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14th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2024

作者： Rehman, Mobeen Ur Ilyas, Talha Seneviratne, Lakmal Hussain, Irfan Khalifa University KU Center for Autonomous Robotic Systems Abu Dhabi United Arab Emirates Clayton 3168 Monash University Monash Medical AI Group Faculty of Engineering and Computer Science VIC Australia

ISBN: (纸本)9798350366556

This study introduces an advanced framework for recognizing hand gestures from a first-person view, leveraging the integration of multimodal data including optical flow, pose, depth, and RGB video recordings. Adeptly navigating the challenges and opportunities presented by the integration of multimodal data. At its core, the framework employs two pivotal components: a cross-attention based adaptive graph convolutional network and relational graph interactions for modality fusion. The former is designed to extract features from skeleton-based gesture data, ensuring a nuanced capture of hand movements by emphasizing the interconnections within the hand's skeletal structure. The latter component innovatively models each output modality feature as a node in a fully connected relational graph, facilitating the fusion of heterogeneous data types through dynamic interactions between modalities. This approach allows for the leveraging of each data type's strengths and the mitigation of their weaknesses, significantly enhancing the system's classification accuracy and robustness. Tested on a public benchmark dataset, the framework achieved a remarkable accuracy of 98.48%, demonstrating its efficacy. Moreover, it proves resilient, maintaining strong performance (93.48% accuracy) even in scenarios where only one modality is available, highlighting its potential for real-world applications. This advancement sets a new benchmark in hand gesture recognition, promising future developments in multimodal data fusion. © 2024 IEEE.

关键词： Musculoskeletal system

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th International Conference on Advances in Computing and Data sciences, ICACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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Beyond Playlists: AI-Driven Emotion-Based Music Recommendation Systems 2

Beyond Playlists: AI-Driven Emotion-Based Music Recommendati...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Shiralaskar, Hrishikesh Mendhe, Sameer Datta Meghe Institute of Higher Education md Research Department of Computer Science & Engineering Faculty of Engineering & Technology Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Computer Science & Design Faculty of Engineering & Technology Maharashtra India

ISBN: (纸本)9798331505745

Music recommendation systems have evolved from simple playlist curation techniques to sophisticated AI-driven models capable of analyzing human emotions for personalized song suggestions. Emotion-based music recommendation systems integrate advanced machine learning, deep learning, and artificial intelligence techniques to detect users' emotional states through various input modes, including facial expressions, speech tone, text analysis, and physiological signals. These systems aim to enhance user experience by dynamically selecting music that aligns with real-time emotions, thereby improving mood regulation and mental well-being. This review explores the methodologies employed in emotion-based music recommendation systems, including deep learning architectures, facial recognition techniques, and sentiment analysis models. It also discusses the challenges associated with real-time emotion detection, data privacy concerns, cross-cultural differences in music perception, and system adaptability. Furthermore, the paper highlights emerging trends and future research directions, such as multimodal emotion detection, hybrid AI models, reinforcement learning, and ethical AI frameworks. Addressing these challenges and advancements will be crucial in developing more robust, accurate, and user-centric emotion-based music recommendation systems. © 2025 IEEE.

关键词： Deep learning

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Tensile Testing Based on the Polarization-maintaining Fiber Powered by an SM Optical Fibre 4

Tensile Testing Based on the Polarization-maintaining Fiber ...

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4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Kyselak, Martin Vavra, Jiri Vylezich, Zdenek Grenar, David Slavicek, Karel University of Defense Faculty of Military Technology Department of Electrical Engineering Brno Czech Republic University of Technology Faculty of Electrical Eng. and Communication Dept. of Telecommunications Brno Czech Republic Institute of Computer Science Masaryk University IT Infrastructure Division Brno Czech Republic

ISBN: (纸本)9798350391183

Optical fibre sensors are currently used to detect many non-electrical phenomena. The latest sensors analyze the polarization properties of light inside an optical fiber. Unlike interferometric methods, this method uses the interference of two planes of polarization at the output of the optical fibre. The sensors are divided into linear polarizer sensors or multi-fibre interference sensors based on different ways of converting the polarization state into a change in the intensity of the optical radiation. These sensors are particularly suitable for low frequency vibration measurements due to the transformation of beats into the longitudinal deformation of the optical fibre. The aim of the presented measurements was to demonstrate that these polarization vibration sensors have excellent sensitivity and are thus suitable for vibration detection. This paper demonstrates that polarization sensors have excellent sensitivity, are convenient and inexpensive to install, and, due to their non-electric nature, are suitable for hazardous environments. In addition, it is possible to employ these sensors as intrusion detection and prevention systems and remotely power them at a distance of tens of kilometers. The motivation for this research was the lack of information and equipment for vibration testing of optical fibres and the associated polarization changes. © 2024 IEEE.

关键词： Tensile testing

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Retraction Note: A blockchain-based electronic medical records management framework in smart healthcare infrastructure(Multimedia Tools and Applications, (2023), 82, (35419–35437), 10.1007/s11042-023-15152-z)

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Multimedia Tools and Applications 2025年第15期84卷 15625-15625页

作者： Zakzouk, Amaal El-Sayed, Ayman Hemdan, Ezz El-Din Alexandria Higher Institute of Engineering and Technology Alexandria Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menoufia Egypt

The Editor-in-Chief has retracted this article because it contains significant textual overlap with two prior publications with no common authors [1, 2]. The Publisher was not able to confirm a current contact email for Ayman El-Sayed. The remaining authors did not respond to correspondence from the Publisher about this retraction. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

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A Novel Image Encryption and Decoding Approach Based on DNA Coding and Arnold's Transformation for IoT Applications

A Novel Image Encryption and Decoding Approach Based on DNA ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Shaltout, Nawal Abd El-Latif, Ahmed A. Al-Adrousy, Waleed Hammad, Mohamed Ateya, Abdelhamied A. Elmougy, Samir Mansoura University Faculty of Computers and Information Department of Computer Science Mansoura35516 Egypt Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science 32511 Egypt Menoufia University Faculty of Computers and Information Department of Information Technology Shibin El Kom32511 Egypt Zagazig University Department of Electronics and Communications Engineering Zagazig Egypt

ISBN: (纸本)9798350351408

Nowadays, the Internet of Things (IoT) is significantly utilized by an extensive range of applications that lead to the enhancement of different aspects of defense and military capabilities. In IoT, a wide range of smart devices that can communicate with each other are connected through it. Therefore, it is essential to protect multimedia data, especially images with an encryption approach that offers a sufficient degree of security in military applications. Hence, this paper introduces an effective scheme for image encryption and decoding relying on Arnold's transformation DNA coding. It proposes a new pseudo-random number generation PRNG using the Sine-Logistic map and Duffing map called SLDM. The encryption method involves three phases: 1- Key generation and PRNG. 2- confusion phase using the Arnold transformation method. 3- diffusion using DNA rules and operations. The results based on the USC-SIPI dataset, show that the suggested scheme is sufficiently resistant to every kind of attack. Furthermore, the suggested method yields NPCR scores of around 99.62% and UACI scores of approximately 33.48%. Due to its low complexity, it has a great deal of potential for safe and real-time picture applications. © 2024 IEEE.

关键词： Random number generation

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A Continual Learning System with Self Domain Shift Adaptation for Fake News Detection 10

A Continual Learning System with Self Domain Shift Adaptatio...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Basterrech, Sebastian Kasprzak, Andrzej Platos, Jan Wozniak, Michal Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby Denmark Wroclaw University of Science and Technology Department of Systems and Computer Networks Wroclaw Poland VŠB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava Czech Republic

ISBN: (纸本)9798350345032

Detecting fake news is currently one of the critical challenges facing modern societies. The problem is particularly relevant, as disinformation is readily used for political warfare but can also cause significant harm to the health of citizens, such as by promoting false data on the harmfulness of selected therapies. One way to combat disinformation is to treat fake news detection as a machine learning task. This paper presents such an approach, which additionally addresses an important problem related to the non-stationarity characteristics of the fake news. We elaborated a stream data with the simulation of domain shift based on two popular benchmark datasets dedicated to the fake news classification problem (Kaggle Fake News and Constraint@AAAI2021-COVID19 Fake News Detection). The proposed learning system works in a Continual Learning (CL) framework and integrates a self domain shift adaptation in a machine learning scheme. The method was built following state-of-the-art techniques, that includes Word2Vec as a feature extractor and the LSTM model as a classifier. The performance of the approach has been evaluated over the generated data stream. The convenience of our approach is showed in the results, where the accuracy gain with respect to a CL approach without domain adaptation is observed to be significant. © 2023 IEEE.

关键词： Long short-term memory

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PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight Factor of Whale Optimization Algorithm 2

PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight F...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Habelalmateen, Mohammed I. Archana Reddy, R. Reddy, Chetana Nagarjuna, N. Pradeep Kumar, S. Universiti Tun Hussein Onn Malaysia Parit Raja Faculty of Electrical and Electronic Engineering Johor Batu Pahat Malaysia The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Telangana India Oxford College of Engineering Department of Electronics and Communication Bangalore India Cvr College of Engineering Department of Csit Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

In the landscape of optimization for mitigating the Peak-to-Average Power Ratio (PAPR) in Multiple-Input Multiple-Output and Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, prevalent techniques like the tone reservation scheme and l-norm Cyclic Algorithm (LNCA) algorithm grapple with inherent challenges linked to computational complexity and iterative algorithm derivation. While strides have been made with models such as Twin Symbol Hybrid Optimization by the premise of Partial Transmit Sequence (TSHO-PTS) and Advanced Switching Differential Evolution (ASDE), persistent concerns related to symbol overlaps and algorithmic limitations underscore the need for further refinement in computational efficiency within OFDM-based communication systems. In response to these challenges, the proposed Nonlinear Inertia Weight Factor within the Whale Optimization Algorithm (NIWF-WOA) emerges as a solution. Comparative analysis against existing methods like LCNA, Modified Dragonfly Optimization algorithm (MDOA), TSHO-PTS, and ASDE reveals the superior performance of NIWF-WOA. Numerical validation affirms its efficacy, with a recorded in Bit Error Rate (BER) of 10-2.5, SNR 10-3.4 and PAPR of.10-4.2, underscoring the substantial improvement achieved by NIWF-WOA in optimizing PAPR reduction for MIMO-OFDM systems. © 2024 IEEE.

关键词： Landscape of optimization MIMO-OFDM system Non-linear weight factor Peak to Average Power Ratio Whale optimization

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Beyond Playlists: AI-Driven Emotion-Based Music Recommendation Systems

Beyond Playlists: AI-Driven Emotion-Based Music Recommendati...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Hrishikesh Shiralaskar Sameer Mendhe Department of Computer Science & Engineering Faculty of Engineering & Technology Datta Meghe Institute of Higher Education md Research Wardha Maharashtra India Department of Computer Science & Design Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

关键词： Deep learning Emotion recognition Sentiment analysis Analytical models Adaptation models Reinforcement learning Brain modeling Real-time systems Physiology Recommender systems

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Leveraging Machine Learning for Crop Disease Identification: Comparing CNN and SVM Models

Leveraging Machine Learning for Crop Disease Identification:...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Maahi Shah Kruti Sakariya Dipak Ramoliya Computer Science & Engineering Department Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Anand India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Crops form a key part of the food cycle. But, within this vast and complex agriculture domain, the crops have their predators. Crop diseases may cause revenue losses to farmers and jeopardize food security. These detractors, without a doubt, can destroy the productivity and even the quality of crops, creating a definite concern for the economic sustenance of the farmers and food security from a global perspective. The spread of crop diseases has increased in recent decades due to agricultural intensification, globalization, trade, and climate change. For some reasonable circumstances, these farmers do not know the cause of the disease. Thus, there needs to be a valid mechanism through which such farmers can be assisted in diagnosing the crop disease. Henceforth, we have created a machine learning that examines crop leaves to identify agricultural diseases to ease their work. The Implementation of this was done using two algorithms; Convolutional neural Network (CNN) and Support Vector Machine (SVM). Comparing the two algorithms to determine which is more important for picture categorization is the aim. This study offers a thorough analysis of CNN and SVM algorithms used in agricultural disease detection, with a particular emphasis on fruit and vegetable leaf identification. Using the PlantVillage dataset, it tests these two machine-learning models in multiple implementations. CNN achieved an accuracy of 75.76 % for fruit leaves and 84.38% for vegetable leaves, whereas SVM showed an accuracy of 75.00% for fruit leaves and 75.11% for vegetable leaves. From the above comparison, it can be seen that CNN acts better than SVM, and more so in cases when their feature representations are quite complex. This paper is intended to provide the reader with an overview of how these techniques can best be applied for real-world applications in precision agriculture.

关键词： Support vector machines Precision agriculture Accuracy Machine learning algorithms Crops Food security Machine learning Feature extraction Convolutional neural networks Diseases

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