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An efficient scalar multiplication algorithm on Koblitz curves using τ3-NAF

International Journal of Security and Networks 2022年第4期17卷 240-246页

作者： Ganesh, Ponnuru Surya Padmavathy, R. Pinapati, Anil Computer Science and Information Security NIT Warangal India Department of Computer Science and Engineering NIT Warangal India Department of Computer Science and Engineering NIT Calicut India

Elliptic curve cryptography (ECC) is an efficient and widely used public-key cryptosystem. It uses relatively shorter keys compared to conventional cryptosystems hence offering faster computation. The efficiency of ECC relies heavily on the efficiency of scalar multiplication which internally depends on the representation of the scalar value. Based on the representation, the number of point additions and point doublings varies. Koblitz curves are binary elliptic curves defined over F2 and also known as anomalous binary curves. Scalar multiplication algorithms on these curves can be designed without any point doublings. In τ-NAF representation, we need 0.333 m point additions whereas in τ2-NAF it is 0.215 m. This paper proposes a method to improve the efficiency of scalar multiplication on Koblitz curves using τ3-NAF representation that further reduces the point additions to 0.143 m. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Efficiency

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Railway track fault detection using optimised convolution neural network

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International Journal of Reliability and Safety 2024年第2期18卷 163-186页

作者： Chitra, R. Bamini, A.M. Anusha Brindha, D. Jegan, T.M. Chenthil Kirubakaran, S. Stewart Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Mechanical Engineering Sri Ranganathar Institute of Engineering and Technology Tamil Nadu Coimbatore India

Railway accidents are an under-scrutinised cause of death in India. Train accidents are caused by various consequences of collisions, derailments, signal errors and so on. Furthermore, when train derailments become disastrous, they can have tremendous repercussions. It is practically difficult to identify the cause of the derailment efficiently within a limited period. In recent years, we have been making progress in reducing derailments, but even if not deadly, identifying faulty tracks can waste a lot of time and money. And doing this error-free is a pressing matter, as tracks always experience wear and tear with more usage. Here is where neural networks can pitch in their solution. We can train a model to look at train tracks and identify any issues. This paper goes into the methodology of achieving this and optimising a neural network to predict problems in the track with the best possible accuracy that images can provide. The objective of this paper is to identify, develop and optimise neural networks to detect faulty tracks. In this work, a good Convolution Neural Network model is developed to identify the crack in the railway track. The developed model produced 95.54% accuracy in fault classification. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Derailments

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AI-Powered Fungal Detection In Dried Chilli: A Deep Learning Approach 4

AI-Powered Fungal Detection In Dried Chilli: A Deep Learning...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Indira Priyadharshini, M. Pavithra, R. Mahalakshmi, M. Mohanapriya, S.U. Jasiya Yasika, S. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354218

The project aims to create an automated system for detecting fungal contamination in dried red chilies using advanced deep learning algorithms such as Convolutional Neural Network (CNN), Visual Geometry Group (VGG16), and Residual Neural Network (ResNet). Fungal contamination poses a significant threat to the quality and safety of food products, and its early detection is crucial for ensuring consumer health and preventing economic losses in the agricultural sector. To train and evaluate the performance of the CNN, VGG, and ResNet models, the proposed system uses a dataset that includes custom images of dry red chilies, both contaminated and uncontaminated. The CNN is used to extract spatial features, whereas the VGG model is known for its depth and efficacy in image recognition tasks. The proposed system employs a dataset comprising custom images of dry red chilies, both contaminated and uncontaminated, to train and evaluate the performance of the CNN, VGG, and ResNet models. The CNN is used for its ability to extract spatial features, and the VGG model is known for its depth and effectiveness in image recognition tasks. Additionally, ResNet is employed to capture temporal dependencies in the dataset, considering the potential evolution of contamination over time. The study involves a comprehensive comparison of the output accuracy of these three algorithms, considering metric accuracy. The findings from this research contribute to the development of a robust and reliable automated system for the food industry, facilitating timely and accurate identification of fungal contamination in dry red chilies. This work aims to assist in the enhancement of food safety measures and the overall quality control processes in the agricultural and food processing sectors. © 2024 IEEE.

关键词： Convolutional neural networks

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MRI Scans for Deep Learning-Based Chronic Nephropathy Detection: A Comparison of CNN, MobileNet, VGG16, and ResNet-50 Models 5

MRI Scans for Deep Learning-Based Chronic Nephropathy Detect...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Amanatulla, M.D. Swathi, G. Pallavi, M. Bindu, K. Pavana Computer Science & Engineering Department Lakkireddy Balireddy College of Engineering Mylavaram India

ISBN: (纸本)9798350361155

Prolonged Nephropathy Given that detection usually shows no signs in its early phases and has a high morbidity rate, it presents a serious danger to world health. This research paper proposes a machine learning architecture that uses models based on deep learning (CNN, MobileNet, VGG16, ResNet-50) to identify illness using MRI data. 12, 446 images in the dataset obtained through Kaggle are categorized as normal, cyst, stone, and malignancy. The study evaluates the models' performances, and ResNet-50 exhibits the highest accuracy. The VGG16 and ResNet-50 designs for the proposed system are presented, with a focus on their characteristics. Pre-processing, instruction, classification, and collection of data are all included in the implementation. Users may upload their photos to be classified by different algorithms. The results demonstrate the effectiveness of the ResNet-50 model, which had an overall 98% classification accuracy for renal diseases. In conclusion, the application of deep learning specifically, the ResNet-50 model shows promise as a quick and dependable technique for detection, therefore alleviating a major healthcare issue. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Implications of the forthcoming forensic sciences standard ISO/IEC 21043 for forensic biometrics 12

Implications of the forthcoming forensic sciences standard I...

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12th International Workshop on Biometrics and Forensics, IWBF 2024

作者： Meuwly, Didier University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Enschede Netherlands

ISBN: (纸本)9798350354478

The forthcoming forensic sciences standard ISO/IEC 21043 is a methodological and technical standard, currently at the stage of Draft International Standard. When adopted, it will apply to all forensic disciplines, including forensic biometrics. This standard has been designed by forensic, legal, and standardisation professionals to ensure that forthcoming forensic services, products, and systems are safe, reliable, and consistently perform as intended. The aim of this contribution is to present to the forensic biometric community some of the implications of adopting ISO/IEC 21043 for the forensic biometric *** an introduction (§ I) and a review of the existing international standards applied in forensic science (§ II), the five-part structure of ISO/IEC 21043 is described: 1. Terms and definitions, 2. Recovery, 3. Analysis, 4. Interpretation and 5. Reporting, (§ III). Then a selection of requirements and recommendations of this five-part standard are highlighted, and their implication for forensic biometric research, development, implementation, and practice is discussed (§ IV).The conclusion is that the adoption and use of ISO/IEC 21043 will help forensic laboratories to reach accreditation and to enhance their existing quality management systems. It will also make it easier for forensic biometric researchers to develop, deliver, and implement more relevant, more reliable, and safer services, products, and systems to the forensic biometric examiners performing casework (§ V). © 2024 IEEE.

关键词： Forensic science

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FHarmony- An AI Based Personalised E-commerce Application 4

FHarmony- An AI Based Personalised E-commerce Application

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Deepika, R. Monish, J. Daniel Felix, V. Bharath Kumar, A. Sri Sairam Engineering College Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331508845

Unofficially, Agriculture is now considered to be one of the least profitable jobs one could do. The main reason behind this is that the livelihood of farmers which is based on the only source of their income is being affected. Most of their income is decided by the Intermediates. The price of the cultivated crops is being decided by the third parties rather than being decided by Farmers itself. An application to overcome the existing intermediate system in the field of agriculture by connecting farmers with consumers directly through this app can provide a solution the above problem. Giving the control of fixing the price of harvested products by the farmers itself through this app can help solving the abovementioned problem. Our idea is to create an AI based Application that provides a direct link between the customers and the cultivators, which helps the farmers to decide their price for their products with a reasonable amount of profit. By this, customers can directly avail products from farmers for a reasonable price. Our target are the cities near cultivable lands, where we establish a hub for storing and delivering goods. Customers can either pick up their orders directly from these hubs or opt for door delivery. This App contains personalised User Interface (UI)for both Farmers and Consumers. That works on the principle inspired from REVERSE BIDDING. Also have included features such as Artificial Interface (AI) based recommendations of products, user friendly chatbots. © 2024 IEEE.

关键词： Artificial Intelligence E-Commerce Farming Reverse Bidding

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Face Mask Detection and Alert System 1

Face Mask Detection and Alert System

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Singh, Sukhvir Rawat, Daksh Narang, Harshit Das, Purushottam Dhanalakshmi, R. Yadav, Satya Prakash Graphic Era Hill University Computer Science and Engineering Dehradun India Indian Institute of Information Technology Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350371802

The COVID-19 pandemic has underscored the importance of preventive measures, with the use of face masks being a crucial aspect of controlling the virus's spread. Thus, in the response to the global imperative to curb the spread of infectious diseases, particularly in the context of the COVID-19 pandemic, this research paper delves into the realm of face mask detection and alert systems. With the ubiquity of technology, the development of automated solutions for ensuring adherence to mask-wearing protocols has become most necessary. This paper provides a thorough examination of existing methodologies for face mask detection, ranging from image& video-based approaches to sophisticated deep learning models. Additionally, explores the integration of alert systems into these detection frameworks, emphasizing real- time processing considerations and user interface design. The review incorporates case studies, ethical considerations, and privacy implications, offering a comprehensive overview of the current state of the art. By elucidating the challenges, successes, and future directions, this research paper contributes to the discourse on leveraging technology for public health measures and sets the stage for continued advancements in face mask detection systems and also provides a comprehensive review of the state-of-the-art in face mask detection technologies and explores the design. © 2024 IEEE.

关键词： COVID-19

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Detecting Offensive Language in Tamil YouTube Comments

Detecting Offensive Language in Tamil YouTube Comments

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International Conference on Computing and Machine Learning, CML 2024

作者： Arul Antran Vijay, S. Tanush, K. Udhayarajan, M. Jishnu, B. Suwinkumar, T. Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9789819775484

Online content moderation faces significant challenges in identifying offensive language across diverse linguistic environments. This study compares the performance of five advanced BERT models mBERT, BERT Base, BERT Large, DistilBERT, and RoBERTa in detecting offensive language specifically in Tamil YouTube comments. Through meticulous evaluation using metrics like accuracy, precision, recall, and F1-score, we analyze the nuanced processing capabilities of these models tailored to address the complexities of the Tamil language. Among the models assessed, mBERT emerges as a computationally efficient option due to its multilingual efficacy, while RoBERTa demonstrates impressive accuracy with fine-tuning on a Tamil-specific corpus. BERT Base exhibits solid performance, albeit with higher computational demands, and BERT Large showcases enhanced accuracy at the cost of increased resource requirements. DistilBERT presents a memory-efficient alternative, maintaining reasonable performance with a smaller model size. Nonetheless, all models face challenges inherent in processing Tamil YouTube comments, such as code-mixing and informal language usage. This comparative analysis not only aids in selecting the most suitable BERT model for Tamil language content moderation but also sheds light on the broader landscape of offensive language detection in multilingual contexts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Natural language processing systems

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Liver Disease Prediction Using Ensemble Machine Learning Techniques

Liver Disease Prediction Using Ensemble Machine Learning Tec...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Mohammad, Manzoor Rout, Saroja Kumar Kumar, Kottu Santosh Sahu, Ruchismita Barik, Shekharesh Reddy, S. Ranjith Hyderabad India Koneru Lakshmaiah Educational Foudation Department of Computer Science and Engineering Vaddeshwaram India Department of Information Technology Hyderabad India Templecity Institute of Technology and Engineering Department of Computer Science & Engineering Bhubaneswar India Driems University Department of Computer Science & Engineering Cuttack India

ISBN: (纸本)9798350373783

Every person's health depends on the availability of affordable, high-quality medical treatment. This study highlights the significance of feature selection processes and classification algorithms in liver disease prediction. The main goal is to enhance healthcare outcomes by applying a prediction model to the Indian Liver Patient Dataset (ILPD) from the College of California, Irvine database. This dataset contains several characteristics that are used as inputs to the risk level of liver illness prediction, including age and direct bilirubin. Many classification techniques are used and their accuracy is assessed to accomplish this goal, including k-Nearest Neighbors, AdaBoost, XGBoost, Gradient Boosting, and Support Vector Machines (SVM). The purpose of putting these algorithms into practice is to improve diagnostic capacities so that liver disorders can be identified and treated early. In addition, the research includes a comparative analysis of classifier outputs, taking feature selection and non-feature selection scenarios into account. A crucial component of prediction model accuracy is featuring selection, which aids in determining the most pertinent qualities. The goal of the study is to enhance healthcare services by predicting liver illness, with a focus on the significance of precise and user-friendly diagnostic instruments. The results of this study have the potential to help policymakers and medical professionals create more successful care and preventive plans for liver disease, which would ultimately benefit a larger population. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Hydroponic Systems with IoT: Optimizing pH, Temperature, and Humidity Parameters 3

Enhancing Hydroponic Systems with IoT: Optimizing pH, Temper...

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3rd Odisha International Conference on electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Debdas, Subhra Jain, Akshat Mariam, Anika Maiti, Anurati Borah, Anushka School of Electrical Engineering Kiit University Bhubaneshwar India School of Computer Science Engineering Kiit University Bhubaneshwar India School of Computer Science Engineering Kiit Kiit University Bhubaneshwar India

ISBN: (纸本)9798350354379

Hydroponic systems integrated with Internet of Things (IoT) technology offer precise monitoring and control capabilities vital for optimizing plant growth. This research paper investigates the implementation of IoT in hydroponic setups, fo- cusing on monitoring pH, temperature, and humidity parameters to ensure optimal growth conditions. The IoT system continuously measures these parameters and compares them against prede- fined optimal ranges. If the measured values deviate from the optimum, the system triggers an error notification, indicating the need for corrective action. Through a series of experiments and simulations, this study evaluates the effectiveness of the IoT-based monitoring system in maintaining ideal environmental conditions for hydroponic cultivation. The research aims to contribute to the advancement of precision agriculture by providing insights into the integration of IoT technologies to enhance crop yield and quality in hydroponic farming. © 2024 IEEE.

关键词： Hydroponics

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