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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 545-553页

作者： Sarkar, Somrita Dey, Anamika Pradhan, Ritam Sarkar, Upendra Mohan Chatterjee, Chandranath Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Industrial and Systems Engineering Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Agricultural and Food Engineering Kharagpur721302 India Indian Institute of Technology Patna Department of Computer Science and Engineering Patna801106 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Crop yield prediction is a crucial task in agricultural science, involving the classification of potential yield into various levels. This is vital for both farmers and policymakers. The features considered for this task are diverse, including weather, soil, and historical yield data. Recently, plant images captured in different modalities, such as red-green-blue, infrared, and multispectral bands, have also been utilized. Most of these data are inherently temporal. Integrating such multimodal and temporal data is advantageous for yield classification. In this work, a deep learning framework based on meta-transformers and temporal graph neural networks has been proposed to achieve this goal. Meta-Transformers allow the modeling of multimodal interactions, while temporayel graph neural networks enable the utilization of time sequences. Experimental results on the publicly available EPFL multimodal dataset demonstrate that the proposed framework achieves a high classification accuracy of nearly 97%, surpassing other state-of-the-art models, such as long short-term memory networks, 1-D convolutional neural networks, and Transformers. In addition, the proposed model excels in accuracy metrics, with a precision of approximately 98%, an F1-Score of 91%, and a recall of 94% in crop yield prediction. © 2024 IEEE.

关键词： Distribution transformers

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A Novel Approach for Optimal Cluster Identification and N-Order Hesitation Based Time Series Forecasting

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SN computer science 2024年第7期5卷 872页

作者： Dixit, Ankit Jain, Shikha Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Time series forecasting is an important field of research, especially when the series is completely random, known as a strictly non-stationary time series (NS-TS). To handle the randomness efficiently, the paper presents a novel approach to identify the optimal number of clusters in NS-TS followed by a hesitation-based N-order IFS forecasting algorithm (called IFSH_new). The performance of the proposed approach for determining the optimal number of clusters is demonstrated using four variants of the proposed algorithm, IFSH_new, across five datasets. Furthermore, a comparison of the best variant of the proposed approach with existing state-of-art work based on mean absolute percentage error (MAPE) and root mean square error (RMSE) confirms the superiority of the proposed model over others. Additionally, the graphical representation of actual versus forecasted data points for different datasets shows good improvement at high change points compared to other approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Degree of hesitation Intuitionistic fuzzy set (IFS) N-order forecasting model Non-stationary time series (NS-TS) Time series forecasting model

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Advanced Breast Cancer Detection Using Spatial Attention and Neural Architecture Search (SANAS-Net)

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SN computer science 2025年第1期6卷 1-12页

作者： Souza, Melwin D. Ananth Prabhu, G. Kumara, Varuna Department of Computer Science & Engineering Sahyadri College of Engineering & Management Mangalore India Department of Electronics and Communication Engineering Moodlakatte Institute of Technology Kundapura India

This paper introduces a novel spatial attention neural architecture search network (SANAS-Net), which incorporates a spatial attention mechanism to enhance the model’s ability to focus on critical regions within mammograms. By utilizing multi-head attention within transformer blocks, the model captures diverse spatial relationships and interactions between features. Positional embeddings are incorporated into the transformer blocks, further enriching the model’s capacity to understand global spatial context. Extensive experiments were conducted to validate the proposed methodology. SANAS-Net was shown to successfully identify attentive networks that demonstrate high performance in distinguishing malignant from benign breast cancer cases. The model achieved a test accuracy of 89.95%, significantly surpassing the performance of previously proposed algorithms for breast cancer detection using mammography images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Breast cancer detection Deep learning Mammography Neural architecture search Spatial attention

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Compete to Win: Enhancing Pseudo Labels for Barely-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2023年第11期42卷 3244-3255页

作者： Wu, Huimin Li, Xiaomeng Lin, Yiqun Cheng, Kwang-Ting The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Shenzhen Research Institute Shenzhen518057 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering The Department of Computer Science and Engineering Hong Kong Hong Kong

This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the existing state-of-the-art semi-supervised solution cross pseudo supervision is the unsatisfactory precision of foreground classes, leading to a degenerated result under barely-supervised learning. In this paper, we propose a novel Compete-to-Win method (ComWin) to enhance the pseudo label quality. In contrast to directly using one model's predictions as pseudo labels, our key idea is that high-quality pseudo labels should be generated by comparing multiple confidence maps produced by different networks to select the most confident one (a compete-to-win strategy). To further refine pseudo labels at near-boundary areas, an enhanced version of ComWin, namely, ComWin + , is proposed by integrating a boundary-aware enhancement module. Experiments show that our method can achieve the best performance on three public medical image datasets for cardiac structure segmentation, pancreas segmentation and colon tumor segmentation, respectively. The source code is now available at https://***/Huiimin5/comwin. © 1982-2012 IEEE.

关键词： Image segmentation

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Human-computer Interaction Using Deep Fusion Model-Based Facial Expression Recognition System

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computer Modeling in engineering & sciences 2023年第5期135卷 1165-1185页

作者： Saiyed Umer Ranjeet Kumar Rout Shailendra Tiwari Ahmad Ali AlZubi Jazem Mutared Alanazi Kulakov Yurii Department of Computer Science&Engineering Aliah UniversityKolkata700156India Department of Computer Science and Engineering National Institute of TechnologySrinagarJammu and Kashmir190006India Department of Computer Science&Engineering Thapar UniversityPatiala147004India Computer Science Department King Saud UniversityRiyadh11451Saudi Arabia Department of Computer Engineering National Technical University of UkraineIgor Sikorsky Kyiv Polytechnic InstituteKyiv03056Ukraine

A deep fusion model is proposed for facial expression-based human-computer Interaction ***,image preprocessing,i.e.,the extraction of the facial region from the input image is ***,the extraction of more discriminative and distinctive deep learning features is achieved using extracted facial *** prevent overfitting,in-depth features of facial images are extracted and assigned to the proposed convolutional neural network(CNN)*** CNN models are then ***,the performance of each CNN model is fused to obtain the final decision for the seven basic classes of facial expressions,i.e.,fear,disgust,anger,surprise,sadness,happiness,*** experimental purposes,three benchmark datasets,i.e.,SFEW,CK+,and KDEF are *** performance of the proposed systemis compared with some state-of-the-artmethods concerning each *** performance analysis reveals that the proposed system outperforms the competitive methods in terms of various performance ***,the proposed deep fusion model is being utilized to control a music player using the recognized emotions of the users.

关键词： Deep learning facial expression emotions recognition CNN

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A Scrutiny of Machine Learning Methods for the Detection and Identification of Cyber Intrusion

A Scrutiny of Machine Learning Methods for the Detection and...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Eluri, Rama Krishna Valicharla, Karunakumar Divya, Modalavalasa Anusha, K.B. Narasaraopeta Engineering College Department Of Computer Science & Engineering Narasaraopet India Aditya Institute Of Technology And Management Department Of Computer Science & Engineering Tekkali India

ISBN: (纸本)9798350371567

This study investigates the interruption identification problem for organization safe havens;the main goal is to classify network behavior as normal or abnormal while minimizing misclassification. In this investigation, two effective information mining techniques are combined to identify network disruption. This methodology makes use of the two computations while avoiding their flaws, combining SVM, Decision Tree, and K Means together for effective information arrangement. This approach is implemented and surveyed using the standard benchmarking KDDCUP99 informational collecting. The experimental findings outperformed the other methods in terms of precision rate and run duration productivity, and this computation is suitable for detecting novel attack kinds. © 2024 IEEE.

关键词： Decision trees

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Interactive Learning with AlphAR: A Symbol-Recognition Aid for Dyslexic Children Through Augmented Reality

Interactive Learning with AlphAR: A Symbol-Recognition Aid f...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Shilbi, Susan Chhirolya, Vaidik Basha, Niha Kamal Surendiran, B. School of Computer Science and Engineering Vellore Institute of Technology Vellore India National Institute of Technology Puducherry Department of Computer Science and Engineering Karikal India

ISBN: (纸本)9798331540685

Dyslexia is a neurological condition that affects a person's ability to read, write, and spell. It is characterized by difficulties in processing phonological information, leading to challenges in acquiring fluent and accurate reading skills. Early assistance fosters a positive learning trajectory, enhancing the overall well-being and confidence of dyslexic individuals. AlphAR is an innovative augmented reality (AR) web system specifically tailored to address the challenges of symbol recognition in dyslexic children, developed using cutting-edge A-Frame and *** technologies. It harnesses marker-based AR to create an immersive learning environment. The novelty lies in its seamless integration of marker patterns with corresponding 3D models, facilitating interactive learning experiences for children aged 5 to 10. Through intuitive user interface elements and audio cues, AlphAR enhances engagement and promotes auditory learning. Ethical design principles ensure inclusivity and user-friendliness, prioritizing participant comfort and engagement. Users interact with the system through intuitive gestures, such as flipping down and left, further enhancing skill development. The iterative nature of AlphAR allows for adaptability to individual learning paces, fostering a positive and inclusive experimental environment. To validate AlphAR's effectiveness, we will benchmark it against established AR interventions like LitAR, AHA System and many others. © 2024 IEEE.

关键词： Contrastive Learning

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Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

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computer Systems science & engineering 2023年第9期46卷 3387-3401页

作者： A.L.Amutha R.Annie Uthra J.Preetha Roselyn R.Golda Brunet Department of Computational Intelligence SRM Institute of Science and TechnologyChennai603203India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India Department of Computer Science and Engineering Government College of EngineeringSalem636011India

The invention of Phasor Measurement Units(PMUs)produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make *** are used in transmitting data to Phasor Data Concentrators(PDC)placed in control centers for monitoring purpose.A primary concern of system operators in control centers is maintaining safe and efficient operation of the power *** can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal *** normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power *** a result,detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical.A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this *** proposed method consists of three modules namely,offline Gaussian Mixture Model(GMM),online GMM for identifying anomalies and clustering ensemble model for classifying the *** significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset,adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the *** proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.

关键词： Smart Grid PMU data incremental learning classifying anomalies artificial intelligence

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Hybrid Activation Functions in Deep Convolutional Neural Networks for Maize and Paddy Leaf Disease Recognition: A Transfer Learning Approach

Hybrid Activation Functions in Deep Convolutional Neural Net...

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Control Instrumentation System Conference, CISCON 2023

作者： Sunilkumar, H.R. Poornima, K.M. Department of Computer Science and Engineering PES Institute of Technology Management Shivamoggaa India Department of Computer Science and Engineering JNN College of Engineering Shivamoggaa India

ISBN: (纸本)9789819758654

Early detection of crop diseases is an art to prevent further loss in the yield. It not only prevents further spread but also increases the revenue for the farmers. Leaf is a major part of any plant and its good health is very important for maximum yield. Maize and paddy are major food crops grown in India and contribute significant part in food security. There are various reasons for leaves to get diseases that include pathogens like virus, bacteria, pests and even adverse environmental conditions. The idea of proposed work is to take dataset of maize and paddy leaf images with diseases like corn blight, corn common rust, corn gray leaf spot, brown spot, hispa, leaf blast along with healthy leaves. Use transfer learning to train different Deep Convolutional Neural Networks (DCNNs) with hybrid activation functions and use the trained models to test new leaf images. Different activation functions like Rectified Linear Unit (ReLU), Exponential Linear Unit (ELU) and Leaky Rectified Linear Unit (Leaky-ReLU) have been combined and applied. Initially all DCNNs are trained by using respective original activation functions, later hybrid activation functions are applied with suitable epochs, learning rate, iterations. Different DCNNs like AlexNet, DarkNet-19, VGG-16, SqueezeNet, and ResNet-18 have been tested on maize leaf images and obtained accuracies from 75 to 97.5%. Further, Shuffle Net, DarkNet 53, and Resnet-50 have been used for paddy leaf images and obtained accuracies from 70 to 98%. Our study found that AlexNet and VGG-16 and ResNet-50 show promising improvements on classification accuracy with hybrid activation functions. A detailed ensemble of DCNNs is given at the end to check the performance of various hybrid activation functions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Energy-Efficient Encounter, Buffer and Contact Duration-Based Routing Protocol in Opportunistic Networks 2nd

Energy-Efficient Encounter, Buffer and Contact Duration-Bas...

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2nd NIELIT's International Conference on Communication, Electronics and Digital Technologies, NICEDT 2024

作者： Dutta, Amit Borah, Satya Jyoti Singh, Jagdeep Department of Computer Science and Engineering NERIST Nirjuli Itanagar India Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal India

ISBN: (纸本)9789819736003

Opportunistic network (OppNet) is a type of advanced wireless mobile network that has evolved from Mobile Ad-hoc Networks (MANETs) and belongs to the category of Delay Tolerant Network (DTN). The unique features that make OppNet different from other wireless networks are its intermittent connectivity, unstable topology, no dedicated path to the destination from the source, and high node mobility. This further makes the process of data transmission a challenging task. For this a method called "store-carry-forward" is used in which the battery-operated intermediate nodes store the message, discover a suitable relay node, and forward the message to that node. This further leads to the depletion of the battery power or energy of the node in due course of time. In this context, there is a necessity to develop routing models by taking the current energy status of a node into consideration. In this paper, the authors proposed an energy-efficient version of a popular routing mechanism EBC, termed E-EBC where the selection of the next suitable node depends upon the battery power (referred to as Energy) of that node. This model is then compared with existing benchmark protocols- Epidemic, Prophet, EDR, E-Prophet, E-EDR, and EBC. Simulation results (using ONE Simulator) depict that the proposed model performs much better than that of the benchmark models respectively by around 42.73, 41.46, 58.19, 32.03, 65.99, and 44.77% in terms of average latency. The proposed protocol E-EBC is also compared against messages dropped by varying the TTL, Buffer Size, and number of nodes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Delay tolerant networks

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