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Intelligent Routing for IoT-Driven MANETs Using Computational Intelligence 3

Intelligent Routing for IoT-Driven MANETs Using Computationa...

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3rd International Conference on Machine Learning and data Engineering, ICMLDE 2024

作者： Kaur, Ramanpreet Taneja, Kavita Taneja, Harmunish Dept. of Computer Science and Applications Panjab University Chandigarh India Dept. of Computer Science and Applications DAV College Chandigarh India

The interlinkage of the Internet of Things (IoT) with Mobile Ad Hoc Networks (MANETs) creates an autonomous communication ecosystem, imperative for applications ranging from disaster recovery to smart agriculture. On the other hand, this coalescing gives rise to crucial challenges, especially in energy efficiency, higher transmission rates, and network connectivity. As the devices in IoT-driven MANETs have restricted battery life, the optimum use of energy is vital for extending network lifespan. The global explosion of IoT devices demands rapid data transfer and constant connectivity. Moreover, frequent topological changes brought on by the high mobility devices in IoT-driven MANETs elevate the chances of link failure. These disruptive actions undermine network productivity resulting in reduced throughput and higher latency. Computational intelligence approaches offer credible solutions for resolving these concerns. This study presents a Unified Energy-aware Link Auto-configuration Routing Model (UELARM) for IoT-driven MANETs that favors energy-aware paths and the auto-configuration of broken links. UELARM exploits the flower pollination algorithm by incorporating three vital parameters such as energy cost, average community time, and packet loss to explore the optimal route. Additionally, for automatically configuring broken links in IoT-driven MANETs, UELARM presents a route repair approach based on mobility prediction, drawing inspiration from the termite colony optimization algorithm. The simulation results indicate that the UELARM outpaces other state-of-the-art routing algorithms regarding energy consumption, throughput, delay and network lifetime. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Vehicular ad hoc networks

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HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization of Biomedical Research Articles using Seq2Seq Transformers 23

HGP-NLP at BioLaySumm: Leveraging LoRA for Lay Summarization...

引用

23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Malik, Hemang Pradeep, Gaurav Seth, Pratinav Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Data Science & Computer Applications India Dept. of Computer Science & Engineering India

ISBN: (纸本)9798891761308

Lay summarization aims to generate summaries of technical articles for non-experts, enabling easy comprehension for a general audience. The technical language used in research often hinders effective communication of scientific knowledge, making it difficult for non-experts to understand. Automatic lay summarization can enhance access to scientific literature, promoting interdisciplinary knowledge sharing and public understanding. This has become especially important for biomedical articles, given the current global need for clear medical information. Large Language Models (LLMs) (Yao et al., 2024), with their remarkable language understanding capabilities, are ideal for abstractive summarization, helping to make complex information accessible to the public. This paper details our submissions to the BioLaySumm 2024 Shared Task: Lay Summarization of Biomedical Research Articles (Goldsack et al., 2024). We fine-tune and evaluate sequence-to-sequence models like T5 across various training dataset settings and optimization methods such as LoRA (Hu et al., 2021b) for lay summarization. Our submission achieved the 53rd position overall.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Multi-Layered Quantum-Resistant Algorithms Based Approach to Mitigate Emerging Threats in ISP Cybersecurity 3

A Multi-Layered Quantum-Resistant Algorithms Based Approach ...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Alagh, Arjun Wason, Ritika Arora, Parul Dept. of Computer Science and Applications Delhi India

ISBN: (纸本)9798331518592

The Internet Service Providers (ISP), and their structures are at high risk with the growing quantity and complexity of cyber threats. The growth of quantum computing further elevates the risks and challenges especially for the conventional cryptographic standards. This manuscript proposes a multi-layered suite of quantum-resistant cryptographic algorithms that can be proficiently integrated into ISP networks to secure against conventional cyber threats as well as prevent the impending quantum-enabled cyberattacks. This work emphasizes the importance of transitioning to quantum-resistant cryptography to ensure the longevity of ISP cybersecurity measures in the face of emerging quantum threats. © 2024 IEEE.

关键词： Encryption algorithms

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TEST-Web: An Automated Web Application Testing Tool for Enhanced Efficiency and Consistency 3

TEST-Web: An Automated Web Application Testing Tool for Enha...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Verma, Ishaan Wason, Ritika Arora, Parul Dept. of Computer Science and Applications Delhi India

ISBN: (纸本)9798331518592

This manuscript specifies the development of TEST-Web an automated testing tool projected to evolve the efficacy and reliability of web application testing process. Using the Selenium WebDriver and the TestNG framework, TEST-Web restructures the testing process by automating repetitive and comprehensive testing in large organizations. The tool generates detailed reports, providing valued visions into application performance and functionality. TEST-Web development follows an iterative approach, beginning with prototype development and then expanding functionality through user feedback. This work aims to achieve a user-friendly and robust solution for enhancing the accuracy, speed, and reliability of web application testing processes. © 2024 IEEE.

关键词： Automated Testing Tool Software Testing TestNG Web Application Testing

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Performance Evaluation of Multi-Disease Classification and Recommendation Models 3

Performance Evaluation of Multi-Disease Classification and R...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Singh, Lakhvinder Dalip Dept. of Computer Science & Applications Ambala India

ISBN: (纸本)9798331518592

This paper aims to review the current advances in healthcare uses of machine learning (ML) for the diagnosis of numerous diseases using different classification models. It proposes an efficient mechanism for multi-disease diagnosis by implementing the Bee-Featured Convolutional Neural Network (BFCNN), coupled with a Hybrid Statistical Fuzzy (Hybrid S-Fuzzy) scheme, to improve classification efficiency. The primary goal is to develop an accurate diagnostic system for heart, kidney, and liver diseases at early stages. The methodology presented compares several common ML models-Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Fast Condition Mutual Information (FCMIM-SVM)-using datasets from various sources with the BFCNN model. According to the simulated results, the proposed BFCNN model achieved a 96.7% accuracy rate and outperformed existing models in terms of sensitivity and specificity. This study suggests that using the BFCNN together with the Hybrid S-Fuzzy model enhances diagnosis for multiple diseases, offering higher accuracy and earlier identification compared to individual models, with up to 78% potential benefits in healthcare. Further studies on this model may lead to the application of deeper methods to increase diagnostic accuracy and improve treatment recommendations. © 2024 IEEE.

关键词： Convolutional neural networks

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Forecasting Traffic Air Pollution Increases from Urban Transportation Using Machine Learning in Metropolitan Cities-A Review 4

Forecasting Traffic Air Pollution Increases from Urban Trans...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Bhaskar, Hari Naga Vijaya Mayil, V. Valli KL University Dept. of Computer Science and Applications India

ISBN: (纸本)9798331523923

Rapid urban growth and industrial expansion in Indian cities have led to a 30% increase in vehicles over the past 5-10 years, causing a rise in air pollution and health risks. Existing methods to predict air quality face challenges like limited data, inaccurate models, slow predictions, and changing environmental conditions. This review explores how machine learning, including deep learning and hybrid models, can improve pollution forecasting. It also highlights issues such as unreliable sensors, high computing costs, and difficulty in understanding model results. Finally, the study suggests future directions for better air quality management using AI-driven predictions and policy improvements. © 2025 IEEE.

关键词： Air Pollution Forecasting Environmental Impact Machine Learning Metropolitan Cities Public Health Urban Transportation

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Enhancing Quantum Operations: Random Forests for Anomaly Detection and Alert Generation 7

Enhancing Quantum Operations: Random Forests for Anomaly Det...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Anoop, M. Nithya, B. SRM Institute of Science and Technology Dept. of Computer Science and Applications Chennai India Hindustan Institute of Technology and Science Dept. of Computer Applications Chennai India

ISBN: (纸本)9798350372816

A promising approach for enhancing the dependability and security of quantum processes is the integration of Random Forests, a traditional machine learning technique, with quantum anomaly detection and alert production. This approach involves training Random Forest models to inspect quantum data and spot abnormalities such as variations in quantum states, entanglement patterns, and the results of quantum processes. When anomalies are found, alarms are activated, categorized by severity, and sent out via several notification channels. Operators of quantum systems can act quickly by applying techniques for adaptive control, error correction, or system reinitialization. The rigorous preservation of historical data makes post-incident study and ongoing system improvement possible. This novel strategy strengthens security while simultaneously improving the performance of quantum systems, particularly in situations involving quantum communication. To guarantee its efficiency in the continually changing quantum landscape, careful design and ongoing monitoring are essential. © 2024 IEEE.

关键词： Quantum communication

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Stuxnet Unveiled: The Blueprint for Modern Cyber Conflict 3

Stuxnet Unveiled: The Blueprint for Modern Cyber Conflict

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rai, Preeti Nain, Vishal Dept. of Computer Science and Applications New Delhi India

ISBN: (纸本)9798331518592

The 2010 discovery of the Stuxnet malware signaled a sea change in the field of cyberwarfare, and the 21st century has seen an unparalleled spread of digital technology and their incorporation into vital infrastructure systems. Given this, Stuxnet is a sobering reminder of the weaknesses present in our globalized society. This paper aims to investigate the intricacies of the Stuxnet attack, including its origins, development, objectives, and significant implications for cybersecurity and international relations. By carefully analyzing this sophisticated cyberweapon, this study sheds light on how cyberthreats are evolving in the twenty-first century. © 2024 IEEE.

关键词： Critical infrastructures

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Feature Extraction Techniques and Their Role in Alzheimer's Disease Diagnosis: A Comprehensive Review 3

Feature Extraction Techniques and Their Role in Alzheimer's ...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rai, Preeti Ambooj Kukkar, Sania Dept. of Computer Science and Applications New Delhi India

ISBN: (纸本)9798331518592

Feature extraction is essential for breaking down complicated datasets into more digestible and useful formats, particularly in medical imaging. Accurate and timely diagnosis is critical in the context of Alzheimer's disease (AD), and MRI scan feature extraction algorithms have demonstrated significant potential. This study looks at a number of feature extraction methods, their applications across several fields, and their specific use in Alzheimer's disease diagnosis. By examining MRI-based research, we highlight important advancements and contributions in the field, emphasizing the techniques' potential to improve diagnostic accuracy. Despite these developments, there are still things that can be done to optimize feature extraction processes, such as reducing computational costs, enhancing model generalization, and refining feature selection for improved clinical outcomes. The paper continues by discussing the challenges of applying these methods in real-world clinical settings, including differences in patient demographics and imaging procedures. In conclusion, advanced feature extraction methods-particularly those that enable early detection, lower computing requirements, and provide more precise interpretations of MRI data-that employ deep learning and machine learning algorithms-have the potential to revolutionize Alzheimer's disease detection. The paper also makes recommendations for future research directions to get beyond current limitations and maximize the usage of feature extraction techniques for Alzheimer's disease diagnosis. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Detecting Feet's Bending Patterns With Smart Insoles 5

Detecting Feet's Bending Patterns With Smart Insoles

引用

5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Zhong, Yuchen Ying, Qijun Cai, Xiaohui dept. of Computer Science and Technology Hefei China dept. of Data Science Hefei China

ISBN: (纸本)9798350355925

Foot bending is a basic component of walking. Although bending is important for foot-ground interaction and ensures the speed and balance of the whole walking cycle, there is still no practical method for recording and analyzing foot bending under everyday scenarios. In this study we propose to use a smart insole, with two embedded Inertial Measurement Units, to acquire the movements of the fore-foot and the hind-foot and deduce the bending, represented by the bending point's position. We use the deep learning approach to estimate the bending position and its changes during different walking patterns. On the dataset containing 8 test subjects and 6 walking patterns, our method can achieve an overall mean absolute error accuracy of 2.58 cm. The smart insole can be with the subject all the day, bring in a new method for continuous foot bending monitoring. © 2024 IEEE.

关键词： Wearable technology

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