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3rd International Conference on advanced Computing and Applications, ICACA 2024

作者： Mandal, Diptarka Ray, Amartya Sarkar, Sujan Ojjela, Odelu Sarkar, Ram School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Maharashtra Pune411025 India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India

ISBN: (纸本)9789819747986

Skin cancer ranks among the deadliest forms of cancer worldwide, leading to numerous fatalities annually. Hence, the development of efficient computer-aided diagnosis (CAD) systems has become a pressing need to help medical professionals classify various types of skin cancer. This paper introduces a novel method which combines the outcomes of two deep learning models, leveraging the feature fusion strategy. Our approach utilizes Xception and Google’s Big Transfer (BiT-M) model as base learners, complemented by a squeeze and excitation attention module to enhance the fused feature maps. We also establish a combined loss function to optimize our model. We have evaluated the model on the challenging ISIC 2017 dataset and obtained an impressive accuracy of 0.795, surpassing the performance of many recently published methods within the literature. The source code of the proposed model is available at https://***/Cmatermedicalimageanalysis/ISIC2017. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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A Precise Model of Insect Flight Vitality and Development of Unmanned Micro Aerial Vehicle

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Journal of The Institution of Engineers (India): Series B 2021年第5期102卷 915-926页

作者： Anandan, R. Kalaivani, K. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies PallavaramTamil Nadu India

In recent times, scores of innovations are taking place in the field of unmanned aerial vehicle (UAV) technology that includes continuous development of design of on-board processing, memory, storage, and communication capabilities. Since UAVs are now being used extensively in many areas of expertise that include commercial, military, civilian, agricultural, and environmental applications, it is very essential that these UAVs communicate efficiently. The communication may be of the UAV-to-UAV (U2U) type or it could be with the already existing prevalent networking infrastructures UAV-to-Infrastructure (U2I) type of communication. A new prototype is developed based on the insect flight dynamism. These insect wings are very thin membranous structure. They are subjected to many forces and follow aerodynamic flying and hovering. The current research trend on this subject is the simulation of insect wing structure and utilization to create a prototype for the development of unmanned micro air vehicles. In the present study, three flying insects such as dragon fly, cicada, and jewel beetle are selected and the wing aerodynamic parameters are analyzed by mathematical models. The comparative scanning of the data revealed that cicada wing structure is their aerodynamic characteristics may serve as a prototype. The results are discussed with relation to the base data for the flapping-wing system in micro air vehicle. In this paper, we have tried to put forward an approach that is capable of providing real-time positioning and tracking of a UAV. The approach has three parts: tracking device, backend server, and mobile app. © 2021, The Institution of Engineers (India).

关键词： Aerodynamics

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X-ClusterLink: An Efficient Cross-Cluster Communication Framework in Multi-Kubernetes Clusters 25

X-ClusterLink: An Efficient Cross-Cluster Communication Fram...

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34th ACM Web Conference, WWW 2025

作者： Wang, Pengbo Zhao, Gongming Wu, Yuantao Xu, Hongli Wang, Haibo School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China University of Kentucky Department of Computer Science Lexington United States

ISBN: (纸本)9798400712746

Kubernetes is widely adopted by enterprises to enhance service availability for applications such as web services and large-scale model training, due to its advantages in managing containerized applications. As service demands increase, a single Kubernetes cluster often becomes insufficient, leading to the trend of using multiple clusters to improve service scalability. However, achieving efficient cross-cluster communication poses significant challenges due to the need for low latency, high throughput, and strong robustness. Existing methods for cross-cluster communication either employ a centralized control plane, which becomes a communication bottleneck, or use numerous service-bound proxies, leading to increased management complexity and possibly compromised robustness in cross-cluster communication. To address the above challenges, we introduce X-ClusterLink, a framework designed for efficient cross-cluster communication in multi-Kubernetes clusters. X-ClusterLink first employs broker clusters to ensure low-latency cross-cluster synchronization. Then, it aggregates multiple containerized gateways to enhance throughput and leverages eXpress Data Path (XDP) for advanced packet processing, thereby accelerating traffic forwarding. Finally, it incorporates Bucket-Based Consistent ECMP to facilitate seamless failover and enhance robustness. Experimental results demonstrate that X-ClusterLink significantly improves cross-cluster communication efficiency, increasing cross-cluster forwarding bandwidth by 3.1 × compared to existing solutions. © 2025 Copyright held by the owner/author(s).

关键词： Resource allocation

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DRL-Driven Localization With UAV in Near-Field Communications

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IEEE Internet of Things Journal 2025年第13期12卷 22587-22598页

作者： Khan, Muhammad Fawad Peng, Limei Ho, Pin-Han Chen, Yuguang Dong, FangJie Kyungpook National University School of Computer Science and Engineering Daegu41566 Korea Republic of University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Guangdong China University of Waterloo Department of Electrical and Computer Engineering ON Canada Shenzhen Unicom Innovation Business Capability Center China National Health Commission's Center for Statistics and Information China

In this paper, we propose a Deep Reinforcement Learning (DRL)-based Multi-Point Localization Scheme (MLS) to efficiently localize IoT devices using a single Unmanned Aerial Vehicle (UAV) equipped with a large-scale multi-antenna configuration in near-field communication (NFC). By utilizing the spherical wave-based near-field steering vector, the multi-antenna array on the UAV captures both the Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI) measurements from IoT devices to estimate their locations relative to the position of the UAV. This approach eliminates the need for multiple hovering points required by a single-antenna UAV (SA-UAV) or the deployment of multiple SA-UAVs. To enhance localization accuracy, key hovering points for the multi-antenna UAV (MA-UAV) are strategically selected, with weights assigned based on signal strength to prioritize stronger and more reliable signals. Furthermore, DRL dynamically adjusts the position of the MA-UAV to optimize the trade-off between localization accuracy and energy consumption. Extensive simulations conducted across rural, urban, and dense urban scenarios demonstrate that the proposed DRL-based MLS significantly improves localization accuracy while reducing the energy consumption of the UAV. © 2025 IEEE.

关键词： Deep reinforcement learning

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Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

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computer Systems science & Engineering 2022年第6期41卷 891-902页

作者： Parul Gandhi Mohammad Zubair Khan Ravi Kumar Sharma Omar H.Alhazmi Surbhi Bhatia Chinmay Chakraborty Department of Computer Applications Manav Rachna International Institute of Research and StudiesFaridabad121006India Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia Department of Computer Applications Chandigarh Group of CollegesLandran140307India College of Computer Sciences and Information Technology King Faisal UniversitySaudi Arabia Department of Electronics&Communication Engineering Birla Institute of TechnologyMesra JharkhandIndia

Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.

关键词： Software quality reliability neural networks fuzzy logic neuro-fuzzy inference system

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Object Tracking in Intelligent Video Surveillance System Based on Artificial System 1

Object Tracking in Intelligent Video Surveillance System Bas...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Choubisa, Mukesh Kumar, Vijay Kumar, Mukesh Khanna, Samrat Institute of Advanced Research Department of Computer Science and Engineering Gujarat Gandhinagar India Ministry of Electronics & It Indian Computer Emergency Response Team Delhi India

ISBN: (纸本)9798350338027

Object moving tracking is one of the furthermost attractive and critical chores in computer science and computer vision research domain. Object moving tracking is underpinned for various higher-level automated applications in different research domains such as surveillance of video, motion capture and Augmented Reality (AR) in computer vision. Object tracking is the significant section of an intelligent video surveillance system (IVS) which can be further used for finding many other apprehensive or suspicious activities. Through the independent and exhaustive research of the current object motion and moving tracking technology, collective with hands- on application of IVS (intelligent video surveillance). In computer science many different algorithms available for object path tracking, object tracking and object motion detection, but mean shift algorithm (MSA) is the most popular and basic approach. We use mean shift algorithm (MSA) for our research work. The proposed work in this paper with following objectives, a) Video series frame production for tracking of a moving body, b) Design of experiments for object detection, c) Formulation and implementation of an algorithm to detect object *** algorithm was applied on an image frame of a recorded video. Based on multiple input images, we were able to detect the object and track its successive image frames. The realistic execution may want to obtain the object body tracking without losing any input frame and could magnificently coat bounding field. After completion of the mean shift algorithm (MSA) it would be able to produce an image sequence. The maneuver and precision of the algorithm was tested by successfully overlay function in the identified object and user defined object. © 2023 IEEE.

关键词： Augmented reality

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Radial Basis Function Networks for Image Restoration with Stochastic Normalizations as Bayesian Learning in Deep Conventional Neural Network 6

Radial Basis Function Networks for Image Restoration with St...

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

作者： Senthilkumar, V. Jayalakshmi, V. Vels Institute of Science Technology and Advanced Studies Dept. of Computer Science Chennai India

ISBN: (纸本)9781665410281

The objective of the image restoration process is to improve the high-quality visual resolution from degraded images using various techniques in Artificial Intelligence., utilizing a normalization method, a method of centers and weights that has been adapted from the Radial Basis Function Network (RBFN) is initiated to the Stochastic Normalizations method as a Bayesian Learning algorithm. The normalized form of the RBFN-SB algorithm is derived, and the RBFN input data vector is from a damaged image array raised using Taylor's expansion. Following the dynamics of the normalized technique appeared to be tactful in comparison with the RBFN-SB. The test performance comparable to BN and, concomitantly, better validation losses suitable for ensuring results unpredictability estimation through approximate Bayesian posterior are procured. To test the outcome of the proposed system gave speed up the learning process, with minimum mean squared error (MSE) and produced better results of restoration images from damaged images. In the same form, the normalization accuracy can also be improved notably by the Bayesian learning algorithm. © 2022 IEEE.

关键词： Radial basis function networks

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Metric Identification of Vertices in Polygonal Cacti

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computer Modeling in Engineering & sciences 2023年第7期136卷 883-899页

作者： Xiujun Zhang Muhammad Salman Anam Rani Rashna Tanveer Usman Ali Zehui Shao School of Computer Science Chengdu UniversityChengduChina Department of Mathematics The Islamia University of BahawalpurBahawalpurPakistan Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya UniversityMultanPakistan Institute for Intelligent Information Processing South China Business College of Guangdong University of Foreign StudiesGuangzhouChina

The distance between two vertices u and v in a connected graph G is the number of edges lying in a shortest path(geodesic)between them.A vertex x of G performs the metric identification for a pair(u,v)of vertices in G if and only if the equality between the distances of u and v with x implies that u=v(That is,the distance between u and x is different from the distance between v and x).The minimum number of vertices performing the metric identification for every pair of vertices in G defines themetric dimension of *** this paper,we performthemetric identification of vertices in two types of polygonal cacti:chain polygonal cactus and star polygonal cactus.

关键词： Metric metric identification metric generator metric dimension cactus graph

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A hybrid technique for selection and minimization of test cases in regression testing

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Recent Advances in computer science and Communications 2021年第2期14卷 477-488页

作者： Singh, Leena Singh, Shailendra N. Dawra, Sudhir Department of Computer Science and Engineering Amity University Noida India Department of Computer Application Tecnia Institute of Advanced Studies New Delhi India

Background: In today’s era, modifications in a software is a common requirement by customers. When changes are made to existing software, re-testing of all the test cases is required to ensure that the newly introduced changes do not have any unwanted effect on the behavior of the software. However, re-testing of all the test cases would not only be time consuming but also expensive. Therefore, there is a need for a technique that reduces the number of tests to be performed. Regression testing is one of the ways to reduce the number of test cases. Selection technique is one such method which seeks to identify the test cases that are relevant to some set of recent changes. Objective: It is evident that most of the studies have used different selection techniques and have focused only on one parameter for achieving reduced test suite size without compromising the performance of regression testing. However, to the best of our knowledge, no study has taken two or more parameters of cov-erage, and/or execution time in a single testing. This paper presents a hybrid technique that combines both regression test selection using slicing technique and minimization of test cases using modified firefly algorithm with combination of parameters coverage and execution time in a single testing. Methods: A hybrid technique has been described that combines both selection and minimization. Selection of test cases is based upon slicing technique while minimization is done using firefly algorithm. Hybrid technique selects and minimizes the test suite using information on statement coverage and execution time. Results: The proposed technique gives 43.33% much superior result as compared to the other hybrid approach in terms of significantly reduced number of test cases. It shows that the resultant test cases were effective enough to cover 100% of the statements, for all the programs. The proposed technique was also tested on four different programs namely Quadratic, Triangle, Next day, Co

关键词： Testing

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Novel Lung Cancer Classification and Segmentation Algorithm with Web UI Interface 6

Novel Lung Cancer Classification and Segmentation Algorithm ...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Divya, M. Sathya, S. Vels Institute of Science Technology and Advanced Studies Hcas Department of Computer Science Erp Department India Vistas Department of Bca and It India

ISBN: (纸本)9781665482714

For humans, it is difficult and time-consuming to use Computed Tomography (CT) pictures to identify lung illnesses. This research work develops a novel algorithm for segmenting the cancer part in the lung and an algorithm is enhanced to classify the lung cancer images. MediNet is a segmentation-based algorithm, used to segregate the cancer-affected region from an unaffected region in the CT image whereas LuNet is the algorithm developed by enhancing an algorithm to achieve the classification of the type of cancer. This MediNet algorithm involves layers such as thirty convolutional layers, three maxpooling layers, and three bi-directional convolutional layers. By obtaining 97% accuracy, MediNet outperformed other models. Besides this, a web application is also developed to assist the doctor with a UI interface. This is developed in such a way that the doctor can upload a cancerous or non-cancerous scan in the application which will be linked to the developed classification and segmentation algorithm Medinet and LuNet. Thebackend will be running in a server that will take the input from the web app and process the output and return back the results to the web application in terms of the presence or absence of cancer as well as segmented cancerous areas in case of presence of cancer. © 2022 IEEE.

关键词： Diseases

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