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作者： Gupta, Nishant Mahajan, Nitish Kaushal, Sakshi Kumar, Naresh Kumar, Harish Sangaiah, Arun Kumar University Institute of Engineering and Technology Panjab University Chandigarh India School of Computing Science and Engineering Vellore Institute of Technology Vellore India

Voice over Internet Protocol (VoIP) carries and transforms voice over the IP networks. The principles of VoIP calls are similar to traditional telephony that involves signalling, channel-setup, digitization, and encoding of speech signal, but it transmits data over a packet-switched network instead of circuit-switched network. Factors which determine VoIP Quality of Service (QoS) include the choice of codec, packet loss, delay, jitter, and optimal hardware selection to handle different services. Hardware Calibration is a mechanism used for selecting an appropriate hardware for call manager to process and transmit different applications in real time. The widespread use of VoIP services in formal and informal sector produces a significant amount of data with variety of dimensions. This data can be used as leverage to analyze the system and predict various factors boosting the performance and cost effectiveness of the system. This paper proposes a predictive model that selects the best suitable hardware to handle particular offered load, which can support desired numbers of concurrent calls from wide array of processors available in the market today. This model would help the VoIP service providers in providing efficient services with QoS for different VoIP services like voice, data, video, chat, etc. This paper attempts to train and evaluate a model using various system parameters and system benchmark is predicted on an absolute scale. The results effectively demonstrate the selection of best call manager to handle offered load and hence provides QoS in overall network performance. © 2019 John Wiley & Sons, Ltd.

关键词： Quality of service

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Predicting Crops Based on Soil and Environmental Characteristics Using ARIMA and LSTM Model 4

Predicting Crops Based on Soil and Environmental Characteris...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Naresh Kumar, U. Madhana Mohan, K. Pragadeeswaran, S. Scaria, Jobin M. Baseera, A. Seenivasan, D. Annamacharya University Department of Artificial intelligence & Data science Rajampet India Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India School of Information Technology Ibs University Mt. Eriama Campus Port Moresby Papua New Guinea School of Computing Science and Engineering Vit Bhopal University Sehore India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (纸本)9798350352931

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. Data preparation involves processing data before storing it machine-readable. SFFS and RFE select features. The model was trained using a hybrid ARIMA-LSTM technique, which blends deep learning and time-series forecasting. The ARIMA-LSTM model outperforms solo ARIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. Data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ARIMA-LSTM can boost agricultural productivity while minimizing environmental impact. © 2024 IEEE.

关键词： crop productivity long short-term memory (lstm) soil management

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An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

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Computers, Materials & Continua 2023年第9期76卷 3859-3876页

作者： Weizheng Wang Xiangqi Wang Xianmin Pan Xingxing Gong Jian Liang Pradip Kumar Sharma Osama Alfarraj Wael Said College of Information Science and Engineering HunanWomen’s UniversityChangsha410138China School of Mathematics and Statistics Hunan First Normal UniversityChangsha410138China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenAB243FXUK Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial *** remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to ***,image denoising compromises the classification accuracy of original *** address these challenges in AE defense through image denoising,this paper proposes a novel AE detection *** proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network *** used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting *** analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected *** technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by *** proposed approach demonstrates excellent detection performance against mainstream AE *** results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.

关键词： Deep neural networks adversarial example image denoising adversarial example detection machine learning adversarial attack

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Immersive Learning Constructs for Deaf and Hard-of-Hearing Students in TVET Practical Skill

Immersive Learning Constructs for Deaf and Hard-of-Hearing S...

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Visualization, Informatics and technology Conference (IVIT), International

作者： Rini Hafzah Abdul Rahim Dinna Nina Mohd Nizam Nur Faraha Mohd Naim Aslina Baharum User Experience Group Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Computing and Information System School of Engineering and Technology Sunway University Selangor Malaysia

ISBN: (数字)9798350354447

ISBN: (纸本)9798350354454

The advent of advanced educational technologies, and immersive learning utilizing virtual reality (VR), augmented reality (AR), and mixed reality (MR) have emerged as a powerful tool to enhance learning experiences. However, traditional approaches often lack the ability to address the unique needs of Deaf and Hard of Hearing (DHH) students, particularly in practical skill development. The usage of immersive learning for the purpose of DHH students is still limited, especially in Malaysia. This study explores critical elements of immersive learning environments designed to facilitate practical skill acquisition for DHH students. Using the systematic literature review (SLR) determines immersive practical skill learning elements from selected 34 papers to help DHH students learn more effectively. 30 constructs are highlighted in the SLR results as recommendations for creating immersive learning for DHH practical skills. It is thus intended that the highlighted features would contribute to both the process of teaching and learning as well as the enhancement of student performance.

关键词： Visualization Prototypes Mixed reality Auditory system Educational technology Object recognition Informatics Augmented reality Immersive learning

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Gamification in Agile Development Methodology: A Systematic Literature Review

Gamification in Agile Development Methodology: A Systematic ...

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International Conference on Network Applications Protocols and Services (NETAPPS)

作者： Noraini binti Mohd Banua Fathey Mohammed Moaadh Mukred Mahmood A. Bazel Mohd Samsu Bin Sajat Awang Had Salleh Graduate School School of Computing University Utara Malaysia Jitra Kedah Sunway Business School Sunway University Subang Jaya Selangor Malaysia Faculty of Engineering and Computing University of Science and Technology Taiz City Yemen School of Computing Universiti Utara Malaysia (UUM) Sintok Kedah Malaysia

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

Gamification is a technique that uses game mechanics and aspects to engage and inspire people to complete a task. Gamification has grown in popularity in several industries recently, including software development. The purpose of this systematic literature review (SLR) is to review the most recent findings in the field of gamification's application to agile methodology between the years 2015 and 2024. With the help of search terms like “gamification,” “agile methodology,” and “game mechanics,” “agile development,” a thorough scan of databases including the IEEE Xplore, and Scopus was carried out. Articles and studies released in English were examined. Data were extracted analysed using a spreadsheet to organize information such as study design, sample size, research setting, kinds of game mechanics used, and major findings. To combine the results from the chosen studies and discover any recurring themes or patterns, a thematic analysis method was used. The findings of this SRL points to areas for further study and contributes to understanding the benefits and best practices of using gamification in agile methodology and provides insights for practitioners and researchers in the field.

关键词： Industries Protocols Databases Games Teamwork Internet Data mining Best practices Systematic literature review Software development management

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Python And Opencv For Sign Language Recognition

Python And Opencv For Sign Language Recognition

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2023 IEEE International Conference on Device Intelligence, computing and Communication Technologies, DICCT 2023

作者： Srinivasan, R. Kavita, R. Kavitha, M. Mallikarjuna, Basetty Bhatia, Sandeep Agarwal, Bhawna Ahlawat, Vinay Goel, Amit Veltech University Chennai Dept of Computer Science and Engineering India Galgotias University School of Computing Science and Engineering Uttar Pradesh Greater Noida India Birla Institute of Technology Mesra Jaipur Center India Kiet Ncr Delhi Department of Cse Uttar Pradesh India

ISBN: (纸本)9781665474924

Hearing-impaired people cannot communicate with normal people easily. Most people are not aware of sign language recognition. To support this, machine learning and CV can be used to create an impact on the impaired. This can be improved into automatic editors, in which the person can easily understand the sign language of the impaired people by just using hand sign recognition. In non-verbal communication, hand gesture always being an important mode of communication and it plays a vital role to bridge gap between deaf and dumb people. Several sign language recognition systems have been developed but the systems are not flexible and cost-effective. Physically challenged people can express their emotions and feelings through sign language. In this paper, we develop a sign detector that can detect signs and numbers and also other signs that are used in sign language with the help of OpenCV and Keras modules in python. By using this technology, we can understand what they want to convey through sign language which is not a common language to communicate with people. OpenCV and Keras of python are the modules used to achieve our work and the proposed work proved to be a user-friendly approach to communication by using Python language to recognize sign languages for hearing-impaired persons. © 2023 IEEE.

关键词： Python

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High-efficient machine learning projection method for incompressible Navier-Stokes equations

arXiv

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arXiv 2025年

作者： Chen, Ruilin Jin, Xiaowei Adams, Nikolaus A. Li, Hui School of Civil Engineering Harbin Institute of Technology Harbin150090 China College of Shipbuilding Engineering Harbin Engineering University Harbin150001 China School of Engineering and Design Technical University of Münih Münich85748 Germany Faculty of Computing Harbin Institute of Technology 150001 China

Hybrid simulation that integrating traditional numerical methods with artificial intelligence offers great potential for enhancing computational efficiency in fluid dynamics. High-fidelity data is one of most critical issues for the successful applications of AI in fluid mechanics, which is expensively obtained or even inaccessible. This study proposes a high-efficient machine learning (ML) projection method using forward-generated data for incompressible Navier-Stokes equations. A Poisson neural network (Poisson-NN) embedded method and a wavelet transform convolutional neural network multigrid (WTCNN-MG) method are proposed, integrated into the projection method framework in patchwork and overall differentiable manners with MG method, respectively. The solution of the pressure Poisson equation split from the Navier-Stokes equations is first generated either following a random field (e.g. Gaussian random field, GRF, computational complexity Ο(ΝlogΝ), Ν is the number of spatial points) or physical laws (e.g. a kind of spectra, computational complexity Ο(Ν · M), M is the number of modes), then the source terms, boundary conditions and initial conditions are constructed via balance of equations, avoiding the difficulties of obtaining high-fidelity training datasets. The feasibility of generated data for training Poisson-NN and WTCNN as well as the acceleration performances of the Poisson-NN embedded method and WTCNN-MG method are validated. The results indicate that even without any DNS data, the generated data can train these two models with excellent generalization and accuracy. The data following physical laws can significantly improve the high-frequency approximation, convergence rate, generalization and accuracy than that generated following GRF. The ML projection method offers significant improvements in computational efficiency. Particularly, the Poisson-NN embedded method achieves an average speed-up of 5.83 times over the traditional MG method, while the WTCNN

关键词： Navier Stokes equations

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Maximizing the Revolutionary Potential of Quantum computing: Challenges, Opportunities, and Future Directions 10

Maximizing the Revolutionary Potential of Quantum Computing:...

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

作者： Yamini, B. Nithyanandhan, R. Sudha, K. Nithya, T. Vijayakumar, K. Siva Subramanian, R. Srm Institute of Science and Tech School of Computing College of Engg and Tech Dept of Networking and Communications India S A Engineering College Dept of Cse India R.M.D Engineering College Dept of Csbs India Rajalakshmi Institute of Technology Dept of Cse India Adhiparasakthi Engineering College Department of It Melmaruvathur India Rmk College of Engg and Tech Dept of Cse India

ISBN: (纸本)9798350353068

The discipline of computing known as quantum computing uses the concepts of quantum mechanics to carry out intricate calculations. Quantum computing employs quantum bits, or qubits, which may exist in superposition states and represent both 0 and 1 simultaneously, in contrast to conventional computing, which uses bits to represent information as either 0 or 1. Beyond the capabilities of conventional computers, quantum computing provides the possibility of computations that are performed exponentially more quickly. It is a fascinating and quickly developing topic that has the potential to revolutionize many different scientific fields and businesses. With the potential to revolutionize a number of industries, including computer security, drugs production, Cybersecurity, the financial sector, ML and data science, and electronic science, quantum computing has emerged as a viable paradigm. Quantum cryptography and secure communication protocols improve security in computer security, however creating quantum-resistant encryption methods is a major difficulty. Quantum chemistry simulations and quantum ML have the potential to speed up drug development and advance pharmaceutical research, but integrating them into current processes is difficult. Quantum-resistant cryptographic algorithms, intrusion detection, and safe multi-party computing have the potential to improve security in cyberspace, necessitating further study in this area. In general, overcoming hardware limitations, enhancing qubit coherence, creating effective algorithms, and bridging the theoretical and practical implementation gaps are key to the future of quantum computing. Realizing the revolutionary effect of quantum computing requires collaboration between quantum scientists, subject matter experts, and industrial stakeholders. This review article delivers a thorough summary of the uses, advantages, difficulties, and potential uses of quantum computing in several fields. Further it focusses future resear

关键词： Intrusion detection

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A Novel Multimodal Sentiment Analysis Model Based on Gated Fusion and Multi-Task Learning

A Novel Multimodal Sentiment Analysis Model Based on Gated F...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xin Sun Xiangyu Ren Xiaohao Xie School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Applications Research Center on High Volume Language Information Processing and Cloud Computing

Sentiment analysis is an important research area in Natural Language Processing (NLP). With the explosion of multimodal data, Multimodal Sentiment Analysis (MSA) attracts more and more attention in recent years. How to Effectively harnessing the interplay between diverse modalities is paramount to achieving comprehensive fusion of MSA. However, current research predominantly emphasizes modality interaction, while overlooking unimodal information, thus neglecting the inherent disparities between modalities. To address these issues, we propose a novel model for multimodal sentiment analysis based on gated fusion and multi-task learning. The model adopts multi-task learning to concurrently address both multimodal and unimodal sentiment analysis tasks. Specifically, for the multimodal task, we leverage cross-modal Transformers with gating mechanisms to facilitate modality fusion. Subsequently, the fused representations are harnessed to generate sentiment labels for the unimodal tasks. Experiments on the CMU-MOSI and CMU-MOSEI datasets demonstrate that our model outperforms the existing methods and achieves the state-of-the art performance.

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Federated Learning Empowered Violence Recognition in CCTV Footage: A YOLO and ResNet-50 Fusion Approach

Federated Learning Empowered Violence Recognition in CCTV Fo...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Vinay Gautam Himani Maheshwari Raj Gaurang Tiwari Ambuj Kumar Agarwal Naresh Kumar Trivedi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Dehradun Uttrakhand India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India

Violence against humans in society is one major issue and cases are increasing rapidly. This creates an imbalance in society. Violence takes place suddenly in lonely places and it is difficult to handle it as there is a lack of information exchange. Although, surveillance cameras are installed at various places and the videos from these cameras can be utilized to detect violence. Several centralized deep learning(DL) and machine learning(ML) algorithms are used to address the problem, however, these methods are not suitable for protecting sensitive information. This study presented a deep learning method that uses federated learning to identify violent behaviors in CCTV video while protecting the privacy of individual users. This research uses the power of two deep learning models YOLO and RestNet-50. RestNe is trained with the data fetched with YOLO from CCTV footage available on the client side. Afterward, the trained models from clients are transferred to the server to integrate as a global model. After integration, the global model will be shared with the client for performance evaluation. It has been found that the suggested model beats another state-of-the-art deep learning model in the same situation, with an accuracy rate of 98.73%, when the data is distributed uniformly to all clients in the scenario. In research, the federated environment is set up with various hyper-performance parameters such as varying clients and communication rounds.

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