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Dynamic Real-Time Scheduling on Distributed Hierarchical Fog Networks 31

Dynamic Real-Time Scheduling on Distributed Hierarchical Fog...

引用

31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Sharma, Amit Auluck, Nitin Dept. of Computer Science & Engineering IIT Ropar India

ISBN: (纸本)9798331509118

We present a novel approach for efficient task scheduling on hierarchical fog nodes, catering to real-time (RT) and non-real-time (NRT) tasks with varying sizes and deadline constraints. Leveraging machine learning (ML) techniques, our proposed solution autonomously allocates tasks across fog nodes, dynamically adapting to changing workload patterns and system conditions. Our novel methodology integrates supervised learning algorithms to predict workload patterns and resource availability, enabling intelligent decision-making in task assignment. Our findings contribute to advancing the state-of-the-art in fog computing by offering a scalable and adaptive solution for dynamic task scheduling in hierarchical heterogeneous environments. © 2024 IEEE

关键词： Fog computing

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Smart Garden Watering and Lighting System Integrated with Machine Learning Model to Maintain Urban Gardens

Smart Garden Watering and Lighting System Integrated with Ma...

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Shahapure, Arsheyee Siddha, Rishika Tewary, Tanmay Shahapure, Aniruddha Mantri, Shamla Kamble, Aparna Dept. Computer Science and Engineering MIT-WPU Pune India

ISBN: (纸本)9798331512965

Garden maintenance may be a difficult task, particularly in urban settings. Many people are not skilled enough to determine their plants' precise requirements for lighting and watering. Conventional gardening techniques frequently result in underwatering or overwatering, wasting resources, and producing unhealthy plants. Our completely automated smart garden watering and lighting system offers a fully automated solution to these problems. With its sophisticated sensors, this system keeps an eye on the quantity of moisture in the soil and the amount of sunshine it receives, making sure plants get the correct amount of both for healthy growth. Traditional gardening involves guesswork and physical effort, which our solution avoids by automating the irrigation and lighting operations. You may have the advantages of a flourishing garden without the trouble of ongoing upkeep with our intelligent gardening system. Our method offers a dependable and effective solution to care for your plants and improve your urban green space, regardless of experience level. © 2025 IEEE.

关键词： Support vector machines

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Enhancing Artificial Intelligence and Machine Learning Understanding Through EnVision: A Virtual Reality Approach 1

Enhancing Artificial Intelligence and Machine Learning Under...

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Patra, Louise Kumari, Manisha Gopalapillai, Radhakrishnan Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350365016

The quality of teaching and learning process can be improved through innovative methods like use of virtual reality. We introduce EnVision, a groundbreaking Virtual Reality based approach to revolutionize Artificial Intelligence and Machine Learning (AI/ML) education. EnVision offers a unique approach where users build models through immersive learning experiences. Using interactive tutorials mixed with real-time data integration and visualization, users learn AI/ML concepts with ease. Through qualitative and quantitative methods, we evaluate EnVision's effectiveness and user satisfaction, contributing to the advancement of innovative educational gaming technologies. The evaluation results show that EnVision provides the AI/ML practitioners engaging learning experiences. © 2024 IEEE.

关键词： Virtual environments

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Deep Learning Model for Detecting the Presence of Neurological Conditions in MRI and CT Scans 2

Deep Learning Model for Detecting the Presence of Neurologic...

引用

2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Kanchan, Jayapadmini Ananya, H.N. Anoksha, N.A. Hegde, Sarika Dept. of Computer Science and Engineering Nitte Karkala India

ISBN: (纸本)9798350354461

An AI-powered MRI/CT analysis solution for medical professionals can significantly enhance diagnostic accuracy and efficiency. By harnessing deep learning algorithms, this solution can minimize human error and accelerate image interpretation. The incorporation of a Convolutional Neural Network (CNN) model empowers the solution to identify abnormalities in MRI/CT images with high consistency and reliability. This solution supports intricate case evaluations and integrates effortlessly with electronic health records, making high-quality diagnostic capabilities available to a wider range of healthcare professionals. The solution is meticulously designed to detect various abnormalities, including tumors, Alzheimer's, and hemorrhages from MRI images, as well as COVID-19 and checking for squamous conditions from CT images. Ultimately, this solution aims to improve patient outcomes through precise and prompt diagnoses, while also serving as an educational resource and contributing to ongoing medical research and innovation. The brain tumor MRI model achieved an accuracy of 87.5% after 100 epochs, while the chest CT scan model attained 95.65% accuracy in just 50 epochs. Brain hemorrhage MRI model accomplished 88.45% accuracy in 30 epochs, and the COVID-19 dataset model reached 78.9% accuracy in 40 epochs. Lastly, the Alzheimer's model obtained an accuracy of 80.70% after 40 epochs. © 2024 IEEE.

关键词： Convolutional neural networks

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Implementation of Fuzzy Logic Model to Solve Traffic Congestion Problem at Road Intersections

Implementation of Fuzzy Logic Model to Solve Traffic Congest...

引用

2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Negi, Vinay Jha, Sundaram Kumar Behl, Rachna Mriirs Dept. of Computer Science & Engineering Faridabad India

ISBN: (纸本)9798350382723

This research aims to tackle traffic congestion at intersections, primarily caused by static traffic light timings that don't adapt to changing traffic conditions. The proposed solution involves using adaptive fuzzy controllers, which can adjust their decisions based on fuzzy IF-THEN rules. These controllers employ both Mamdani and Sugeno fuzzy logic. The Mamdani method considers factors like the number of vehicles, queue length, emergency vehicle access, and road width to optimize traffic light timings and reduce unnecessary green signals. The Sugeno-type fuzzy logic enhances controller efficiency and decreases waiting times. The study also explores the application of direct adaptive fuzzy controllers to manage unstable systems, which is relevant in optimizing traffic control. Ultimately, the goal is to develop a simulator capable of predicting the right traffic light timings in real-time. This involves extending green light timings for lanes with high vehicle volumes and also for the emergency vehicles to alleviate congestion at intersections. © 2024 IEEE.

关键词： Traffic congestion

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Share-a-Meal: A Mobile App-based Food Sharing System 6

Share-a-Meal: A Mobile App-based Food Sharing System

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Uddin, Mohammad Salah Ansary, Fozlay Rabby Dept. of Computer Science and Engineering East West University Bangladesh

ISBN: (纸本)9798331522667

Many problems have been solved using the internet of things (IoT). Waste management, resource management, traffic management, financial services (e.g. stock rate prediction), logistics services, agriculture, etc. are the major sectors where IoT has played a fruitful role. The use of IoT is increasing day by day due to its immense advantages. This paper presented an IoT-based system for sharing leftover food with others or hungry people. The system consists of a mobile application and an embedded system. Cloud service is used as middle-ware between the mobile application and embedded system. Mobile application is used for sending and receiving notifications. The rectangular-shaped box type embedded system is responsible for food collecting and distribution. The development and prototype implementation of our proposed system are discussed throughout this paper. The system helps to share leftovers (food) with others which can reduce food wastage. © 2025 IEEE.

关键词： Cloud-Food Food waste Food-sharing internet of things Leftover food

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Advancing Multilingual Speaker Identification and Verification for Indo-Aryan and Dravidian Languages 1

Advancing Multilingual Speaker Identification and Verificati...

引用

1st Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, IndoNLP 2025

作者： Sritharan, Braveenan Thayasivam, Uthayasanker Dept. of Computer Science & Engineering University of Moratuwa Sri Lanka

ISBN: (纸本)9798891762145

Multilingual speaker identification and verification is a challenging task, especially for languages with diverse acoustic and linguistic features such as Indo-Aryan and Dravidian languages. Previous models have struggled to generalize across multilingual environments, leading to significant performance degradation when applied to multiple languages. In this paper, we propose an advanced approach to multilingual speaker identification and verification, specifically designed for Indo-Aryan and Dravidian languages. Empirical results on the Kathbath dataset show that our approach significantly improves speaker identification accuracy, reducing the performance gap between monolingual and multilingual systems from 15% to just 1%. Additionally, our model reduces the equal error rate for speaker verification from 15% to 5% in noisy conditions. Our method demonstrates strong generalization capabilities across diverse languages, offering a scalable solution for multilingual voice-based biometric systems. © 2025 Association for Computational Linguistics.

关键词： Speech recognition

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Stomach Cancer Prediction and Detection using Deep Learning: A Review 2

Stomach Cancer Prediction and Detection using Deep Learning:...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kaur, Sharandeep Goel, Paurav Kapoor, Nitika Chandigarh University dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798331505745

Stomach cancer, sometimes referred to as gastric cancer, is still one of the most common and widespread deadly disease worldwide offers a significant challenge in oncology due to late detection and high mortality rates. The advent of deep learning has revolutionized stomach cancer prediction by leveraging sophisticated models such as convolutional neural net-works (CNNs), multimodal learning and hybrid frameworks that integrate multiple data sources like medical imaging, histological slides, clinical records and genetic profiles and provide better performance with high accuracy. This review comprehensively ex-plores recent advances in deep learning applications for the detection of stomach cancer, highlighting their performance, accuracy, and clinical implications. Comparative analysis demonstrates that different deep learning models, such as ResNet50, DenseNet121, EfficientNetB5, and MobileNetV2, provide impressive results with accuracy up to 99% and higher sensitivity and specificity. It also indicates that the system accuracy needs to be improved to 100% in order to detect stomach cancer as early as possible. Early detection helps in the stoppage of further spreading of the disease and lowers the death rates. © 2025 IEEE.

关键词： Convolutional neural networks

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Synergizing Insights for Precise Rice Leaf Disease Diagnosis Via Multi-Modal Fusion 10

Synergizing Insights for Precise Rice Leaf Disease Diagnosis...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Pally, Shelly Chelsy Jenefa, J. Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350384369

Rice holds a significant position in India, especially in the southern part of the country, where people tend to eat some rice at least once a day. Farmers are facing a huge loss due to diseases in leaf, which is the main problem of agriculture. By using techniques like machine learning, main problems detection can be done. This review, discusses common plant diseases that affect the leaf. Some include Leaf Spots, Rusts, Fusarium Wilt, Early Blight, Powdery Mildew and Downey Mildew. Our research found that machine learning techniques on rice plants make finding diseases on leaves easier. Finally, we concluded that the most accurate method is the Enhanced VGG16, with an accuracy of 99.60% because it is really good at spotting diseases on rice leaves because it's great at recognizing the small details and patterns in leaf pictures. This helps it to tell the diseases apart more accurately and make fewer mistakes in identifying them. © 2024 IEEE.

关键词： Plant diseases

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Gender Classification from Thermal Images using CNN

Gender Classification from Thermal Images using CNN

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Gurram, Harika Kethineni, Keerthi Kommineni, Deva Harshini Soumya, Kakani VRSEC Dept. Computer Science & Engineering Vijayawada India

ISBN: (纸本)9798350361537

In this study, two deep learning architectures - AlexNet and InceptionV3 - are used to evaluate gender categorization using thermal imagery. One of the most promising techniques for classifying gender is thermal imaging, which offers distinct insights into physiological traits. Divided into separate training and testing subsets, the dataset is made up of carefully preprocessed thermal pictures. InceptionV3, known for its complex design combining inception modules, and AlexNet, a pioneering convolutional neural network (CNN), are refined on the training data to adjust to the complexities of gender categorization. Then, in order to measure the performance of the trained models, they are rigorously evaluated on the testing set. Specifically, their accuracy is evaluated. Experimental findings show that InceptionV3 performs better than AlexNet, with an accuracy of 92.3% as opposed to 82.6% for AlexNet. This significant disparity highlights how much better InceptionV3 is at identifying subtle thermal patterns and characteristics, which improves gender categorization accuracy. The work makes a substantial contribution to the rapidly developing area of thermal imagingbased gender categorization by highlighting how crucial it is to use advanced deep learning architectures in order to improve performance and accuracy. Subsequent investigations might delve into novel methodologies, including multi-modal fusion or sophisticated methods, to enhance precision and resilience in tasks using thermal-based gender categorization. © 2024 IEEE.

关键词： AlexNet Convolutional Neural networks Deep learning Gender classification Inception V3 Thermal images

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