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A BERT-encoded ensembled CNN model for suicide risk identification in social media posts

Neural Computing and Applications 2024年第18期36卷 10955-10970页

作者： Gorai, Joy Shaw, Dilip Kumar Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

Suicide is a significant public health issue that devastates individuals and society. Early warning systems are crucial in preventing suicide. The purpose of this research is to create a deep learning model to identify suicide risk using a combination of bidirectional encoder representations from transformers (BERT) and an ensemble of multiple convolutional neural networks (CNN). BERT is used to encode the text data into numerical representations that capture the context-aware meaning of words and phrases, while ensemble CNN is used to analyze the encoded text data to identify patterns and relationships relevant to suicide risk. The model is trained on a large corpus of text data from social media and suicide notes and evaluated on a validation set of labeled data. The results of the proposed model perform better than the recent approaches in detecting suicide risk and can potentially be a valuable tool for suicide prevention efforts. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Efficacy of Ensemble Learning Method over Individual Classifier for Rainfall Forecasting in Eastern India

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Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 929-939页

作者： Karmakar, Rahul Kundu, Saranagta Biswas, Saroj Kumar Tripathi, Deeksha Department of Computer Science The University of Burdwan West Bengal Bardhaman India Department of Computer Science and Engineering National Institute of Technology Assam Silchar India

Severe rainfall has seriously threatened human health and survival. Natural catastrophes such as floods, droughts, and many other natural disasters are caused by heavy rains, which people worldwide have to deal with throughout the year. Accurate rainfall forecasting is essential in nations like India where cultivation is the main occupation. Machine learning (ML) techniques are more effective than many other strategies because of the unpredictable nature of rainfall. Ensemble learning (EL) methods yield higher accuracy than ML specific classifiers. Therefore, this paper proposes an EL-based prediction model for not only a specific state of India but for the entire eastern India region, which includes ten states: West Bengal, Jharkhand, Bihar, Odisha, Assam, Tripura, Mizoram, Manipur, Nagaland and Meghalaya. We acquired the rainfall data for eastern India using NASA’s power data access view for 22 years (2000–2021). In this model, apart from classification feature selection is done using Pearson correlation and ANOVA. This study looks at the accuracy of several different classifiers, including DT, LR, and SVM. The acceptable accuracy of 96.53% for rainfall prediction in eastern India was determined using an ensemble model that includes the XGBoost ensemble technique and the classifiers DT, LR, and SVM. The findings of our experiment reveal that EL-based prediction model outperforms the individual classifiers utilized in the experiment. © The Institution of Engineers (India) 2024.

关键词： Decision trees

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Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined data Center Networks

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Tsinghua science and Technology 2024年第5期29卷 1615-1632页

作者： Abdul Basit Dogar Sami Ullah Yiran Zhang Hisham Alasmary Muhammad Waqas Sheng Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China and also with Department of Informatics and SystemsUniversity of Management and TechnologyLahore 54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir 18050Pakistan School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha 61421Saudi Arabia Department of Computer Engineering Faculty of Information TechnologyUniversity of BahrainSakheer 32038Bahrainand also with School of EngineeringEdith Cowan UniversityPerth WA 6027Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

关键词： software defined data center networks network updates DCTCP out-of-order packets packet drop SDN

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Deep Learning-Based Holistic Speaker Independent Visual Speech Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1705-1713页

作者： Nemani, Praneeth Krishna, Ghanta Sai Ramisetty, Nikhil Sai, B Digvijay Sri Kumar, Santosh IIIT Naya Raipur Department of Computer Science and Engineering Raipur493661 India IIIT Naya Raipur Department of Data Science and Artificial Intelligence Raipur493661 India

From a broader perspective, the objective of visual speech recognition (VSR) is to comprehend the speech spoken by an individual using visual deformations. However, some of the significant limitations of existing solutions include the dearth of training data, improper end-to-end deployed solutions, lack of holistic feature representation, and less accuracy. To resolve these limitations, this study proposes a novel, scalable, and robust VSR system that uses the videotape of the user to determine the word which is being spoken. In this regard, a customized 3-D convolutional neural network (3-D CNN) architecture is proposed by extracting the spatio-temporal features and eventually mapping the prediction probabilities of the elements in the corpus. We have created a customized dataset resembling the metadata contained in the MIRACL-VC1 dataset to validate the concept of person-independence. While being robust to a broad spectrum of lighting conditions across multiple devices, our model achieves a training accuracy of 80.2% and a testing accuracy of 77.9% in predicting the word spoken by the user. © 2020 IEEE.

关键词： Speech recognition

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Integrating blockchain and machine learning for enhanced anti-money laundering system

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2439-2447页

作者： Shafin, Khandakar Md Reno, Saha Department of Computer Science and Engineering CCN University of Science and Technology Cumilla 3503 Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology (AUST) Dhaka 1208 Bangladesh

Money laundering is a serious threat to global financial systems, causing instability and inflation, and especially hurting middle-class savings. This paper suggests a new way to tackle these problems by using blockchain technology and advanced machine learning models. We use hyperledger fabric to securely record transactions and advanced algorithms like autoencoders and neural networks to create a strong anti-money laundering (AML) system. This system can detect and predict illegal financial activities in real-time and includes continuous monitoring and alerts. These features improve financial transparency and stability while protecting the savings of the middle class. By identifying and reducing risks early, our solution not only ensures compliance with regulations but also strengthens the resilience of global financial systems against new threats. This research helps develop effective ways to fight financial crime, promoting a safer and more transparent financial world. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blockchain Hyperledger fabric Inflation Machine learning Money laundering Security

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Appearance and motion-based unusual crowd events detection using multiple moving objects

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Multimedia Tools and Applications 2024年第42期83卷 90177-90189页

作者： Babu, K. Atlas, L. Godlin Sreeji, S. Arjun, K.P. Rasmi, A. Senthilvel, P. Gururama Computational Intelligence SRM Institute of Science & Technology Chengalpet Kattankulathur603203 India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Gitam University Karnataka Bengaluru India Information Science and Engineering RRIT Bangalore India Saveetha University Chennai602 105 India

Unusual crowd analysis is an important problem in surveillance video due to their features cannot be extracted efficiently on the crowd scenes. To overcome this challenge, this paper introduced the appearance and motion based unusual events detection method by using multiple moving objects. This method includes appearance and motion based computation, and moving objects trajectory analysis. By using these all techniques, the unusual crowds and their activities can be obtained by analyzing moving objects size and the distance among multiple moving objects. Two benchmark datasets such as PETS 2009 and UMN are used for the experimental results. Also, several existing algorithms are compared with the proposed method to evaluate the effectiveness of the proposed work. The comparative analysis results proved that the proposed method achieved best accuracy for unusual crowd events. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： And trajectory analysis Appearance based analysis Distance based analysis Unusual crowd events detection

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An ensembled-deep-learning paradigm trained with a self-improved coyote optimization algorithm (SI-COA) for crop disease detection

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Multimedia Tools and Applications 2025年第4期84卷 1697-1724页

作者： Shukla, Preeti Chandanan, Amit Kumar Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya CG Bilaspur India Department of Computer Science & Engineering Guru Ghasidas Vishwavidyalaya CG Bilaspur India

In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowledge of farmers, identification, and treatment of the disease have become a major challenge. The leaves have texture and visual similarities which are attributes for the identification of disease type. Hence, computer vision employed with deep learning provides a way to solve this problem. In this research work, a novel voting-based ensembled-deep-learning paradigm is developed for crop disease detection. Initially, the collected raw images are pre-processed via Laplacian Filter and morphological operations. From, the pre-processed image, the ROI region is identified using K-means Clustering. Then, from the ROI regions, the features like texture feature (Chi-squared Local Binary Pattern (CLBP)), Scale Invariant Feature Transform (SIFT), Gray-Level Co-occurrence Matrix (GLCM)), color features, and shape features are extracted. Then, the optimal features are selected by using Self-Improved Coyote Optimization Algorithm (SI-COA) is an extended version of the standard Coyote Optimization Algorithm (COA) and is a metaheuristic for optimization that is inspired by the canis latrans species. For crop disease detection, the voting-based ensembled-deep-learning paradigm was used which includes the Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs), and Optimized Deep Neural Networks (O-DNN). The weight of DNN is optimized via Self-Improved Coyote Optimization Algorithm (SI-COA). The overall outcome of this process is evaluated by using various performance metrics such as Precision, Recall, F1-Score, Accuracy, Sensitivity and Specificity, NPV, FNR, FPR, and MCC, as well. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2

关键词： Long short-term memory

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An efficient deep learning model for early disease detection in vegetable crops

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Neural Computing and Applications 2025年 1-20页

作者： Bhola, Amit Kumar, Prabhat School of Computer Science and Engineering Sharda University Greater Noida201310 India Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna800005 India

Plant diseases significantly threaten global food security and economic stability by reducing crop yields, increasing production costs, and exacerbating food shortages. Early and precise detection of plant diseases is essential for mitigating these risks. This study introduces a lightweight deep learning model for early and efficient disease detection in key vegetable crops, including eggplant, potato, tomato, and soybean. The model leverages a comprehensive dataset containing 23 classes of healthy and diseased plant images. With only 388,055 parameters and a model size of 1.48 MB, it achieves an impressive accuracy of 92.75%, outperforming 17 state-of-the-art deep learning models in terms of performance, efficiency, and size. The novelty lies in the model’s compact architecture, making it highly suitable for deployment in resource-constrained environments such as mobile and edge devices. The primary objective is to provide a scalable and cost-effective solution for early disease detection to enhance agricultural productivity. The findings underscore the model’s superiority over traditional methods and emphasize its potential for real-world applications in smart and sustainable agriculture systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Smart agriculture

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Robust subspace clustering via two-way manifold representation

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Multimedia Tools and Applications 2024年第16期84卷 1-18页

作者： Ezeora, Nnamdi Johnson Anichebe, Gregory Emeka Nzeh, Royransom Chiemela Uzo, Izuchukwu Uchenna Department of Computer Science University of Nigeria Nsukka Nigeria School of Computer Science and Communication Engineering JiangSu University JiangSu212013 China

Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overlapping subspaces. To tackle this problem and ensure that each sample is only assigned to a single subspace, a new method is proposed in this paper. Specifically, a two-way learning technique is introduced by inducing data manifold via two representative structures. The first is a low-rank structure learned directly from original data. The second structure is an affinity matrix obtained via a k symmetric nearest neighbor graph. By further introducing a dual regularization term, both structures are allowed to guild themselves adaptively to find robust clustering directly without spectral post-processing. In order to evaluate the effectiveness of the proposed method, several experiments are conducted on multiple benchmark datasets. The experimental results, evaluated using six standard metrics, clearly demonstrate that the proposed method outperforms state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clustering algorithms

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A Voice Assistive Mobile Application Tool to Detect Cardiovascular Disease Using Machine Learning Approach

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Biomedical Materials and Devices 2024年第2期2卷 1246-1257页

作者： Uddin, Khandaker Mohammad Mohi Dey, Samrat Kumar Babu, Hafiz Md. Hasan Department of Computer Science and Engineering Southeast University School of Science and Technology Bangladesh Open University Department of Computer Science and Engineering University of Dhaka

Many people all around the world suffer from heart disease, which is regarded as a severe illness. In healthcare, especially cardiology, it is crucial to accurately and quickly diagnose cardiac problems. In this research, we proposed an accurate and efficient mobile application-based system for diagnosing cardiac disease based on machine learning approaches. The developed mobile application is voice assistive, which makes the proposed application more user-friendly. Numerous machine learning methods have been examined in this study to predict and diagnose cardiovascular disease (CVD). A detailed comparative study was also drawn using eighteen (18) classification algorithms such as Support Vector Machines, Logistic Regression, Linear SVC, K-Nearest Neighbors, Naive Bayes, Stochastic Gradient Descent, Gradient Boosting, Ridge, Bagging, Random Forest, Decision Tree, XGB, LGBM, Extra Trees Perceptron, and Voting Classifier (hard or soft voting). Sixty-eight thousand nine hundred seventy-five (68,975) samples from various patients were used to test the performance of each approach. According to the study, Random Forest and Decision Trees have the best accuracy levels at 99.9%. However, the Support Vector Machine classifier had the lowest performance, barely achieving 60% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2024.

关键词： Cardiovascular heart disease Detection Healthcare informatics Machine learning Mobile application

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