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An Intensive Review on Deep Learning Approaches in Non-Invasive Fetal Healthcare Monitoring 4

An Intensive Review on Deep Learning Approaches in Non-Invas...

引用

4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Harismithaa, L.R. Sadasivam, G. Sudha Psg College of Technology Dept. of Computer Science of Engineering Coimbatore India

ISBN: (纸本)9798331540364

Fetal health care is one of the most crucial concerns in today's world. Evaluating fetal well-being has always been challenging. Proper development and monitoring of the fetus are indispensable for its growth and healthy life. Monitoring and diagnosing abnormalities at the right gestational age is very significant. There are remarkable technical advancements in fetal well-being evaluation, bridging the gap between prenatal medicine and Artificial intelligence. This study provides an intensive review of various modes of non-invasive data that can be used for fetal health monitoring and decision support. Also, existing studies on fetal health care that use deep learning techniques for diagnosis and monitoring are explored. Also, a generic framework designed for an Automated Fetal healthcare Monitoring system using AI approaches is presented. © 2024 IEEE.

关键词： Fetal monitoring

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OpenVAS vs the Rest: Unveiling the Competitive Edge in Vulnerability Scanners 3

OpenVAS vs the Rest: Unveiling the Competitive Edge in Vulne...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Sharma, Mayank Desai, Divyanshu Arun, Aditya Ranganathan Priya, L. Rajagopalan, Narendran National Institute of Technology Dept. of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350381931

In an increasingly interconnected and digital world, robust vulnerability management is paramount to safeguarding critical information assets and maintaining the integrity of network infrastructures. This paper explores into the heart of vulnerability assessment by presenting a comprehensive analysis of OpenVAS, an open-source vulnerability scanning tool, and its distinct advantages over other competing solutions. OpenVAS has emerged as a standout contender in the realm of vulnerability scanners, owing to its unique features, exceptional performance, and a commitment to open-source principles. Our study begins by providing an overview of the vulnerability management landscape, highlighting the importance of timely and accurate vulnerability assessment. Key advantages examined in this paper include OpenVAS's extensive coverage of vulnerability databases, ease of customization, scalability, and its robust community support. Moreover, its open-source nature ensures transparency and flexibility, making it an attractive choice for both small businesses and large enterprises. This paper aims to serve as a valuable resource for cybersecurity practitioners, IT professionals, and decision-makers seeking a thorough understanding of vulnerability assessment tools. By shedding light on OpenVAS's unparalleled strengths, it provides insights that empower organizations to make informed choices for their cybersecurity posture. In an era where cyber threats evolve rapidly, OpenVAS emerges as the beacon of proactive vulnerability management, offering distinct advantages that redefine the standards for the industry. As cybersecurity assumes an increasingly critical role in the digital age, this paper underscores the significance of choosing a tool that not only detects vulnerabilities but also empowers organizations to address them effectively. © 2024 IEEE.

关键词： Cybersecurity

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Multiple language Discovery Abuse of Cyberspace with Advanced Transformer Architecture 2

Multiple language Discovery Abuse of Cyberspace with Advance...

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

作者： Singh, Arvind Tripathi, Abhinandan Buddha Institute of Technology Dept. of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

The number of reported instances of cyber abuse has also increased in tandem with the growth in the quantity of people that are regularly using the internet. Users' online freedom and privacy are jeopardized by such occurrences. Traditionally, the removal of such content from the internet has been accomplished through manual moderation and reporting systems. This strategy has, however, certain drawbacks, such as a reliance on people, longer delays, and less privacy for data. Traditional recurrent sequence models and supervised machine learning have been used in previous attempts to automate this procedure, but they have a tendency to perform badly on non-English material. A versatile solution that can handle multilingual text is urgently needed, given the increasing diversity of consumers using the internet. Moreover, translations frequently lose important context and emotion from content written in vernacular languages. In this work, we offer a generative deep learning based method for the detection of cyber abuse throughout Hindi, English with mixed codes, and Hindi text. The method uses bidirectional transformer-based BERT *** the TRAC-1 standard aggressiveness identification task, the suggested architecture can get extremely positive outcomes on the task leaderboard for English and accomplish cutting-edge performance on the Hindi dataset with mixed codes. The outcomes show that the new strategy is superior to the current ones because they were obtained without the need of numerous models or ensemble-based methods. Effective models that utilize deep learning techniques to analyze multilingual text can process a wider variety of inputs, making them indispensable in combating these types of social ills. © 2024 IEEE.

关键词： Generative adversarial networks

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A High Throughput Bloom Filter Based TCAM Architecture for Fast NIDS

A High Throughput Bloom Filter Based TCAM Architecture for F...

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2024 International Conference on Smart Electronics and Communication systems, ISENSE 2024

作者： Shaik, Sameera Noor Mahammad, S.K. IIITDM Kancheepuram Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331528126

One of the computationally most intensive tasks of an Network Intrusion Detection System (NIDS) is string/pattern matching where the payload of incoming packet is compared against predefined rule-set in order to detect any intrusion or threat. Although, various software algorithms and hardware architectures have been proposed to accelerate the performance of NIDS, wire-speed matching of these patterns still remains as a challenge. TCAMs are popularly used in many networking applications as they can perform the pattern matching in single clock cycle, but they always suffer with high power dissipation and delay due to their complex circuitry. Hence, most of the research focuses on efficient utilization of TCAM to cope up with high-speed links. In this paper, a high-throughput TCAM based architecture employed with Bloom filters is proposed. When a Bloom filter is deployed with TCAM, the number of patterns to be matched on TCAM reduces significantly since most of the patterns are eliminated in the prefiltering stage. There exist various Bloom filters such as Standard Bloom filter [1], Cuckoo filters [2], Counting Bloom filters [3] and Compressed filters [4]. Our proposed variant of Bloom filter outperforms all the existing filters in terms of speed while using less memory. We performed experiments on widely used open-source Snort NIDS and the proposed approach is able to achieve a throughput of 128Gbps for version 3.0c and 77.5Gbps for version 2.9.17. © 2024 IEEE.

关键词： Open source software

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A Random Forest Framework for Predicting Cardiovascular Disease in Diverse Populations' 2

A Random Forest Framework for Predicting Cardiovascular Dise...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Rahi, Parvez Kang, Sandeep Singh Singh, Ajay Pal Singh, Inderjeet Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798331519056

According to the latest information from WHO, the World Health Organization, heart disease remains a major global health challenge, resulting in millions of deaths each year. Heart disease presents in various forms, including Hypertensive Heart Disease, Congestive Heart Failure (CHF), Coronary Artery Disease, Cardiomyopathy, and Arrhythmia. Accurate classification of these conditions is crucial for reducing mortality rates and improving patient outcomes. This study utilizes an enhanced Random Forest classifier to improve the accuracy of heart disease classification. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to rectify the disparities in class. and Polynomial Feature engineering was employed to capture complex interactions within the data. The dataset underwent comprehensive preprocessing, including feature normalization and label encoding, followed by extensive hyperparameter tuning using GridSearchCV. The optimized model achieved a high accuracy of 92% and a Mean Squared Error (MSE) of 0.05. Key performance indicators, including an F1 score of 0.87, recall of 0.88, and accuracy of 0.92, highlight the model's efficacy in differentiating between various forms of heart disease. Future research could explore incorporating additional clinical data to further enhance the model's accuracy and applicability across diverse patient populations. © 2024 IEEE.

关键词： Clinical research

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Enhancing Agricultural Quality Control: Freshness Detection in Root Vegetables Through Deep Neural Networks 15

Enhancing Agricultural Quality Control: Freshness Detection ...

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

作者： Shorna, Aklema Akther Ave, Abida Sultana Mahi, Hasibul Hasan Chowdhury Shital, Sabrina Bari Sowrna, Nourin Jahan Nahid, Aynul Hasan Daffodil International University Dept. computer science and engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

In monarchy agricultural technology, this research pioneers the classification of freshness in root vegetables, a field that has been abruptly neglected in earlier research on a variety of vegetables. This study addresses this research gap and employs a transfer learning-based system to swiftly determine the freshness of root vegetables through images. To conduct this research, we create our own dataset which contains six different vegetables, 'Potato', 'Radish', 'Carrot', 'Onion', 'Taro', and 'Turnip'. Vegetables were collected from the local market and clicked manually by personal mobile phone, giving the dataset authenticity and real-world application. In total we collect 6384 images, divided into 3 classes: 'Fresh', 'Aged' and 'Rotten' based on their physical attributes and texture. Different types of transfer learning model VGG19, ResNet50, InceptionResNetV2 and DenseNet201 used to classify freshness of root vegetables. Although every model achieved decent accuracy, DenseNet201 achieved a remarkable result. DenseNet201achived highest accuracy 98.75% where data loss was 0.02%. Conversely, though ResNet50 achieved lowest accuracy 60.97% among all models. The variety of algorithms used seeks to determine the best course of action for this particular agricultural setting in addition to precisely classifying freshness. This research not only defines a novel benchmark but also sets the stage for future explorations in agricultural technology, ushering in a new era of precision farming for root vegetables © 2024 IEEE.

关键词： classification deep neural network freshness Root vegetable transfer learning

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Deep Learning for Tea Leaf Disease Detection: Comparing VGG16, Vision Transformer, EfficientNetB3, and Xception 15

Deep Learning for Tea Leaf Disease Detection: Comparing VGG1...

引用

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

作者： Ayon, Rokonozzaman Mojumdar, Mayen Uddin Mubin, Arif Shahriar Chakraborty, Narayan Ranjan Hridoy, Al-Amin Gazi Md. Jahidul, Islam Dept. of Computer Science & Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9798350370249

The need for efficient disease management in crop production is highlighted by the economic relevance of agriculture. Various leaf diseases cause major losses for tea, a staple product in many places. In this paper, a deep learning-based method is provided for the automated identification of illnesses affecting tea leaves. Using the unique architectural benefits of four models: VGG16, ViT (Vision Transformer), EfficientNetB3, and Xception, their performance is examined. Each model's accuracy and effectiveness in classifying diseases are evaluated by conducting a thorough series of experiments on a dataset of photos of tea leaves. The results show that ViT and VGG16 perform better than expected, with accuracies of 96.09% and 98.29%, respectively. These results highlight the effectiveness of transformer-based designs and conventional CNN in the identification of tea leaf disease. Our research also demonstrates how deep learning approaches can improve agricultural practices by enabling early and accurate disease identification, which would facilitate proactive disease control tactics and increase crop output. ©2024 IEEE.

关键词： Plant diseases

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Exploring the Efficacy of Deep Learning and Statistical Approaches in Temperature Forecasting 1

Exploring the Efficacy of Deep Learning and Statistical Appr...

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1st International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2024

作者： Tarunkumar, K. Umesh, Aditya Humbarwadi, Maithri Sohan, B.S. Bhargavi, M.S. Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (纸本)9798350391268

Temperature forecasting has become increasingly critical due to the rapidly changing climate, especially in urban environments where temperature fluctuations impact public health, infrastructure, and overall quality of life. Accurately predicting temperature patterns is difficult owing to the complexity of climate systems and the increasing variability caused by global climate change. This study addresses the issue by incorporating key weather features to forecast temperatures. The study investigates conventional statistical models, such as ARIMA and SARIMA, and cutting-edge methodologies for deep learning, such as Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Transformers. Among these, the Transformer model stands out because it uses its attention mechanism to detect precise trends in longitudinal data, making it ideal for high-precision temperature forecasting. By enhancing the accuracy of these predictions, the system provides an optimal tool for mitigating the impacts of climate change, helping urban areas better prepare for and adapt to temperature fluctuations. The study highlights the importance of advanced forecasting models in addressing the growing challenge of climate variability, contributing to more effective climate adaptation strategies. © 2024 IEEE.

关键词： Global warming

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An Analysis of Library Usage in the C++ Code Base of Fedora Linux 37

An Analysis of Library Usage in the C++ Code Base of Fedora ...

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2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： Deng, Jiachao Adams, Michael D. University of Victoria Dept. of Electrical and Computer Engineering VictoriaBC Canada

ISBN: (纸本)9798350362312

Several aspects of library usage in C++ are explored. A framework is proposed for analyzing the C++ code base of Linux distributions that employ the DNF package manager, such as Fedora Linux and Red Hat Enterprise Linux. This framework is used in conjunction with a C++ source-code analysis tool (developed by the authors) to study library usage with a fine level of granularity, considering instances of uses of types, type aliases, member/nonmember functions, variables, and enumerators. The effectiveness of our proposed framework and C++ analysis tool is demonstrated by applying them to the C++ code base of Fedora Linux 37. Using this approach, we were able to analyze library usage in the C++ source code of over 2300 software packages. Numerous observations are made about various aspects of library usage that can facilitate improved teaching of C++, allow more effective design/implementation of C++ libraries, and help to direct the future evolution of the C++ standard. © 2024 IEEE.

关键词： Linux

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Enhanced Security Measures for Image Privacy: Encryption Perspectives 2

Enhanced Security Measures for Image Privacy: Encryption Per...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Omshith, R. Balaji, Vemisetti Pavan Rahul, Rayapudi Venkata Kumaran, U. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. Using a variety of cryptographic techniques and algorithms, the original image is transformed into an encrypted format throughout this process, making it more difficult for unauthorized users to view or understand the content. Secure communication, picture sharing, medical imaging, and many more fields requiring the protection of sensitive visual data find widespread use in image encryption. A key technology in information security, picture encryption is intended to safeguard the integrity and confidentiality of digital photographs. It also discusses the difficulties and potential paths for picture encryption, highlighting the rising demand for reliable, effective, and secure image encryption solutions in a world that is becoming more and more networked and data-driven. Advanced Encryption Standard (AES) is more secure than (Exclusive OR) XOR, Fernet. © 2024 IEEE.

关键词： Advanced Encryption Standard Confidentiality Encryption Exclusive OR (XOR) Fernet Integrity

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