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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Dinh, Tan Ngoc Nguyen, Hoang Minh Luong, Stanley Seyedmahmoudian, Mehdi Chandran, Jaideep Stojcevski, Alex School of Science Computing and Engineering Technologies Swinburne University of Technology Siemens-Swinburne Energy Transition Hub VIC Australia School of Science Computing and Engineering Technologies Swinburne University of Technology VIC Australia School of Science Engineering and Technology Rmit University Viet Nam Curtin Singapore

ISBN: (纸本)9798350353983

For efficient network management and anomaly detection, payload sizes in network traffic require accurate fore-casting. Conventional models, such as Linear Regression (LR), Multi-Layer Perceptron (MLP), and Autoregressive Integrated Moving Average (ARIMA), frequently fail to capture the intricate dependencies and multivariate relationships present in network traffic data. To overcome these shortcomings, this paper presents a novel forecasting model based on Long Short-Term Memory (LSTM) networks. Our multivariate LSTM model with three LSTM layers takes advantage of the multivariate nature of the data, incorporating multiple input features to improve forecasting accuracy. The suggested LSTM model performs much better than standard models in key performance parameters, such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). These findings are based on experimental results utilizing a real network dataset. Results demonstrate the LSTM model's excellent predictive power, which renders it a reliable tool for network management's future payload size forecasting. © 2024 IEEE.

关键词： Long short-term memory

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Hidden Emotion Detection from Speech and Text Using Deep Learning Approach 2

Hidden Emotion Detection from Speech and Text Using Deep Lea...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Reddy, Ravula Tarun Palaniswamy, Suja Bengaluru Amrita VishwaVidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350372892

The value that comes from accurate detection and understanding human emotions stands across a ton of domains. This paper details a few well-tested models applied in a unique way for discovering hidden emotion through audio and text data with the help of a custom dataset. Every sample in the dataset has an audio snippet and a Q&A pair which tries to evoke a certain emotion. The main goal lies in creating a model that can identify emotional indicators from diverse data sources. To achieve this a custom dataset was developed. The audio samples have been annotated with descriptions of emotions, and their corresponding transcripts have been similarly labeled for enhanced clarity and were able to achieve 81.44% accuracy in identifying hidden emotions. © 2024 IEEE.

关键词： Audio and Text Data Custom Dataset Emotion Detection

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Explaining Sentiments in Indian Legal Judgments with LIME and SHAP 8th

Explaining Sentiments in Indian Legal Judgments with LIME an...

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8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Babu, T. Hemanth Kiran, V. Gopi Bhat, Suhas S. Prabhakar, Priyanka Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

This paper presents a comparative study on sentiment analysis applied to legal texts, specifically legal opinions, and judgments. This paper uses embeddings such as T5, XLM Roberta and BERT, Roberta, and Legal BERT to convert the textual data into pre-trained language model word representation, to make it easier for ML algorithms to determine the sentiment (positive, negative, or neutral) of legal documents. The dataset was converted into embeddings, and various models, including KNN, ANN, SVM, random forest, XG boost, logistic regression, and decision tree, were employed for sentiment analysis. Each model was subjected to hyperparameter tuning using Grid Search CV, resulting in the highest accuracy of 66.66% with SVM on T5 embeddings. This research provides valuable insights into the emotional degrees within legal texts, demonstrating the effectiveness of our sentiment analysis approach. © IFIP International Federation for Information Processing 2025.

关键词： Embeddings

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Enhancing Smart Grid Security with Quantum Cryptography: A BB84-based Framework 3

Enhancing Smart Grid Security with Quantum Cryptography: A B...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Garg, Shail Gayatri, Yerukola Surya Kausthub, A. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The study investigates how quantum cryptography can be employed to improve security in smart grid communication networks. Attempting to use the BB84 QKD protocol, it allows secure creation and management of those cryptographic keys that cannot be broken with the help of quantum and traditional hacking techniques. It depends on symmetric encryption mechanisms like AES to provide high data confidentiality during transmission. Regarding the key management, the system provides a key management server that distributes keys to the grid nodes and performs key updating to ensure control with the managing authority. It also presents reliability under practical conditions through performance analogs such as Quantum Bit Error Rates (QBER), and system latency. This approach shows that quantum cryptography can provide a highly secure and efficient communication network for the next-generation smart grid. © 2025 IEEE.

关键词： Smart power grids

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Assessing the Diagnostic Accuracy of Vowel-Based Acoustic Biomarkers in Parkinson's Disease 3

Assessing the Diagnostic Accuracy of Vowel-Based Acoustic Bi...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Divya, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Parkinson's disease (PD) is a neurodegenerative disorder of progressive degeneration, with motor and speech impairments. In this study, we investigate the potential of vowel-based acoustic biomarkers for early detection of PD. The study analyses different vocal features including fundamental frequency, jitter, shimmer, and harmonic-to-noise ratio to identify small changes in voice based on the presence of PD. In the research, we use a dataset of voice recordings from PD patients and healthy controls and explore the effectiveness of machine learning models, including traditional feature engineering approaches. It is found that acoustic analysis, in particular explorations through glottal features and hybrid feature extraction techniques, can radically increase the accuracy of PD diagnosis. This kind of approach looks promising to develop affordable and non-invasive diagnostic tools, particularly in remote or resource-limited environments, thus allowing for earlier intervention and tailored treatment intervention for PD patients. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Efficient Detection of Acute Lymphoblastic Leukemia in Microscopic Blood Cell Images 12th

Efficient Detection of Acute Lymphoblastic Leukemia in Mic...

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12th International Conference on Big Data and Artificial Intelligence, BDA 2024

作者： Pavani, Bhupathi Vishva Thanmai, Gaddam Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031818202

Acute Lymphoblastic Leukemia (ALL), a type of blood cell cancer, rapidly progresses until it affects the white blood cells, compromising the human body’s ability to fight infections. Untreated Acute Lymphoblastic Leukemia (ALL) can quickly infiltrate vital organs, leading to life-threatening situations and its early treatment is essential to prevent the destructive spread and improve the survival chances. Recent deep-learning techniques help in timely diagnosis of the fatal disease with Convolutional Neural Networks (CNN) detecting Acute Lymphoblastic Leukemia (ALL) in patients by automatically analyzing microscopic blood cell images. Hence, this paper proposes a novel CNN-based approach employing the fine-tuned EfficientNet-B0 model enhanced using an optimally placed self-attention layer with HardSwish activation function. Experimentational analysis has also been done by employing self-attention layers at different architectural positions and with recent activation functions to develop an efficient and optimal model architecture. Several other CNN models such as MobileNet, EfficientNetB0, DenseNet, NASNet, InceptionV3, and Xception have also been trained to classifying blood cell images into normal and ALL classes for comparison purposes. The proposed model outperformed all other techniques with a huge margin of 5% and had the highest accuracy of 86.85%, precision of 87%, recall of 87%, and F1-Score of 87%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Keyframe Extraction Approaches for Intelligent Video Analysis 4

Keyframe Extraction Approaches for Intelligent Video Analysi...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sabarivasan, N. Thangavel, Senthil Kumar Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

Real-time violence detection is essential for protecting the safety and security of people, especially in college campuses that are dynamic and have crowds. Manual surveillance systems are popular but inefficient as they require human attention all the time and are not capable of quickly identifying and responding to violent *** the exsisting violence detection model takes quite a time for processing the entire video. The proposed model extracts features as keyframes from the video using MobileNet V3 and K-medoids clustering. The redundancy of video data can be avoided and significant violent activities identified by isolating and analyzing critical frames from video data. This enhances the efficiency and responsiveness of surveillance systems. This work primarily contributes a customized violent activities dataset and implementing keyframe extraction algorithm for optimizing the usage of computational resources, and incorporating lightweight algorithms for seamless deployment with existing CCTV networks. The experimental results clearly demonstrate the ability of this approach to detect keyframes, thereby offering a scalable and practical solution. © 2025 IEEE.

关键词： Security systems

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Path Planning for Autonomous Drones Using Proximal Policy Approximating Agent

Path Planning for Autonomous Drones Using Proximal Policy Ap...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Varshini Devi, I. Pravine Mukesh, R. Paayas, P. Varsha, P. Supritha, R. PrasannaKumar, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819795062

The advent of autonomous drones promises transformative applications across many industries, necessitating the ability to plan long-range paths automatically. The purpose of this paper is to explore the synergistic effect of Markov Decision Processes (MDPs) and deep reinforcement learning (DRL) in the context of autonomous drone racing. A simulated racing track is used to demonstrate how autonomous drones can optimize path planning using the Proximal Policy Optimization algorithm (PPO). In our study, we present an agent that efficiently navigates strategic checkpoints, is adaptable, and generalizes its skills to similar situations. It demonstrates the potential of DRL and PPO to advance autonomous drone path planning, paving the way for practical implementation in a wide range of industries. With drones reshaping industries, this work contributes to the exciting future of autonomous systems. The agent covered an average distance of 19.6 m per episode, showcasing its ability to navigate the test environment effectively. In a notable feat, the agent exhibited the capability to pass through a maximum of 9 holes in the test environment without encountering any collisions. This indicates a high level of adaptability and strategic decision-making, even in the face of increased environmental complexity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Markov processes

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Formal Modeling of Hybrid System Based on Semi-continuous Colored Petri Net: A Case Study of Adaptive Cruise Control System

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ACM Transactions on Embedded computing Systems 2025年第3期24卷 1-21页

作者： Yu, Wangyang Guo, Qi Cheng, Yumeng Liu, Lu Hao, Fei Zhai, Xiaojun Chen, Minsi The School of Computer Science Shaanxi Normal University Shaanxi Xi'an China The School of Computer Science Shaanxi Normal University Xi'an China University of Exeter Department of Computer Science Exeter United Kingdom University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom

Many Next-Generation consumer electronic devices would be distributed hybrid electronic systems, such as UAVs (Unmanned Aerial Vehicles) and smart electronic cars. The safety and risk control are the key issues for the sustainability of such consumer electronic systems. The modeling of hybrid electronic systems is difficult to be abstracted by traditional Petri Nets. This also makes the reachable marking graph unable to be applied to Petri Nets of the hybrid electronic systems. This paper proposes a novel Petri Net to model and analyze the hybrid electronic systems. We name it a Semi-continuous Colored Petri Net (SCPN) that inherits the excellent modeling capabilities and analysis methods of Petri Nets, and can formally depict hybrid quantities. In addition, we propose the construction algorithm for an SCPN reachable marking graph and prove its finiteness. Finally, we model and analyze an Adaptive Cruise Control (ACC) system of smart electronic cars as an example to prove the validity of SCPN. We use the proposed SCPN to model and analyze the running process of an ACC system under the continuous deceleration scenario of the front vehicle. The application study shows that the ACC system has logic flaws under the constant headway strategy when the front vehicle continues to decelerate. Based on this analysis, improvements to the SCPN of the ACC system are made, effectively enhancing its safety and logical correctness. Copyright © 2025 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adaptive cruise control

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