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6th International Conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Musfiq, Md. Kowshik, Aktaruzzaman Utsha, Md. Mahdizzaman Ahmed, Jargis Tareq, Md. Ahsan Habib Green University of Bangladesh Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331531973

In Vehicular Edge Computing (VEC), efficient task offloading is essential for reducing latency and enhancing system performance, especially in resource-constrained environments. In this regard, Roadside Units (RSUs) can provide computation resources to vehicle users to execute offloaded tasks. However, RSUs are resource-constrained and could fail if many vehicular users offload tasks to certain RSUs. Hence balancing the load and distributing users among RSUs is required. In this paper, we considered a Mobile Edge Computing server (MEC) acting as an SDN that can observe the situation of the subordinate RSUs and map RSUs and users for computation offloading considering the delay constraint of the users. Due to the NP-hard nature of the formulated problem, we presented a meta-heuristic algorithm called Delay-aware Particle Swarm Optimization for task offloading (DAPSO). Where MEC as SDN will solve the task placement problem for vehicular users and direct users to offload tasks to the best possible RSU or the MEC server. Our experimental results demonstrate that the proposed DAPSO approach significantly reduces overall system delay and improves overall system efficiency compared to existing literature works. © 2024 IEEE.

关键词： Heuristic algorithms

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Plant Disease Detection Using Custom Deep Learning Model

Plant Disease Detection Using Custom Deep Learning Model

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International Conference on Business and Technology, ICBT 2024

作者： Miah, Parvez Haque, Abrar Ahbabul Hossain, Muhammad Iqbal Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (纸本)9783031674303

Plant diseases have been of great concern in the agricultural sector as they destroy crops and result in low yields, which is not ideal in a world where billions of people need it to survive. Plants can be saved from this kind of destruction if the disease is detected early on. One of the ways of detecting diseased plants is by looking at the leaves. Diseased leaves look different from healthy ones. It’s nearly impossible for humans to check for dying crops in vast fields, but the process can be automated through computer vision and deep neural networks for highly accurate detections. Our goal today is to create a custom, lightweight DNN model that would be fine-tuned for the specific task of classifying diseased plants by using images of their leaves. We will test three custom models. One of the custom models will use EfficientNet-B5 as the backbone and this will be used for comparison with the lightweight models that include MobileNet V2 and MobileNet V3 Large. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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EmoTa: A Tamil Emotional Speech Dataset 1

EmoTa: A Tamil Emotional Speech Dataset

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Thevakumar, Jubeerathan Thavarasa, Luxshan Sivatheepan, Thanikan Kugarajah, Sajeev Thayasivam, Uthayasanker Department of Computer Science and Engineering University of Moratuwa Colombo Sri Lanka

ISBN: (纸本)9798891762015

This paper introduces EmoTa, the first emotional speech dataset in Tamil, designed to reflect the linguistic diversity of Sri Lankan Tamil speakers. EmoTa comprises 936 recorded utterances from 22 native Tamil speakers (11 male, 11 female), each articulating 19 semantically neutral sentences across five primary emotions: anger, happiness, sadness, fear, and neutrality. To ensure quality, inter-annotator agreement was assessed using Fleiss’ Kappa, resulting in a substantial agreement score of 0.74. Initial evaluations using machine learning models, including XGBoost and Random Forest, yielded a high F1-score of 0.91 and 0.90 for emotion classification tasks. By releasing EmoTa as open-access, we aim to encourage further exploration of Tamil language processing and the development of innovative models for Tamil Speech Emotion Recognition. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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Predicting Stress, Anxiety, and Depression from Social Media Comments: A Holistic Multi-Modal Deep Learning and NLP Framework 1

Predicting Stress, Anxiety, and Depression from Social Media...

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1st International Conference on software, Systems and Information Technology, SSITCON 2024

作者： Singh, Kulvinder Kumari, Akanksha Sharma, Nakul Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350352931

In the current digital era, millions of people are affected by increased levels of stress, anxiety, and desperation making mental health a critical worldwide concern. Proactive approaches to early detection of these illnesses are necessary to address this dilemma. This research aims to create a mental health condition prediction model by utilizing deep learning, machine learning, and natural language processing (NLP) cutting edge methods. We use social media data to identify potential red flags for mental health issues early on. To build a strong framework, our suggested approach combines classic machine learning techniques like Random Forest and SVMs with neural networks, such as CNNs, LSTMs, and GRUs. Through an examination of language patterns and behaviors seen in social media content, our models provide insights into current trends in mental health. Experimental results demonstrate high predictive accuracy, highlighting the potential for early intervention. This research addresses the growing need for innovative, data-driven approaches to mental health care, facilitating timely support, personalized treatment, and improved outcomes for individuals and communities. © 2024 IEEE.

关键词： Electronic health record

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Novel image processing architecture using AWS 4th

Novel image processing architecture using AWS

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Mahesh, M. Kiran, V.K. Department of Computer Science NSS College of Engineering Kerala Palakkad India

ISBN: (纸本)9781032911571

Image Processing is quickly developing advancements today, with its applications in different parts of science. Traditional server-based solutions can be expensive and difficult to scale. Image management can be a challenging task for web developers, especially when dealing with large volumes of images. Instead of using the usual servers, we’re now using a more flexible and efficient approach called "serverless computing" to overcome some problems with the old concept. We specifically use Amazon Web Services (AWS) to make this happen, using things like AWS Lambda and S3 storage. Our system shows clear benefits, like handling more work, faster processing, and using resources more wisely. We tested our system and compared it to the old ways, and it turns out that serverless computing, especially with AWS, is a much better way to process images. Here we are going to process the image and get three various outputs Thumbnail image, cover image, and passport size image with a single processing time. This paper not only explains how we set up our system but also helps other people, like researchers and those working in the field, understand how to use these new cloud-based technologies for image processing, showing there’s room for more improvements in the future. © 2025 the Author(s).

关键词： AWS Lambda AWS(Amazon Web Services) S3-(Simple Storage Services)

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Internet of Things and Analysis of Data in Agriculture 4

Internet of Things and Analysis of Data in Agriculture

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Chauhan, Neeru Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350352931

Internet of things (IOT) is taking over the world and is rapidly modernizing and transforming the world. Dominant parts of India are dependent on agriculture as occupation and with current farming practices, it is not possible to meet the farming needs. This paper focuses on how IOT and big data is transforming agriculture. It aims to harness the potential of IOT and big data technologies to upgrade agricultural practices. The sensors gather information about the various parameters such as weather conditions, moisture present in the soil, humidity and other factors in real world environment. The results thus obtained are passed through the various tools used in analytics to achieve better understanding of the various patterns for outburst of diseases, amount of water to be used and increase in the yield of crop. Farmers can utilize this data to improve irrigation, fertilization, resulting in improved crop yields and less resource usage. © 2024 IEEE.

关键词： Smart agriculture

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A Review on Alzheimer Prediction using Machine Learning 1

A Review on Alzheimer Prediction using Machine Learning

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Rampal, Aakash Kapoor, Nitika Chandigarh University Computer Science and Engineering Department Mohali India

ISBN: (纸本)9798350392111

Alzheimer's disease (AD) is a devastating neurological disorder that progressively worsens cognitive function and memory. Early detection is critical to avoiding worse symptoms. While computational approaches have been used to identify Alzheimer's disease, they do have drawbacks. Predicting AD in its early stages is challenging but critical for effective therapy. Machine Learning (ML) offers the potential for improving AD. We want to learn more about how machine learning might help with early AD diagnosis, perhaps revolutionizing patient treatment, by reviewing significant studies. ML provides hope by analysing complicated data patterns and improving our capacity to detect AD sooner, resulting in better patient outcomes. Out of 432 total papers in total selected 15 papers were for this review analysis from different databases like IEEE, Web of science, etc. using keywords like "Alzheimer", "Machine Learning"etc. In this paper, a case study is also being added for the Support Vector Machine which has given an accuracy of 89%. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine and deep learning approaches for heart disease risk assessment 4th

Machine and deep learning approaches for heart disease risk ...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Rahul Deepika Khan, Reshma Department of Computer Science and Engineering Chandigarh University Mohali Gharaun India

ISBN: (纸本)9781032911571

Heart disease is a major public health problem the world over, and accurate measures of risk assessment and appropriate classification of persons are very necessary in the early identification for treatment. This is one reason that the techniques of machine learning and deep learning find great application in the healthcare business, as they can analyze patterns of data and make predictions very effectively. The following research paper provides deep research on the techniques of machine learning (ML) and deep learning (DL) used to evaluate and categorize heart disease illness risk. It describes the different problems of the heart, the factors of risk, and models of ML and DL approaches. It also outlines what the current data sources in use are: digital health records, wearable technology, imaging, genetic data, and patient–reported outcomes. The review observes that, though challenges continue to be present in data collection, processing, and feature selection, it further has the ability to make the model free from bias and reliable. Finally, the authors state that such models might play a role in better patient outcomes and appropriate use of health resources, therefore allowing for focused medical strategies for the treatment of cardiac diseases. © 2025 the Author(s).

关键词： Electronic health record

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Attention-Guided Energy-Based Model for Out-of-Distribution Data Detection 27th

Attention-Guided Energy-Based Model for Out-of-Distribution ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Zongjing Li, Yan Shin, Byeong-Seok Department of Computer Science and Engineering Inha University Incheon Korea Republic of

ISBN: (纸本)9783031783944

Detecting out-of-distribution (OOD) data is crucial for the safe and reliable deployment of deep learning models in open-world scenarios. While energy-based models (EBMs) have shown promising potential in OOD detection through the use of an energy function to capture the underlying probability distribution of data, previous approaches have primarily utilized logits or class probabilities from the fully connected layer to compute energy scores. However, logits are inherently class-specific and focus mainly on the relationship between the input and known classes, potentially ignoring the rich information embedded in raw feature representations that are essential for identifying OOD samples. This study introduces a novel approach that utilizes patterns within the feature space to calculate energy scores instead of relying on logits or class probabilities. We propose a spatial attention score to generate class-specific features for each category, which are then used to compute the energy score. Furthermore, we develop a new energy function that transforms these features into energy scores, significantly improving the OOD detection performance of EBMs. In experiments conducted on a Cifar-10 pre-trained ResNet-50, our feature-based energy score method reduced the average false positive rate at a true positive rate of 95% by 5.33% compared to the logits-based approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy-based model Out-of-distribution detection Uncertainty quantification.

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Enhancing Diabetic Retinopathy Diagnosis with ResNet-50-Based Transfer Learning: A Promising Approach

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Annals of Data science 2024年第1期11卷 1-24页

作者： Karthika, S. Durgadevi, M. Rani, T. Yamuna Department of Computer Science and Engineering SRM Institute of Science and Technology College of Engineering and Technology Vadapalani Campus Tamil Nadu Chennai India ACMS Railway Hospital ICF Chennai Tamil Nadu India

Diabetic retinopathy is considered the leading cause of blindness in the population. High blood sugar levels can damage the tiny blood vessels in the retina at any time, leading to retinal detachment and sometimes glaucoma blindness. Treatment involves maintaining the current visual quality of the patient, as the disease is irreversible. Early diagnosis and timely treatment are crucial to minimizing the risk of vision loss. However, existing DR recognition strategies face numerous challenges, such as limited training datasets, high training loss, high-dimensional features, and high misclassification rates, which can significantly affect classification accuracies. In this paper, we propose a ResNet-50-based transfer learning method for classifying DR, which leverages the knowledge and expertise gained from training on a large dataset such as ImageNet. Our method involves preprocessing and segmenting the input images, which are then fed into ResNet-50 for extracting optimal features. We freeze a few layers of the pre-trained ResNet-50 and add Global Average Pooling to generate feature maps. The reduced feature maps are then classified to categorize the type of diabetic retinopathy. We evaluated the proposed method on 40 Real-time fundus images gathered from ICF Hospital together with the APTOS-2019 dataset and used various metrics to evaluate its performance. The experimentation results revealed that the proposed method achieved an accuracy of 99.82%, a sensitivity of 99%, a specificity of 96%, and an AUC score of 0.99 compared to existing DR recognition techniques. Overall, our ResNet-50-based transfer learning method presents a promising approach for DR classification and addresses the existing challenges of DR recognition strategies. It has the potential to aid in early DR diagnosis, leading to timely treatment and improved visual outcomes for patients. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer

关键词： Blood vessels

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