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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kumar, B. Santhosh Devi, Anusuya Sharma, Smita Bhavana, Munugapati Babu, B. Sankara Singh, Navdeep Institute of Aeronautical Engineering Department of Cse Ai and Ml Telangana Hyderabad India New Horizon College of Engineering Department of Applied Science Karnataka Bangalore India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350350067

Integrating renewable energy sources (RESs) into smart grids (SGs) is challenging due to the intermittent and unpredictable nature of the sources, which are often the sun or wind. Additionally, it is important to think about how to get customers involved in the electricity sector so that we can lower energy prices globally. According to this article, an energy management system (EMS) may help the SG run more efficiently. The EMS aggregates distributed energy resources to make SG market participation easier. Integrating demand-side management (DSM) with active management strategies (AMS) has financial and environmental benefits as it increases the use of renewable energy sources while decreasing consumer energy costs. By incorporating their preferences and costs into market processes, energy supply/demand and distribution system operators (DSOs) may respond to real-time price signals. This has the potential to make the grid more adaptable and resilient. To verify the effectiveness of the proposed EMS, a 23-bus, 11-kV transmission network is used. © 2024 IEEE.

关键词： Clean energy

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning 25

A Case Study on Context-Aware Neural Machine Translation wit...

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25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna India Wipro AI Lab45 London United Kingdom School of AI and Data Science Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781068690709

In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies (Li et al., 2020) have shown that the context encoder generates noise and makes the model robust to the choice of context. This paper further investigates this observation by explicitly modelling context encoding through multi-task learning (MTL) to make the model sensitive to the choice of context. We conduct experiments on cascade MTL architecture, which consists of one encoder and two decoders. Generation of the source from the context is considered an auxiliary task, and generation of the target from the source is the main task. We experimented with German-English language pairs on News, TED, and Europarl corpora. Evaluation results show that the proposed MTL approach performs better than concatenation-based and multi-encoder DocNMT models in low-resource settings and is sensitive to the choice of context. However, we observe that the MTL models are failing to generate the source from the context. These observations align with the previous studies, and this might suggest that the available document-level parallel corpora are not context-aware, and a robust sentence-level model can outperform the context-aware models. © 2024 The authors, © 2024 European Association for Machine Translation.

关键词： Neural machine translation

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TOWARDS IMITATION LEARNING TO BRANCH FOR MIP: A HYBRID REINFORCEMENT LEARNING BASED SAMPLE AUGMENTATION APPROACH 12

TOWARDS IMITATION LEARNING TO BRANCH FOR MIP: A HYBRID REINF...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Changwen Ouyang, Wenli Yuan, Hao Gong, Liming Sun, Yong Guo, Ziao Dong, Zhichen Yan, Junchi AI Lab Lenovo Research China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Branch-and-bound (B&B) has long been favored for tackling complex Mixed Integer Programming (MIP) problems, where the choice of branching strategy plays a pivotal role. Recently, Imitation Learning (IL)-based policies have emerged as potent alternatives to traditional rule-based approaches. However, it is nontrivial to acquire high-quality training samples, and IL often converges to suboptimal variable choices for branching, restricting the overall performance. In response to these challenges, we propose a novel hybrid online and offline reinforcement learning (RL) approach to enhance the branching policy by cost-effective training sample augmentation. In the online phase, we train an online RL agent to dynamically decide the sample generation processes, drawing from either the learning-based policy or the expert policy. The objective is to strike a balance between exploration and exploitation of the sample generation process. In the offline phase, a value function is trained to fit each decision's cumulative reward and filter the samples with high cumulative returns. This dual-purpose function not only reduces training complexity but also enhances the quality of the samples. To assess the efficacy of our data augmentation mechanism, we conduct comprehensive evaluations across a range of MIP problems. The results consistently show that it excels in making superior branching decisions compared to state-of-the-art learning-based models and the open-source solver SCIP. Notably, it even often outperforms Gurobi. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning

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ACM Transactions on Management Information Systems 2025年第2期16卷 1-24页

作者： Liu, Xiao-Yang Zhu, Rongyi Zha, Daochen Gao, Jiechao Zhong, Shan White, Matt Qiu, Meikang Department of Computer Science Rensselaer Polytechnic Institute Columbia University New York United States Department of Electrical Engineering Columbia University New York United States Department of Computer Science University of Rochester Rochester United States Department of Computer Science Rice University Houston United States Department of Computer Science University of Virginia Charlottesville United States Executive Director PyTorch Foundation Gm of AI Linux Foundation CA United States School of Computer and Cyber Sciences Augusta University Augusta United States

The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science. However, concerns regarding data privacy have emerged, especially when multiple stakeholders aim to collaboratively enhance LLMs using sensitive data. In this scenario, federated learning becomes a natural choice, allowing decentralized fine-tuning without exposing raw data to central servers. Motivated by this, we investigate how data privacy can be ensured in LLM fine-tuning through practical federated learning approaches, enabling secure contributions from multiple parties to enhance LLMs. Yet, challenges arise: (1) despite avoiding raw data exposure, there is a risk of inferring sensitive information from model outputs, and (2) federated learning for LLMs incurs notable communication overhead. To address these challenges, this article introduces DP-LoRA, a novel federated learning algorithm tailored for LLMs. DP-LoRA preserves data privacy by employing a Gaussian mechanism that adds noise in weight updates, maintaining individual data privacy while facilitating collaborative model training. Moreover, DP-LoRA optimizes communication efficiency via low-rank adaptation, minimizing the transmission of updated weights during distributed training. The experimental results across medical, financial, and general datasets using various LLMs demonstrate that DP-LoRA effectively ensures strict privacy constraints while minimizing communication overhead. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Cross-Domain Cybersecurity: Integrated Intrusion Detection Framework

Cross-Domain Cybersecurity: Integrated Intrusion Detection F...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Vishnu Kurnala Hema Sriya Kanigolla Mojesh Manda Anudeep Meda Computer Science & Engineering (AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

This study presents a cross-domain cybersecurity strategy that includes a sophisticated intrusion detection system (IDS) positioned strategically at the default gateway of Healthcare Systems and Industrial Internet of Things (IIoT) infrastructures. The suggested model combines a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) architecture and incorporates a simulated XGBoost layer to improve predicting accuracy. The CNN-GRU model is designed to extract intricate spatial and temporal characteristics. The CNN component focuses on collecting local patterns, while the GRU component is responsible for addressing sequential dependencies. The outputs are merged with the original input features to create a comprehensive collection of features. This set is then passed through dense layers that simulate the capabilities of XGBoost for the final prediction. This ensemble methodology utilizes deep learning and gradient boosting to enhance accuracy and resilience in classification tasks. The framework prioritizes the processing of data in real-time, guaranteeing the integrity of the system and enabling swift detection of threats. The effectiveness of the model can be measured by its packet processing rate, which demonstrates its performance under different workloads. Our Intrusion Detection System (IDS) establishes a higher benchmark for proactive and dynamic defense, offering strong safeguarding capabilities for healthcare, IIoT, and general networks. It continuously adjusts to changing cyber threats in order to ensure the security of key infrastructure.

关键词： Deep learning Accuracy Intrusion detection Medical services Telecommunication traffic Logic gates Boosting Convolutional neural networks computer security Industrial Internet of Things

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Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4787-4798页

作者： Rahman, Wahidur Hossain, Muhammad Minoar Hasan, Md. Mahedi Iqbal, Md. Sadiq Rahman, Mohammad Motiur Fida Hasan, Khondokar Moni, Mohammad Ali Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Uttara University Department of Computer Science and Engineering Dhaka1230 Bangladesh Prime University Department of Management and Information System Mirpur Dhaka1216 Bangladesh Bangladesh University Department of Computer Science and Engineering Dhaka1207 Bangladesh School of Professional Studies CanberraNSW2052 Australia Artificial Intelligence and Cyber FuturesInstitute Charles Sturt University AI & Digital Health Technology BathurstNSW2795 Australia

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 s after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms. © 2020 IEEE.

关键词： Blockchain

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A Review and Comprehensive Analysis of Recent Research in Crop Yield Prediction using Machine Learning Algorithms

A Review and Comprehensive Analysis of Recent Research in Cr...

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IEEE Recent Advances in Intelligent Computational Systems (RaiCS)

作者： Selvakumar R. K. Salehundam Sai Tanoj Tigulla Hrushitha Goud Department of Computer Science and Engineering CVR College Of Engineering Hyderabad India Department of Computer Science and Engineering (AI&ML) CVR College Of Engineering Hyderabad India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

Traditional farming based on conventional methods has been the pillar of agricultural practices for a long time. But, due to the escalating global population, unstable climatic patterns and limited resources, the efficiency of traditional methods is facing challenges. The introduction of data-driven and artificial intelligence based approaches involving machine learning and deep learning have gained significant popularity capturing the interest of numerous researchers. This review examines various algorithms, including Random Forest, Decision Trees, K-Nearest Neighbours, Lasso Regression, Stacked Regression, and XGBoost, along with deep learning techniques like LSTM, RNN, and SVM. The study highlights their performance across multiple metrics such as accuracy, RMSE, MAE, R 2 , and MSE. Notably, Random Forest emerges as a highly effective model due to its excellent performance in accuracy, RMSE, MAE, and R2. Lasso Regression and XGBoost also show remarkable results in RMSE and MSE, respectively. Deep learning models like LSTM, RNN, and SVM are recognized for their high accuracy, underlining their potential in handling complex agricultural data. Through an examination of diverse approaches and data sources, this review seeks to explain and define the capabilities of these innovative technologies for sustainable agrarian applications.

关键词： Deep learning Support vector machines Machine learning algorithms Accuracy Reviews Prediction algorithms Crop yield

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Multi-Modal Emotion Detection using Deep Learning Techniques

Multi-Modal Emotion Detection using Deep Learning Techniques

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Arundhati Sitharam Rashmi KR Nishita Eeralli Prakash Jayashree Rangareddy Department of Computer Science (AI & ML) PES University Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Accurately detecting emotions and analysing it, especially in real-time, plays a major role in bolstering human-computer interaction across a spectrum of applications like virtual assistants, call centers, and cyber-security. Highly precise emotion recognition can potentially transform user experience and provide some invaluable insights. In this paper, an attempt to explore multimodal emotion detection using audio signals in the form of. wav audio clips and images that have been grey-scaled by employing appropriate advanced deep-learning techniques and previously available machine learning classifiers. By the efficient and succinct usage of various Machine Learning Architectures and techniques, it must be noted that separate datasets have been used for both images and audio. An effective combination of both further strengthens the possibility of achieving superior performance.

关键词： Deep learning Emotion recognition Computational modeling Virtual assistants Refining Transforms Feature extraction User experience Real-time systems Information technology

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Emotion Recognition in Visual Art on Color and Texture Analysis using Improved Vision Transformer 1

Emotion Recognition in Visual Art on Color and Texture Analy...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Suma, K.G. Balasm, Zaed Sasirekha, N. Veera Manikandan, P. Sandhya, K. Vellore Institute of Technology School of Computer Science and Engineering Department of Ai & Ml Andhra Pradesh India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Electronics and Communication Engineering Salem India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electrical and Electronics Engineering Mamallapuram India Tirupati India

ISBN: (纸本)9798331529246

Nowadays, Emotion Recognition plays a key role in social communication interaction in which human emotions are interpreted from facial expressions and speech. Several challenges occur in emotion recognition such as capturing context of any emotion also facial attributes make the recognition process complex. To overcome these drawbacks Improved Vision Transformer (IViT) classifier is proposed for emotion recognition. Initially, the input data is collected from the Japanese Female Facial Expression (JAFFE) to collect color features and visual features. Then, the input data is processed using min max scaling method which normalizes pixel values. After that, colour and texture-based features are extracted by utilizing Rotation Invariant Local Binary Patterns (RI-LBP) with Hue Saturation and Value (HSV) and Red Green Blue (RGB). Finally, classification is performed by IViT, which processes images as sequences of patches. Hence, the proposed IViT model achieved better output when compared to existing Visual-Audio Attention Network (VAANet) in terms of accuracy by obtaining 96.6% respectively. © 2025 IEEE.

关键词： Local binary pattern

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