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Revitalizing the single batch environment: a ‘Quest’ to achieve fairness and efficiency

International Journal of computers and Applications 2024年第8期46卷 651-665页

作者： Manna, Supriya Mudigonda, Krishna Siva Prasad Dept. of Computer Science and Engineering SRM University AP Mangalagiri Andhra Pradesh India

In the realm of computer systems, efficient utilization of the CPU (Central Processing Unit) has always been a paramount concern. Researchers and engineers have long sought ways to optimize process execution on the CPU, leading to the emergence of CPU scheduling as a field of study. In this research, we have analyzed the single offline batch processing and investigated other sophisticated paradigms such as time-sharing operating systems and wildly used algorithms, and their shortcomings. Our work is directed toward two fundamental aspects of scheduling: efficiency and fairness. We propose a novel algorithm for batch processing that operates on a preemptive model, dynamically assigning priorities based on a robust ratio, employing a dynamic time slice, and utilizing periodic sorting to achieve fairness. By engineering this responsive and fair model, the proposed algorithm strikes a delicate balance between efficiency and fairness, providing an optimized solution for batch scheduling while ensuring system responsiveness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Efficiency

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An Overview on IoT in Electric Vehicles: Efficiency and Sustainability 2

An Overview on IoT in Electric Vehicles: Efficiency and Sust...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Gupta, Shashank Sounkhla, Indu Dubey, Ranjeet Kumar Yadav, Mithilesh Kumar Singh Yadav, Sandeep NIT Dept. of Computer Science and Engineering Jalandhar India KIPM Dept. of Computer Science and Engineering Gorakhpur India Buddha Institute of Technology Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Internal combustion engine (ICE) vehicles are very polluting and release high nitrogen oxides, carcinogens, and soot into the environment. ICE vehicles are the best choice in this era, but replacing ICE vehicles with electric vehicles (EVs) and automated vehicle control is a potential transition process. The most important turning point in the history of the automobile occurred with the entry of EVs into the vehicle market. The rise of the Internet of Things (IoT) paradigm in electric vehicles (EVs) has changed the way we look at large-scale transportation by making it intelligent and more sustainable. Recently, with the advent of connectivity through IoT platforms, IoT in EVs has improved performance and reduced environmental impact: from any on-off switch and adoption of IoT through our smartphones placed on the charging base. We can implement smart charging solutions for better energy consumption management, reduction of grid load, and battery life enhancement. IoT allows electric vehicles to communicate directly with charging stations and even the power grid, providing a better way to create balanced transportation networks. But implementing IoT in electric vehicles also raises some issues, including data security and user privacy. With all of these issues, there is huge potential in IoT usage with electric vehicles to help produce cleaner transportation and create a more sustainable future. © 2024 IEEE.

关键词： Electric Vehicles Internal Combustion engine Internet of Things

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Effective Machine Learning Model for Disease Recognition and Categorization of Mango Leaves

Effective Machine Learning Model for Disease Recognition and...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Kumar, Sandeep Gupta, Bhupesh Grover, Richa Chhabra, Mohit Computer Science & Engineering Dept. Ambala Mullana India Panipat Institute Of Engineering & Technology Computer Science & Engineering Dept. India

ISBN: (纸本)9798350306446

Maintaining the health and production of mango farms depends on the early detection of illnesses in mango trees. Despite the vital role agriculture plays, it remains an industry receiving limited attention from the machine-learning community. A noteworthy challenge is the lack of standardized and openly accessible agricultural datasets, impeding researchers from fully leveraging the potential of advanced computational prediction tools. The study focuses on employing deep learning and image processing techniques for diagnosing prevalent mango leaf diseases in India. The goal is to enhance both the quality and quantity of agricultural production by implementing effective disease control measures upon identifying plant diseases. The automation of plant disease detection proves particularly beneficial, reducing the monitoring efforts required by large farms. Rapid identification of leaf diseases is crucial, given that leaves serve as a plant's primary source of nutrition. Notably, none of the seven different mango leaf illnesses that have been observed in India have ever been the subject of earlier research. In this study, we provide a lightweight Resnet50 model that can reliably classify healthy mango leaves and the seven discovered illnesses on mango leaves. The performance of our proposed lightweight Resnet-50 model is evaluated against many pre-trained models, such as Exception, Resnet101, and VGG16. The findings reveal that the lightweight Resnet-50 model achieves the highest testing accuracy, reaching an impressive 95%. This underscores the model's effectiveness in automating the identification of mango leaf diseases, offering a promising solution for improving agricultural practices in India. © 2024 IEEE.

关键词： Diseases

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COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

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Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): Using MLAlgorithms 2

Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Juneja, Sonam Bhati, Bhoopesh Singh Atwal, Shikha Anand, Monika Maiti, Souvik Chandigarh University Dept. of Computer Science and Engineering Gharuan India Iiit Dept. of Computer Science and Engineering Sonepat India

ISBN: (纸本)9798350371055

The difficulties and ramifications of PCOS, which affects a sizable portion of women who are of reproductive age, are discussed in this review. The article covers the various clinical manifestations of PCOS, how it affects both reproductive and non-reproductive health, and how it's linked to psychological distress and metabolic disorders. It highlights how critical lifestyle modifications, early detection, and accurate diagnosis are. Additionally, the study presents machine learning techniques for PCOS detection, demonstrating the effectiveness of techniques like Random Forests, CNN, and SVM. An innovative CDSS that uses the Red Deer Algorithm shows encouraging accuracy. The necessity of continued research, diversified datasets, and cooperative efforts to enhance PCOS detection at the nexus of technology and healthcare is highlighted in the abstract's conclusion. © 2024 IEEE.

关键词： Deep Learning KNN Machine Learning PCOS Random Forest SVM

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Architectural Model of Deep Convolution Neural Network Approach for Brain Tumor Prediction

Architectural Model of Deep Convolution Neural Network Appro...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Panigrahi, Raghunath Sahu, Rekha Nayak, Suvendu Chandan Kabat, Manas Ranjan IMIT Dept. of Computer Science and Engineering Cuttack India Silicon University Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350369175

Magnetic Resonance Imaging (MRI) is most commonly used technique to detect a tumor by physicians in medical science. The MRI images are three-dimensional images with the number of slices that are analyzed to identify the defective cells in the naked eye that leads to a time-consuming process. Machine Learning (ML) techniques are investigated as a solution to improve tumor identification in a better way. Early and accurate detection is crucial for effective treatment planning and improving patient outcomes. Traditionally, brain tumor diagnosis relies on manual analysis of MRI (Magnetic Resonance Imaging) scans by radiologists, which can be time-consuming and prone to human error. The advent of deep learning, particularly Convolutional Neural Networks (CNNs), has opened new avenues for automating and improving the accuracy of brain tumor prediction. In this paper, we study the Convolutional Neural Network(CNN) model which is popular in image analysis. Similar way, we develop a CNN model to process and classify the MRI images to detect the type of brain tumor. We examine the model's performance by using 5712 MRI images from a standard dataset available in the public domain. The aim of the work is to classify the tumor in four classes like: Glioma, Meningioma, Pituitary and No Tumour using the proposed CNN model. Our model follows data preprocessing, image encoding, filtering and classification. The proposed model scores 78% accuracy as its performance in the dataset which needs more investigations and comparison with other techniques in further study. © 2024 IEEE.

关键词： Convolutional neural networks

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Secured Entry control: An IoT and Deep Learning based Mask Detection System for COVID-19 Suspected Patient Screening and Access Regulation 3

Secured Entry Control: An IoT and Deep Learning based Mask D...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Sultana, Abida Peyal, Hasibul Islam Islam, Md. Injamul Abdal, Md. Nazmul Miraz, Shafiun Kontho, Md. Mahin Rahaman Leion, Zannatul Mawa Mamun, Abdul Al Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh Tejgaon College Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

The new coronavirus SARS-CoV-2, which triggered the COVID-19 pandemic, has had an unparalleled effect on economies, cultures, and world health. In response to the critical need for strict COVID-19 screening systems in public areas, this study presents a creative Secured Entry control system. Detecting and controlling possible COVID-19 carriers attempting to enter the country is made possible by this system, which makes use of deep learning algorithms and IoT technology. The mask detection algorithm, MobileNetV2 model, has an exceptional validation accuracy of 98.96\%. The model's reliability is supported by performance evaluations using ROC curves, confusion matrix analysis, and an AUC value of 98.96\%, which is close to the optimal AUC score of 100\%. Because MobileNetV2 is well-suited for low-processing devices, it is easy to deploy it on Raspberry Pi, which helps to create an affordable system. Furthermore, by spotting increased body temperatures, a contactless temperature sensor improves the system's ability to identify possible COVID-19 carriers. The functioning of the system is confirmed by the working prototype that is presented in the Experimental Results section. The main goal of this research is to create an autonomous system that is affordable and selectively allows access to people who are less likely to transmit COVID-19 in public areas. To achieve the overall objective of reducing the spread of COVID-19 in public spaces, this research highlights the successful integration of mask identification algorithms and IoT. © 2024 IEEE.

关键词： COVID-19

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An Effective Deep Learning Approach for Kidney Stone Detection in CT Scan Images using Convolutional Neural Network

An Effective Deep Learning Approach for Kidney Stone Detecti...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Islam, Md. Nurul Khan Chowdhury, Md. Fuad Hasan Nusrat, Fariha University of Scholars Dept. of Computer Science and Engineering Dhaka Bangladesh United International University Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Kidney stones represent a significant global health concern, frequently causing individuals to seek immediate medical care due to intense pain. Radiological imaging modalities are one of the most common modes of diagnosis, but specialist interpretation is always important for accuracy. In this paper, we present computer vision in an automated kidney stone detection system that involves five stages: loading the dataset, preprocessing data, loading the InceptionV3 model, predicting, and evaluating performance. Various methods of preprocessing and data augmentation were utilized to make the model less biased and more robust. This study uses the InceptionV3 architecture to classify kidney stones in CT scan images of KUB (Kidney, Ureter, and Bladder). We utilize a total dataset of 433 participants, either diagnosed with kidney stones or in a normal state. 278 cases are diagnosed with kidney stones, and 165 are normal cases. The proposed approach achieved an impressive 98.55% accuracy across performance metrics when evaluated on an independent test dataset. © 2024 IEEE.

关键词： computerized tomography

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Privacy-Preserving Speech Analysis- A Review 6

Privacy-Preserving Speech Analysis- A Review

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Kumar, Pankaj Shukla, Mayank Malik, Medha Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798350374629

Speech-recognition technology is an essential component of many smart IoT devices. To make these systems secure and privacy-preserving, it is important to consider outsourcing speech-recognition functionality to a third-party storage provider. By removing the speech-recognition process from the device itself, the amount of personal data stored on the device is minimized. Additionally, the provider can ensure the strictest security standards are met when the audio data is stored, including the use of encryption and access control mechanisms. Furthermore, the provider can also ensure that the speech-recognition process is done using the latest technologies to ensure accurate recognition of the audio data. To ensure the end-user's privacy is kept intact, the provider should allow only authorized users to access the audio data. Additionally, the provider should also enable data access logging and audit trails allowing for the tracking of any access to the data. By outsourcing speech-recognition services to a third-party, smart IoT devices can ensure the highest levels of privacy and security while still enabling users to get the most out of their device. The server processes the encrypted data to generate recognition results, such as user commands and voice printing, without compromising the privacy of the user's voice data. To enhance voice data privacy, federated learning can be employed in the design of these models. This approach enables distributed training of the model without the need to transmit the user's voice data to a central server. The adoption of this approach in the IoT industry is gaining momentum as it offers the potential to create smarter and more secure IoT devices that inspire customer trust. ©2024 IEEE.

关键词： Speech recognition

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Trust Based Cooperative Secure Routing Protocols for Internet of Things (IoT) 2

Trust Based Cooperative Secure Routing Protocols for Interne...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Mekala, Sagar Ramji, Bhukya CVR College of Engineering Dept. of Computer Science and Engineering Hyderabad India Kakatiya Institute of Technology and Science Dept. of Computer Science and Engineering Hanmakonda India

ISBN: (纸本)9798350366570

The Internet of Things has rapidly emerged and continues to create services, software, sensors-embedded devices, and protocols. IoT allows physical objects to communicate, exchange information, and make decisions while performing critical jobs. The Internet of Things is enhanced by wireless sensor networks, which serve as a continuous layer. To gain adoption and spread, Internet of things-based sensor networks must overcome serious general and specialized risks and technological hurdles. The main drawbacks of wireless sensor networks are insufficient resources. Internet of Things (IOT)-based Wireless Sensor Networks have issues in energy consumption, longevity, throughput, routing, and security. The goal of this effort was to enhance network longevity, throughput, packet latency/loss, and better encounters with hostile nodes. Consider wireless energy harvesting in the proposed three-layer cluster-based wireless sensor network routing protocol to enhance network lifespan. The proposed approach is a Trust based Cooperative Secure Routing Protocol (TCSRP) technique with security system to obtain malicious activities of objects and to slant them into blacklist. Finally, experiments were done to test the methodology. It outperforms most traditional routing protocols like AODV, DSR, and DSDV. © 2024 IEEE.

关键词： Network layers

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