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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： E Shrenitha M Manvith Reddy D Sushanth M A Jabbar Computer Science and Engineering (AI and ML) Vardhaman College of Engineering Shamshabad Hyderabad

Among Cancers lung cancer is one of the most prevailing and death-dealing cancers and the early detection of lung cancer poses a significant challenge due to its often asymptomatic nature, leading to high mortality rates. We introduce a novel method for precise lung cancer detection from CT images, leveraging deep learning techniques. Our approach combines Recurrent Residual Convolutional Neural Networks (RRCNN) like RU-Net and R2U-Net with Long Short-Term Memory (LSTM) networks and VGG16 architecture. Incorporating LSTM and Recurrent Residual Convolutional layers enhances the model's ability to capture temporal dependencies and improve feature representation for segmentation tasks. Additionally, VGG16's strong feature extraction capabilities aid in identifying complex patterns and subtle abnormalities in lung images. Evaluation on the LUNA16 dataset, comprising 888 CT scans with 1186 annotated lung nodules, demonstrates our method's superiority, achieving an impressive 90% accuracy. Our findings underscore the crucial role of temporal information in distinguishing between benign and aggressive lung cancer cases. In conclusion, our study highlights the effectiveness of combining RRCNN, LSTM, and VGG16 for accurate lung cancer detection, offering promising prospects for early diagnosis and improved patient outcome.

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Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

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computers, Materials & Continua 2024年第1期78卷 1443-1460页

作者： Sitara Afzal Imran Ullah Khan Irfan Mehmood Jong Weon Lee Mixed Reality and Interaction Lab Department of SoftwareSejong UniversitySeoul05006Korea Faculty of Engineering and Digital Technologies School of Computer ScienceAI and ElectronicsUniversity of BradfordBradfordUK

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ***,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and *** this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image *** article explores the potential of seeing-through vehicles as a solution to enhance overtaking ***-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of *** address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both *** server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front *** see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other *** network was trained and tested on the Cityscape dataset using semantic *** transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has *** our findings,we have achieved 97.1% *** article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

关键词： Overtaking safety augmented reality VANET V2V deep learning

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Support Vector Regression Based Traffic Prediction Machine Learning Model∗ 8

Support Vector Regression Based Traffic Prediction Machine L...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Srivastava, Ansh Singh, Mrigaannkaa Nandi, Somesh Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India Rv College of Engineering Dept. of Electronics and Communication Bengaluru India Rv College of Engineering Dept. of AI/ML Bengaluru India

ISBN: (纸本)9798331505462

To combat the rising issue of urban traffic congestion, optimization of traffic light timings at intersections is crucial. Traditional methods show stagnancy in managing traffic flow dynamically. This study introduces Support Vector Regression (SVR) as an unconventional solution for traffic signal optimization. The model predicts the traffic conditions and alters the green light to turn on and off accordingly. Utilization of a comprehensive dataset consisting of vehicle distributions and critical flow ratios have been implemented to training a machine learning model. The statistics and performance is checked by usage of Mean Squared Error, R Squared, Mean Absolute Error demonstrating that SVR can effectively enhance traffic signal control. This approach pledges significant improvements in reduction in congestion and streamlined traffic flow. Our findings majorly highlight the capability of the SVR Machine Learning model in improving urban traffic management systems and call for further enhancement and collaboration with experts to address dynamic traffic issues. © 2024 IEEE.

关键词： Mean square error

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Secure blockchain bidding auction protocol against malicious adversaries

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High-Confidence Computing 2024年第3期4卷 58-63页

作者： Xiaobing Dai Jiajing Liu Xin Liu Xiaofen Tu Ruexue Wang Department of Computer and Information Engineering Baotou Vocational&Technical CollegeBaotou 014010China Department of Computer Tianjin Ren’ai CollegeTianjin 301636China School of Information Engineering Inner Mongolia University of Science and TechnologyBaotou 014010China

In recent years,with the development of blockchain,electronic bidding auction has received more and more *** at the possible problems of privacy leakage in the current electronic bidding and auction,this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries,which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third *** protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve *** can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants,which can solve the problem of resisting malicious adversary *** security of the protocol is proved by the real/ideal model paradigm,and the efficiency of the protocol is *** efficiency of the protocol is verified by simulating experiments,and the protocol has practical value.

关键词： Blockchain Secure multi-party computation Electronic bidding auction Malicious model Real/ideal model paradigm

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Improved Bayes Regret Bounds for Multi-Task Hierarchical Bayesian Bandit Algorithms 38

Improved Bayes Regret Bounds for Multi-Task Hierarchical Bay...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guan, Jiechao Xiong, Hui AI Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering HKUST China

Hierarchical Bayesian bandit refers to the multi-task bandit problem in which bandit tasks are assumed to be drawn from the same distribution. In this work, we provide improved Bayes regret bounds for hierarchical Bayesian bandit algorithms in the multi-task linear bandit and semi-bandit settings. For the multi-task linear bandit, we first analyze the preexisting hierarchical Thompson sampling (HierTS) algorithm, and improve its gap-independent Bayes regret bound from O(mpn log n log (mn)) to O(m√n log n) in the case of infinite action set, with m being the number of tasks and n the number of iterations per task. In the case of finite action set, we propose a novel hierarchical Bayesian bandit algorithm, named hierarchical BayesUCB (HierBayesUCB), that achieves the logarithmic but gap-dependent regret bound O(m log (mn) log n) under mild assumptions. All of the above regret bounds hold in many variants of hierarchical Bayesian linear bandit problem, including when the tasks are solved sequentially or concurrently. Furthermore, we extend the aforementioned HierTS and HierBayesUCB algorithms to the multi-task combinatorial semi-bandit setting. Concretely, our combinatorial HierTS algorithm attains comparable Bayes regret bound O(m√n log n) with respect to the latest one. Moreover, our combinatorial HierBayesUCB yields a sharper Bayes regret bound O(m log (mn) log n). Experiments are conducted to validate the soundness of our theoretical results for multi-task bandit algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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Advanced Detection of Cyber-Physical Attacks in Manufacturing Using LSTM-KNN 2

Advanced Detection of Cyber-Physical Attacks in Manufacturin...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Penchalaiah, N. Kumar, Arvind Chaithrashree, A. Saivaraju, Aravindh Bhoopathy, V. Lamba, Anil Kumar Annamacharya University Department of Ai&ml Boyanapalli Andhra Pradesh Rajampet India Sca Mriirs Haryana Faridabad India Brindavan College of Engineering Department of Computer Science and Engineering Bangalore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India Sree Rama Engineering College Department of Computer Science and Engineering Tirupathi India Geeta University Department of Computer Science and Engineering Delhi NCR Panipat India

ISBN: (纸本)9798350373110

Cyber Manufacturing System (CMS) is an assembly system that could be used in the future. There will be an increase in cyberattacks like the Stuxnet strike as the number of connected devices continues to grow. Current cyber security measures are inadequate to protect CMSs from new cyber-physical threats. This new security flaw can be better understood with the help of CMS's taxonomy for cyber-physical hazards, which is part of the agency's mandate. Preprocessing, feature selection, and training the model are the three steps that make up this proposed method. The data collected from the sensors show the greatest distance between two nearby locations after any outliers have been removed by preprocessing. Feature selection makes advantage of dimensional reduction derived from geometric feature distribution. When training the model, an LSTM-KNN was used. The average accuracy of the suggested method is 93.46%, which is higher than that of LSTM and KNN. © 2024 IEEE.

关键词： Cyber attacks

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IAOMDR: Improved Aquila Optimized Hybrid deep learning Model for Classification of Diabetic Retinopathy 2

IAOMDR: Improved Aquila Optimized Hybrid deep learning Model...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Saju, Binju Barwal, Sahil Asha, V. Kumar, Shubham Tressa, Neethu Nikhil, T. Adi Shankara Institute of Engineering & Technology Department of Computer Science-AI Engineering Kalady India New Horizon College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350328202

One of the scariest illnesses that causes irreversible blindness is Diabetic Retinopathy (DR). As a result, early exposure to Diabetic Retinopathy can help to preserve vision. The study proposes a hybrid model to classify Diabetic Retinopathy images. Initially, pre-processing of the photographs includes resizing them. Grey scale translation and noise removing using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is as opposed to other studies in literature. The proposed model has acquired F1 score of 98.98%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 99.1%, and precision of 99%. © 2024 IEEE.

关键词： Median filters

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Analysis of Magnetic Resonance images for the detection of Alzheimer's 4

Analysis of Magnetic Resonance images for the detection of A...

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

作者： Patil, Harshal Swati Kamala, L. Chaturvedi, Arpana Veeranjaneyulu, Rayavarapu Vekariya, Vipul Maharashtra Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Gitam School of Technology Department of Computer Science and Engineering Karnataka Bengaluru561203 India New Delhi Institute of Management Department of IT/AI/ML New Delhi110062 India Pace Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole523272 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat 391760 India

ISBN: (纸本)9798350354218

This study aims to design a CAD tool for Alzheimer's disease (AD) detection using linear SVM classifiers on MRI scans. Existing approaches often face challenges with high-dimensional feature spaces and limited data accessibility from repositories like ADNI. To overcome these, three SVM-based classifier proposals were explored. Results demonstrated promising accuracy, sensitivity (Se), and specificity (Sp), particularly with cross-validation techniques. Notably, classifiers trained with image histograms showed superior performance in differentiating between AD, cognitively normal (CN), and mild cognitive impairment (MCI). Despite successes, initial approaches encountered difficulties, necessitating alternative methods. Challenges in data access and processing highlight the need for improved data management tools. Moving forward, refinements such as feature selection and testing with larger datasets could enhance classifier performance. Exploring deep neural networks as an extension may offer further insights into AD detection methodologies for early diagnosis and intervention. © 2024 IEEE.

关键词： Deep neural networks

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ABSPYRAMID: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph

ABSPYRAMID: Benchmarking the Abstraction Ability of Language...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Wang, Zhaowei Shi, Haochen Wang, Weiqi Fang, Tianqing Zhang, Hongming Choi, Sehyun Liu, Xin Song, Yangqiu Department of Computer Science and Engineering HKUST Hong Kong Tencent AI Lab Bellevue United States

ISBN: (纸本)9798891761193

Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models. In this paper, we present ABSPYRAMID, a unified entailment graph of 221K textual descriptions of abstraction knowledge. While existing resources only touch nouns or verbs within simplified events or specific domains, ABSPYRAMID collects abstract knowledge for three components of diverse events to comprehensively evaluate the abstraction ability of language models in the open domain. Experimental results demonstrate that current LLMs face challenges comprehending abstraction knowledge in zero-shot and few-shot settings. By training on our rich abstraction knowledge, we find LLMs can acquire basic abstraction abilities and generalize to unseen events. In the meantime, we empirically show that our benchmark is comprehensive to enhance LLMs across two previous abstraction tasks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Disease Detection in Finger Millet Plant Using Fine-Tuned Transfer Learning Models

Disease Detection in Finger Millet Plant Using Fine-Tuned Tr...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Prajwal B Subhash Mondal Raghu M Shashank N Computer Science & Engineering (AI & ML) Dayananda Sagar University Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Plant diseases are becoming one of the major threats to global food security, particularly in carbohydrate rich staple crops like finger millet (Eleusine coracana) which requires minimal water and can be grown in diverse climate conditions, highlighting the need for early disease detection to minimize yield losses and promote sustainable agriculture. We employed web scraping for data collection from different open source, data augmentation techniques like flip, rotation, zoom to enhance the model's performance and enhance generalization capabilities. This research focuses on developing an effective disease detection system for finger millet using Convolutional Neural Networks (CNNs) and transfer learning methodologies. We analyzed with pretrained CNN architectures, such as DenseNet121, VGG16, and VGG19, using a dataset of more than thousands of labelled images collected from diverse agricultural settings. Fine tuning these models ensures optimising model's performance and achieving better accuracy. Among the tested models, DenseNet121 achieved the highest accuracy, reaching 96.88%. Additionally, we developed a user-friendly real-time application to disease detection using Streamlit library helping the farmers to detect the disease in mobile for remote access. This study demonstrates the significant use of deep learning techniques like CNN architectures, particularly transfer learning, for developing better crop disease detection systems. By detecting the disease early and prevention at earlier stage will save tonnes of crops. This solves the major issues like food security and also malnutrition.

关键词： Plant diseases Accuracy Transfer learning Fingers Food security Crops computer architecture Convolutional neural networks Sustainable development Tuning

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