactive Segmentation Framework for Patho...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Hannan Wang Jun Shi Minfan Zhao Liang Qiao Zhaohui Wang Yue Dai Yuchen Ma Hong An School of Computer Science and Technology University of Science and Technology of China Hefei China School of Engineering & Applied Science University of Virginia Charlottesville USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Recently, automatic segmentation algorithms based on deep learning have achieved promising results on various pathological image segmentation tasks. However, the inherent data-hungry nature of these methods and the high cost of annotating pathological images make them difficult to apply to practical clinical tasks. To address this problem, many studies have explored interactive segmentation methods to reduce the cost of annotating pathological images, which replace pixel-by-pixel annotation operations with clicks or other user instructions. Despite their effectiveness, most of these methods only utilize small-scale data resulting in poor generalization ability for different tasks or segmentation objects. In this paper, we propose a robust Click-based Interactive Segmentation framework for low-cost and interactive annotation of Pathological images, named Clisp. To improve the generalization ability, we construct a multi-source pathological image annotation dataset including around 79,000 images for supervised training of the proposed method. Besides, we adopt a heavy-parameter vision transformer as the image encoder to learn feature representations for large-scale data. We evaluate the proposed method on 7 open-source datasets, and the experimental results show that Clisp is superior to the baseline method in segmentation performance on most datasets and has outstanding generalization ability.

关键词： Training Deep learning Image segmentation Pathology computer vision Costs Annotations Image annotation Transformers Engineering in medicine and biology

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