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Efficient Optimal Routing Algorithm Based on Reward and Penalty for Mobile Adhoc Networks

computers, Materials & Continua 2023年第4期75卷 1331-1351页

作者： Anubha Ravneet Preet Singh Bedi Arfat Ahmad Khan Mohd Anul Haq Ahmad Alhussen Zamil S.Alzamil Department of Computer Applications I.K.G.Punjab Technical UniversityKapurthala144601PunjabIndia Research and Development I.K.G.Punjab Technical UniversityKapurthala144604PunjabIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of *** main challenges are related to routing techniques that are generally employed in *** Adhoc system management,on the other hand,requires further testing and improvements in terms of *** routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two *** main aim of this research work is to determine the optimum method for sending packets while also extending life time of the *** is achieved by changing the residual energy of each network ***,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are ***,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of *** simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.

关键词： Routing optimization reward penalty mobility energy throughout pheromone

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A Novel Approach for Photo-Realistic High Quality Sign Language Video Synthesis 3

A Novel Approach for Photo-Realistic High Quality Sign Langu...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Pravine Mukesh, R. Natarajan, B. Elakkiya, R. Vaidhya, G.K. Murali, P. Geetha, K. Amrita Vishwa Vidyapeetham Chennai601103 India School of Computer Science and Engineering Vit University Tamilnadu Chennai India Birla Institute of Technology and Science Department of Computer Science Dubai United Arab Emirates Srm Institute of Science and Technology Vadapalani Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Tamilnadu Chennai India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai601103 India

ISBN: (纸本)9798350361186

This research offers a brand-new framework for producing high-quality, photo-realistic sign language videos using sign language corpora. The framework consists of three main components such as Neural Machine Translation (NMT) using transformer, an open pose estimation approach, and a Generative Adversarial Network (GAN) with VGG-19 classifier. The NMT transformer translates the sign language corpus into a sequence of sign glosses, pose estimation model performs mapping of equivalent signer images and further assists to generate sign images. The GAN with VGG-19 classifier then generates high- quality sign language videos from the sign language images. The proposed method performs at the cutting edge in terms of evaluation on three benchmark dataset using metrics such as PSNR, SSIM, FID, and Inception Score. In addition, the framework offers better translation outcomes with less processing overhead and produces high-quality, photo-realistic sign language videos without the need for animation or avatar techniques. The proposed framework has the potential to offer the uninterrupted communication medium between common people with deaf-mute society. The evaluation scores highlights the improved perfor- mance of the proposed model compared with earlier approaches in terms of visual quality. © 2024 IEEE.

关键词： Generative adversarial networks

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Vehicle-Assisted Service Caching for Task Offloading in Vehicular Edge computing

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IEEE Transactions on Mobile computing 2025年第7期24卷 6688-6700页

作者： Jiang, Hongbo Cai, Jianghao Xiao, Zhu Yang, Kehua Chen, Hongyang Liu, Jiangchuan Hunan University College of Computer Science and Electronic Engineering Changsha410012 China Hunan University Shenzhen Research Institute Shenzhen518055 China Zhejiang Laboratory Research Center for Graph Computing Hangzhou311100 China Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada

The development of artificial intelligence (AI) enables vehicular edge computing (VEC) servers to be able to provide more intelligent services. However, the limited storage resources of VEC servers constrain the deployment of intelligent service contents, which greatly restricts the intelligence level of the VEC network. To resolve this problem, we first design a novel vehicle-assisted VEC network architecture and further propose VaCo, a Vehicle-assisted Collaborative caching system. VaCo allows VEC servers to download the cached service content from any vehicle in the VEC network to support task offloading. VaCo mainly considers the real-time scheduling problem of vehicle storage resources under the dynamic VEC network and the benefit problem caused by invoking vehicle resources under the highly dynamic load environment. VaCo models the vehicle storage resources as an independent resource pool and deploys a cross-VEC server content retrieval mechanism to achieve unified and efficient management of the storage resources of the vehicle cluster and the VEC server cluster. Then, we propose a multi-swarm collaborative optimization scheme to jointly optimize the service failure rate and cost, and further propose a Pareto-based optimization scheme to ensuring that VaCo can correctly evaluate the benefits of invoking vehicle resources in a dynamic VEC network. Finally, we implement VaCo and conduct extensive evaluations on real-world dataset. The experimental results on the real trajectory dataset show that VaCo can effectively utilize vehicle resources and ensure the benefits of both vehicles and VEC servers simultaneously. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Exploring the Significance and Paradoxical Nature of the Zero Value in Mathematics Using Artificial Intelligence 4

Exploring the Significance and Paradoxical Nature of the Zer...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Ishwarya, K. Saranya, G. Appathurai, K. Surendran, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Kg College of Arts and Science Department of Computer Technology Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300888

One of the greatest paradoxes in the human mind is the odd number zero. Both everything and nothing are meant by it. It performs an important function in mathematics as the additive identity of integers, real numbers, and a variety of other algebraic structures. Without zero, it would have been difficult for all branches of science - not just mathematics - to come up with definitions that were more exact. The digit 0 serves as a stand-in for other digits in place systems. Here, it creates an interactive learning session to show the existence and importance of '0' to a supernatural that does not utilize zeros in their number system. Three key concepts are used in this study to explain what zero is and what it means. The first idea is based on Apple logic, a fundamental approach to logical reasoning. Understanding gravity includes exploring how the second principle is used in this context. The usage of contemporary technology is the last idea. Without the usage of zero, the logic in all of these concepts will be completely erroneous. These ideas interactively applied artificial intelligence algorithms. © 2023 IEEE.

关键词： Numbering systems

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Scalable Quantum Photonic Neural Network Processor via Cavity-Assisted Interactions

Scalable Quantum Photonic Neural Network Processor via Cavit...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Basani, Jasvith Raj Niu, Murphy Yuezhen Waks, Edo Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics Joint Quantum Institute University of Maryland College ParkMD20742 United States Department of Computer Science Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States

We present an integrated photonic architecture that uses a single atom trapped in a cavity for deterministic high-fidelity quantum operations. Our design is unique in providing a photon-number-selective nonlinearity, ... 详细信息

ISBN: (纸本)9781957171395

关键词： Photonic integrated circuits

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Stacking Ensemble Model for Celestial Object Classification: Galaxies, Stars and Quasars 7

Stacking Ensemble Model for Celestial Object Classification:...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Sudharson, S. Annamalai, R. Reddy, Avuthu Avinash Varsha, P. Vellore Institute of Technology School of Computer Science And Engineering Department of Computer Science And Engineering Chennai600127 India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science And Engineering Chennai601103 India Chennai601103 India

ISBN: (纸本)9798350371406

In the field of astronomy, it is essential to classify celestial objects like stars, galaxies, and quasars based on their spectral characteristics. This spectral data provides valuable information about various properties, such as the elements present, temperature, density, and magnetic field. To tackle this classification task, we investigate the application of different classification and ensemble algorithms. The proposed approach uses a variety of machine learning classifiers, including logistic regression, support vector machines, k-nearest neighbors, decision trees, random forests, and XGBoost. These classifiers are combined to create a stacking classifier, which is then evaluated on its accuracy, precision, recall, F1 score, and support. The Stacking classifier demonstrates the highest accuracy, reaching an impressive 99.99% on the training data. Train Logloss is 0.011. The precision, recall, and f1 score values (all 1.00) indicate a robust classification capability a cross all classes of celestial objects. This outstanding accuracy means that it effectively identifies almost all celestial objects in the training data-set. Consequently, the Stacking model serves as a highly dependable and precise tool for recognizing galaxies, stars, and quasars based on their spectral characteristics. © 2023 IEEE.

关键词： Stars

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Strengthening-softening transition and maximum strength in Schwarz nanocrystals

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Nano Materials science 2024年第3期6卷 320-328页

作者： Hanzheng Xing Jiaxi Jiang Yujia Wang Yongpan Zeng Xiaoyan Li Center for Advanced Mechanics and Materials Applied Mechanics LaboratoryDepartment of Engineering MechanicsTsinghua UniversityBeijing100084China Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)Singapore138632Singapore

Recently,a Schwarz crystal structure with curved grain boundaries(GBs)constrained by twin-boundary(TB)networks was discovered in nanocrystalline Cu through experiments and atomistic *** Cu with nanosized Schwarz crystals exhibited high strength and excellent thermal ***,the grainsize effect and associated deformation mechanisms of Schwarz nanocrystals remain ***,we performed large-scale atomistic simulations to investigate the deformation behaviors and grain-size effect of nanocrystalline Cu with Schwarz *** simulations showed that similar to regular nanocrystals,Schwarz nanocrystals exhibit a strengthening-softening transition with decreasing grain *** critical grain size in Schwarz nanocrystals is smaller than that in regular nanocrystals,leading to a maximum strength higher than that of regular *** simulations revealed that the softening in Schwarz nanocrystals mainly originates from TB migration(or detwinning)and annihilation of GBs,rather than GB-mediated processes(including GB migration,sliding and diffusion)dominating the softening in regular *** analyses of simulation data further showed that compared with those in regular nanocrystals,the GB-mediated processes in Schwarz nanocrystals are suppressed,which is related to the low volume fraction of amorphous-like GBs and constraints of TB *** smaller critical grain size arises from the suppression of GB-mediated processes.

关键词： Schwarz nanocrystal Curved grain boundary Atomistic simulation Grain size effect Maximum strength

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Fruit Ripeness Detection using RipeNet 3

Fruit Ripeness Detection using RipeNet

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bhavana, Kapa Sumathi, R. Vanaja, Kandregula Manjula, S. Srinivasulu, T. Divya, Siripuri Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Department of Information Technology Andhra Pradesh Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331509675

Fruits are absolutely delicious for the most part, but more importantly, they are good for healthy life. Fruits are nature's candy and offer all sorts of health benefits besides the great taste. They provide necessary nutrients to the body so that it will not be susceptible to disease or other ailments. Fruits contain enough fiber to keep our digestive system in top health form. Fruits also helps to regulate the water level in body as well as your blood sugar levels, which means that will usually feel hungry less often if we consumes a lot of fruits. The easier it is for people to recognize that a fruit is ripe and ready for selling at the market allows the farmers, in some cases, to sell their crop early before it would normally spoil. If we look closely, we see that many times people don't buy something because it appears rotten or way past its peak ripeness, which demonstrates how products spoil right on time. This study proposed a user-convenient system for fruit ripeness detection using the convolutional neural network (CNN) based on RipeNet structure. © 2025 IEEE.

关键词： Fruits

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Advancing Neurological Health- A Collaborative Ensemble Learning Approach for Enhancing Early Diagnosis of Amyotrophic Lateral Sclerosis (ALS) 10

Advancing Neurological Health- A Collaborative Ensemble Lear...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Srinivasan, Karthik Ravi, R. Durga Pavani, A. Mandadi, Sowmya Srivel, R. Saudi Electronic University Department of Information Technology Riyadh Saudi Arabia J. J College of Engineering and Technology Department of Computer Science and Engineering India Aurora's Technological and Research Institute Department of Computer Science and Engineering India Adhiparasakthi Engineering College Department of Computer Science and Engineering Melmaruvathur India

ISBN: (纸本)9798350353068

In this study, we propose the integration of Support Vector Machines (SVM) into an ensemble learning framework alongside K-Nearest Neighbors (K-NN) and Naive Bayes models to enhance the early diagnosis of Amyotrophic Lateral Sclerosis (ALS). ALS diagnosis is challenging due to its heterogeneous clinical manifestations. Leveraging diverse datasets including genetic data, medical imaging, and clinical records, we train SVM, K-NN, and Naive Bayes models independently. The ensemble approach amalgamates their predictions, harnessing the complementary strengths of each model. Through rigorous performance evaluation encompassing accuracy, precision, recall, and F1-score metrics, we demonstrate the superior diagnostic capabilities of the ensemble. Employing cross-validation ensures the robustness of our model, while fine-tuning hyperparameters optimizes its performance. Integration of SVM into the ensemble framework contributes additional discriminative power, enhancing the accuracy and reliability of ALS diagnosis. Our research underscores the pivotal role of ensemble learning in addressing the complexities of medical diagnosis, offering a notable advancement in ALS diagnostic methodologies. The proposed approach holds promise for improving patient care and setting new standards in clinical practice © 2024 IEEE.

关键词： Support vector machines

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