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Identification and Classification of Intestinal Parasitic Eggs in Animals Through Microscopic Image Analysis 3rd

Identification and Classification of Intestinal Parasitic Eg...

引用

3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Mishra, Ketan Kavitha, C. Kannan, Devi Department of Computer Science and Engineering Atria Institute of Technology Bangalore India

ISBN: (纸本)9789819986453

Intestinal parasitic infections in animals can cause a range of symptoms, including diarrhea, weight loss, anemia, and malnutrition. This project aims to classify parasitic eggs belonging to the Monezia and Strongyles species using microscopic images from a veterinary hospital. The dataset consisted of stool samples from animals infected with these parasites. Image processing and machine learning techniques, including data preprocessing, feature extraction, and feature selection are used to detect and classify the eggs. The goal was to accurately classify the eggs into either the Monezia or Strongyles species. To achieve the goal, two classification algorithms: K-Nearest Neighbors (KNN) and Decision Tree have been used. Using these algorithms, the model achieved accuracies of 91% and 60%, respectively. However, by incorporating Alyuda NeuroIntelligence for classification, a significantly improved accuracy of 92% was obtained. The results demonstrate that the use of Alyuda NeuroIntelligence in conjunction with machine learning techniques can greatly enhance the accuracy of parasitic egg classification. This approach can aid in the diagnosis and treatment of animal infections, improving animal health and well-being. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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MEC-EnergySaver: Unleashing Efficiency Through D2D and Data Compression 16

MEC-EnergySaver: Unleashing Efficiency Through D2D and Data ...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Ghosh, Anindita Mukherjee, Poulomi De, Tanmay National Institute of Technology Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350383119

The shift from basic cell phones to smartphones has ushered in intelligent devices, alongside a surge in mobile apps that reshape daily life. Simultaneously, technologies like data analytics, AI and IoT have enabled smart homes and advanced transport systems, demanding substantial computational power from mobile devices. To tackle this challenge, mobile-edge computing (MEC) has emerged as a solution. Unlike remote cloud computing, MEC offers computing services at the network's edge, allowing mobile users to transfer their computing tasks to MEC servers located nearby. However, MEC systems face challenges in efficient computation offloading and distributing resources in a way that reduces energy use and processing times. The evolution of mobile devices and the rise of data-intensive applications have led to the need for efficient computing solutions like MEC, with the potential to integrate data compression for energy savings. This paper introduced a two-phase approach for request servicing in MEC systems. The first phase prioritizes Device-to-Device (D2D) connections based on device proximity, efficiently pairing devices for data exchange. Unpaired devices are directed to the nearest MEC node. In the second phase, requests not fulfilled via D2D connections are handled by strategically positioned MEC nodes, ensuring comprehensive coverage and minimizing network energy consumption. © 2024 IEEE.

关键词： D2D communication data compression energy efficiency MEC

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Prediction of Heart Disease Risk in Early Ages with Boosting Techniques 1

引用

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Yadav, Jyotsna Rahman, Habib Ur Department of Computer Science and Engineering Kanpur Institute of Technology Kanpur India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

Heart disease is one of the top causes of mortality globally. Early detection can save a person's life. In previous five years, it is found that young individuals are also experiencing heart disease. In order to automate the medical industry and to forecast cardiac illness, artificial intelligence (AI) provides a prediction model. We could employ a number of AI algorithms to predict the risk of heart disease as in previous work, done in this field;generally, authors have used single classifiers. In this work, we have used two approaches of AI. In the first approach, single or base classifiers like logistical regression (LR), support vector machine (SVM) and decision tree (DT) are examined. In the second approach, boosting classifiers such as adaptive boost (AdaBoost), extreme gradient boost (XGBoost) and gradient boost are analyzed, and then comparison is done between single classifiers and boosting classifiers and found that boosting model achieved highest accuracy and the most accurate model as compared with single classifiers in predicting heart disease risk in youngers. So a type of ensemble model called boosting model is employed to prevent overfitting and to offer the highest accuracy as compared to single models. With the XGBoost classifier, we obtained 96.29% accuracy, with AdaBoost classifier, we obtained 91.35% accuracy, and with the gradient boost classifier, we achieved the highest accuracy of 98.76%, which is best in prediction of heart disease ages between 28 and 50. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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A Cost-Sensitive LSTM Model for Driving Risk Assessment from Vehicular Trajectory Data 100

A Cost-Sensitive LSTM Model for Driving Risk Assessment from...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Osho Pranay Kumar, Pradeep Chakraborty, Suchetana Indian Institute of Technology Jodhpur Department of Computer Science and Engineering India

ISBN: (纸本)9798331517786

Identifying risky driving behavior is crucial for early hazard detection, encouraging safer driving practices, and minimizing accident risks. Driving patterns, characterized by sensory data, can be used to classify risky behavior. However, effective classification into risk categories relies on supervised learning methods that require labeled data. The challenge lies in the high cost and difficulty of obtaining accurate groundtruth labels for these signatures. As a result, most datasets lack risk labels. Additionally, because risky incidents are infrequent during regular driving, the dataset collected from studies becomes imbalanced, containing fewer instances of risky events. This imbalance poses a significant challenge, as it biases the model towards the majority class, increasing the likelihood of costly misclassifications where risky instances are incorrectly identified as safe. To address this, we propose a three-stage method. First, we identify driving events indicative of risky behavior from the trajectory data and mathematically formulate them as potential risk indicators. Using these indicators, we then employ clustering to assign appropriate risk labels to the data. Finally, we tackle the class imbalance problem using a cost-sensitive LSTM model that combines a custom loss function with LSTM architecture to prioritize accurately classifying risky instances. Our method outperforms other state-of-the-art approaches with high accuracy, precision, F1 score, and recall of 98.13%, 95.2%, 96.6%, and 96.35%, respectively, effectively managing an imbalanced dataset. © 2024 IEEE.

关键词： Risk assessment

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Priority-Based MPTCP Scheduler for 5G and Beyond Heterogeneous Links 16

Priority-Based MPTCP Scheduler for 5G and Beyond Heterogeneo...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Gumasthi, Nikhil B. Baswade, Anand M. Indian Institute of Technology Bhilai Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Modern cellular devices often have dual interfaces for providing high-speed data transfer. This dual-interface design enhances versatility and user convenience. Dual interface technology allows 5G NR devices to connect to the 5G core using two separate base stations. One base station operates in the mmWave frequency range, and the other operates in the sub-6 GHz frequency range. However, millimeter-wave (mmWave) signals face challenges in terms of limited penetration through obstacles, such as buildings, and susceptibility to atmospheric absorption, leading to reduced coverage and potential signal degradation in adverse weather conditions. On the other hand, sub-6 GHz waves offer better penetration through obstacles and a broader scope compared to mmWave, making them suitable for providing reliable and expansive wireless connectivity in urban and indoor environments. Both mmWave and sub-6 GHz frequencies have distinct advantages and limitations;mmWave provides high data rates but has limited coverage, while sub-6 GHz offers a broader range but at lower data rates. However, in a heavy data transfer scenario, frequent loss in the mmWave link while transmitting packets can lead to packet drops (wireless medium), reducing the congestion window size and increasing the flow completion time. To mitigate this, we propose to use analytical hierarchy process (AHP) and grey rational analysis (GRA) based priority allocation mechanism to schedule packets on the most reliable link, thereby reducing the packet drops and flow completion time. © 2024 IEEE.

关键词： 5G NR Multipath TCP (MPTCP) network simulator-3 (NS-3) network simulator-3-mmWave priority scheduling

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MediMind: A Comprehensive Health Prediction and Record-Keeping Platform 6th

MediMind: A Comprehensive Health Prediction and Record-Keep...

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6th International Conference on Information Systems and Management Science, ISMS 2023

作者： Naik, Darshana A. Sharma, Aditya Mahajan, Danish Khandelwal, Dhruv Gajya, Arpit Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9783031707889

The "MediMind" project signifies a revolutionary fusion of predictive analytics, centered on diabetes and heart disease risk assessment, coupled with state-of-the-art skin disease prediction and robust Electronic Health Record (EHR) management. By employing advanced machine learning models like Logistic Regression and Support Vector Machines (SVM) for diabetes and heart disease predictions, alongside the cutting-edge Convolutional Neural Network (ResNet-50) for precise skin disease classification, "MediMind" achieves unparalleled accuracy and applicability. The predictive models exhibit exceptional performance metrics, boasting high accuracy, precision, recall, and F1-score. Seamless integration into Electronic Health Records (EHR) ensures immediate implementation in clinical decision-making, providing healthcare professionals with precise insights into diabetes and heart disease risks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electronic health record

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Energy-Aware Priority-Based Task Scheduling for Edge Devices in Internet of Things 5

Energy-Aware Priority-Based Task Scheduling for Edge Devices...

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5th IEEE India Council International Subsections Conference, INDISCON 2024

作者： Pawar, Vijaykumar Naik Mini, S. National Institute of Technology Department of Computer Science and Engineering Goa India

ISBN: (纸本)9798350376753

Billions of networked devices interact and collaborate with one another via the Internet as part of the Internet of Things (IoT). The performance and energy efficiency of IoT environments are greatly enhanced by effective task scheduling, especially for edge devices. In this paper, we present an energyaware, priority-based task scheduling algorithm specifically for edge devices in IoT networks, which prioritizes tasks according to their urgency and resource requirements while optimizing energy consumption to prolong device lifetime and lower operating costs. The model takes into consideration the dynamic nature of task demands as well as the varying energy profiles of edge devices. Simulations validate the efficacy of our approach, revealing that it achieves a substantial reduction in energy consumption while maintaining competitive task completion rates. © 2024 IEEE.

关键词： Scheduling algorithms

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SE-VGGNet: Liver Tumor Detection Using Squeeze Excited VGG Network 2

SE-VGGNet: Liver Tumor Detection Using Squeeze Excited VGG N...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Jose, Andrews Juliet, Sujitha Anitha, J. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India

ISBN: (纸本)9781837242672

Liver tumors, including metastatic cancer and cholangiocarcinoma, pose a significant threat to global health, necessitating accurate and timely detection. This paper presents a novel deep learning model, SE-VGGNet, for liver tumor classification, which innovatively combines Squeeze-and-Excitation (SE) blocks with a VGGNet backbone. This integration enhances feature extraction by allowing the model to recalibrate channel-wise feature responses, focusing more effectively on the most relevant image regions. SE-VGGNet was evaluated on a liver tumor dataset, achieving a classification accuracy of 99.09%, precision of 98.15%, and recall of 100%, significantly outperforming traditional CNN and VGG-16 models. The novelty of this approach lies in its ability to achieve high performance even with small datasets, addressing a critical challenge in medical imaging. Additionally, this method offers the potential for broader application to other medical imaging tasks, highlighting its adaptability and impact on early cancer diagnosis and clinical decision-making. © The Institution of engineering & technology 2024.

关键词： Deep learning

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A Hybrid Channel Access Mechanism for NR-U to Improve the Coexistence Performance 16

A Hybrid Channel Access Mechanism for NR-U to Improve the Co...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Akbar Basha, K.M. Gumasthi, Nikhil Baswade, Anand M. Indian Institute of Technology Bhilai Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The saturated licensed spectrum is insufficient to meet the increasing demand for data traffic, which paves the path for exploring new bands. Since the unlicensed bands are free of cost, efforts are made to offload the cellular data to unlicensed bands, which results in unlicensed versions of cellular technologies. New Radio Unlicensed (NR-U) is the unlicensed version of New Radio (NR). Since anyone can access unlicensed frequency, the devices follow listen before talk (LBT) to gain access to the unlicensed band. Unlike WiFi, the dominant technology in unlicensed bands, the cellular node can not transmit data immediately after the LBT procedure. The node has to wait for the next licensed slot boundary to occur since it is scheduled technology. However, the LBT procedure can end before the occurrence of slot boundary;the period after LBT to the slot bound boundary by sending a reservation signal (RS) or staying idle using a gap. The RS approach yields high fairness with less efficiency, whereas the gap-based approach gives better fairness with comparatively less fairness. To deal with the tradeoff in fairness and efficiency, we have proposed a hybrid channel access method that uses both gap and RS approaches. In the proposed method, the duration of RS is limited by a threshold, and the RS is transmitted as fragments. The proposed method achieves 54 % to 128 % higher fairness than the gap-based approach and 10 % to 28 % than the RS-based approach for the given simulation scenario. © 2024 IEEE.

关键词： Channel access Coexistence LBT NR-U Unlicensed bands Wireless networks

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Enhancing Deployment and Performance Measurement of Serverless Cloud Microservices with Warm Start 15

Enhancing Deployment and Performance Measurement of Serverle...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Amrutha, L. Jayalakshmi, D.S. Geetha, J. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

This paper introduces a comprehensive framework for deploying and evaluating the performance of applications in serverless cloud environments for microservices. It addresses challenges stemming from the distributed nature of microservices and the imperative to protect sensitive data. Key aspects covered include architectural design considerations, emphasizing decomposition into independent microservices with clear interfaces. Robust security mechanisms such as encryption, authentication, and authorization are discussed for safeguarding data shared between microservices. Deployment strategies on serverless cloud platforms like AWS Lambda are explored, highlighting benefits like automatic scaling and cost efficiency. The framework also integrates warm start techniques to mitigate cold start latency issues commonly associated with serverless computing. Techniques for monitoring and logging performance metrics such as responses times, error rate, and supply usage are discussed. Real-world case studies demonstrate the practical implementation and effectiveness of the framework in overcoming challenges associated with deploying an application in serverless cloud environments for microservices. © 2024 IEEE.

关键词： Cloud platforms

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