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2024 International Conference on Smart Devices, ICSD 2024

作者： Gangadharan, S.M.P. Mohammad, Faz Sunil Reddy, G. Kumar, Kaushal Singh, Jagendra Haridass, R. Liverpool John Moores University Liverpool United Kingdom KCC Institute of Legal and Higher Education Department of Information Technology Greater Noida India SR University School of Computer Science & Artificial Intelligence Warangal India Raj Kumar Goel Institute of Technology Department of Computer Science Engineering Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Karpagam College of Engineering Department of Mechanical Engineering Coimbatore India

ISBN: (纸本)9798350394412

The present research looks at how deep learning models may improve the accuracy of COVID-19 diagnosis using CT scan pictures. The project aims to improve the detection of COVID-19-specific patterns and features by using the capabilities of the VGG19, DenseNet201, and VGG16 models. The training and assessment were based on a collection of 2,521 CT scan images from patients with cardiac problems as well as those who tested positive for COVID-19. To assess the models' diagnostic abilities, performance metrics such as accuracy, precision, recall, and specificity are used. DenseNet201 regularly beats the competition across all key measures, revealing its potential for accurate COVID-19 diagnosis. The suggested edge-cloud architecture further integrates the models into real clinical scenarios. Given the important role of false negatives and false positives in medical diagnostics, the results highlight the need of looking at more than just accuracy. These models are useful diagnostic tools, but they should not be used in place of hands-on medical treatment. This study adds to the increasing body of knowledge at the interface of artificial intelligence and healthcare by providing insights into how deep learning models may be used to improve COVID-19 diagnostic accuracy and perhaps modify medical practices. To fully realize the promise of these findings in enhancing patient care and healthcare systems, more cooperation between machine learning researchers and healthcare practitioners is required. © 2024 IEEE.

关键词： COVID-19

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Enhancing Deep Reinforcement Learning with Scenario-Based Modeling

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SN computer science 2023年第2期4卷 1-13页

作者： Yerushalmi, Raz Amir, Guy Elyasaf, Achiya Harel, David Katz, Guy Marron, Assaf Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot 76100 Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem 91904 Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva 8410501 Israel

Deep reinforcement learning agents have achieved unprecedented results when learning to generalize from unstructured data. However, the “black-box” nature of the trained DRL agents makes it difficult to ensure that they adhere to various requirements posed by engineers. In this work, we put forth a novel technique for enhancing the reinforcement learning training loop, and specifically—its reward function, in a way that allows engineers to directly inject their expert knowledge into the training process. This allows us to make the trained agent adhere to multiple constraints of interest. Moreover, using scenario-based modeling techniques, our method allows users to formulate the defined constraints using advanced, well-established, behavioral modeling methods. This combination of such modeling methods together with ML learning tools produces agents that are both high performing and more likely to adhere to prescribed constraints. Furthermore, the resulting agents are more transparent and hence more maintainable. We demonstrate our technique by evaluating it on a case study from the domain of internet congestion control, and present promising results. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep reinforcement learning Domain expertise Machine learning Rule-based specifications Scenario-based modeling

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Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19

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Materials Today: Proceedings 2023年 80卷 3709-3713页

作者： Modi, Shraddha Guhathakurta, Rajib Praveen, Sheeba Tyagi, Sachin Bansod, Saket Narendra Department of Computer Science & Engineering Government Engineering College Patan Katpur India Sri Venkateswara College of Engineering and Technology Department of IT Andhra Pradesh Chittoor India Department of Computer Science & Engineering Integral University Lucknow226026 India Bharat Institute of Technology School of Pharmacy Meerut India Dr. Ambedkar Institute of Management Studies and Research Deekshabhoomi Nagpur India

COVID-19 is one of the biggest pandemics that the world is facing today, and every day, we are coming up with new challenges in this area. Still, much research is already going on to overcome this pandemic, and we also get succeeded to some extent. Diverse sources such as MRI, CT scanning, blood samples, X-ray image, and many more are available to detect COVID-19. Thus, it can be easily said that through image processing, the classification of COVID-19 can be done. In this study, the COVID-19 detection is done by classifying with the use of a type of convolutional neural network termed a detail-oriented capsule network. Chest CT scan imaging for the prediction of COVID-19 and non-COVID-19 are classified in the present paper using a Detailed Oriented capsule network (DOCN). Accuracy, specificity, and sensitivity are parameters used for model evaluation. The proposed model has achieved 98% accuracy, 81% sensitivity, and 98.4% specificity. © 2021

关键词： Coronavirus

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Deep Learning-Based Approach for Calorie Estimation in Indian Foods 14

Deep Learning-Based Approach for Calorie Estimation in India...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Chauhan, Naman Kumar, Aman Teja, Gudi Vishnu Bharati Mohan, G. Prasanna Kumar, R. Kakarla, Yamani Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Kinaxis HalifaxNS Canada

ISBN: (纸本)9798350344837

People in the modern era are increasingly concerned with their diet and food choices to prevent developing chronic diseases like high blood pressure and diabetes. With the increasing usage of smartphones and technology, an application to automatically monitor their diet would be a great boon, especially for the younger generation. This research work proposes automatic food calorie estimation from the food images using deep learning technology. The proposed work has three main phases. In the first phase, the food image is categorised as one of the types, for example, boiled, fried in oil or junk. A novel deep neural network namely MobileNetV2 which is a 15-layer model architecture is used for this purpose. In the second phase, the quantity of the labelled food is estimated. The third and final stage involves computing the calories present in the food using the Google search API. The details of ingredients present in each type of food are used for this purpose. Classification accuracy, food amount calculation precision, and calorie estimation accuracy are used assessing the effectiveness of the proposed approach. The proposed model is producing results with an overall accuracy of 95.21 percent which is better than the existing architecture. © 2024 IEEE.

关键词： Image segmentation

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Hierarchical Attention Decoder for Solving Math Word Problems 4th

Hierarchical Attention Decoder for Solving Math Word Proble...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Li, Jiajia Peng, Letian Wang, Ping Zhao, Hai School of Information Management Wuhan University Wuhan China Center for the Studies of Information Resources Wuhan University Wuhan China Department of Computer Science and Engineering University of California San Diego United States Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819787012

To answer math word problems (MWPs), models must formalize equations from the source text of math problems. Recently, the tree-structured decoder has significantly improved model performance on this task by generating the target equation in a tree format. However, current decoders usually ignore the hierarchical relationships between tree nodes and their parents, which hinders further improvement. Thus, we propose a structure called a hierarchical attention tree to aid the generation procedure of the decoder. As our decoder follows a graph-based encoder, our full model is therefore named Graph to Hierarchical Attention Tree (G2HAT). We show that a tree-structured decoder with hierarchical accumulative multi-head attention leads to a significant performance improvement and reaches a significant improvement on various strong baselines on both English MAWPS and Chinese Math23k MWP benchmarks. For further study, we also apply a pre-trained language model to G2HAT, which even results in a new higher performance. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

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Enhancing Blockchain Security Using Ripple Consensus Algorithm

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computers, Materials & Continua 2022年第12期73卷 4713-4726页

作者： A.Baseera Abeer Abdullah Alsadhan School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science Imam Abdulrahman Bin Faisal UniversityDammam34212Saudi Arabia

In the development of technology in various fields like big data analysis,data mining,big data,cloud computing,and blockchain technology,security become more *** is used in providing security by encrypting the sharing of *** is applied in the peerto-peer(P2P)network and it has a decentralized *** security against unauthorized breaches in the distributed network is *** detect unauthorized breaches,there are numerous techniques were developed and those techniques are inefficient and have poor data ***,a novel technique needs to be implemented to tackle the new breaches in the distributed *** paper,proposed a hybrid technique of two fish with a ripple consensus algorithm(TF-RC).To improve the detection time and security,this paper uses efficient transmission of data in the distributed *** experimental analysis of TF-RC by using the metric measures of performance in terms of latency,throughput,energy efficiency and it produced better performance.

关键词： Blockchain security ripple breaches twofish network

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Multiobjective adaptive car-following control of an intelligent vehicle based on receding horizon optimization

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science China(Information sciences) 2023年第4期66卷 301-302页

作者： Hongbo GAO Juping ZHU Fei ZHANG Ruidong YAN Junjie ZHOU Lei JIANG Jianqiang WANG Keqiang LI Department of Automation University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China Department of Computational Mathematics School of Mathematical Sciences Anhui University School of Traffic and Transportation Beijing Jiaotong University Autonomous Driving Business Unit Chery Automobile School of College of Computer Science and Technology Zhejiang University China North Vehicle Research Institute School of Automotive Engineering Tsinghua University

Dear editor, Vehicle control is one of the key steps of intelligent driving [1]. Algorithms based on receding horizon optimization(RHO) can predict future trajectories and handle multiobjective constraint conditions; therefore, RHO-based methods have attracted considerable attention in the field of vehicle control. Although existing methods use RHO to design controllers, they do not simultaneously meet the multiobjective optimal control performance requirements on tracking, fuel economy, and ride comfort.

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A Novel Approach for Effective Detection and Prediction of Sophisticated Cyber Attacks Using the Stacked Attention GRU and BiLSTM 1

A Novel Approach for Effective Detection and Prediction of S...

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

作者： Shammi, L. Emilin Shyni, C. Presidency University Department of Computer Science and Engineering Karnataka Bengaluru India School of CS and IS Presidency University Karnataka Bengaluru India

ISBN: (纸本)9798350371802

The dynamic and sophisticated cyber threats of today's quickly expanding cybersecurity landscape are surpassing the effectiveness of traditional security solutions. Organizations must take a proactive stance in danger identification and mitigation if they are to successfully tackle these new difficulties. Threat intelligence (TI), or the real-time sharing of cyber threat data, is crucial for both proactive defense and quick reaction to cyberattacks. This study describes a comprehensive approach that includes feature selection, data preparation, and model training for threat intelligence-based cyber security. Normalization and standardization are two preprocessing methods that maximize data representation for efficient analysis. Principal component analysis is one of the feature selection techniques used to find the most pertinent variables for model training. A Stacked BiLSTM-A-GRU model is trained with careful respect to feature selection, guaranteeing optimal performance. A comparative examination shows that the suggested method outperforms the most advanced algorithms, with a remarkable accuracy rate of 98.71% This study emphasizes how crucial it is to use threat intelligence to have a strong cybersecurity defense against ever-changing threats. © 2024 IEEE.

关键词： Cybersecurity

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Imbalanced Classification in Diabetics Using Ensembled Machine Learning

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computers, Materials & Continua 2022年第9期72卷 4397-4409页

作者： M.Sandeep Kumar Mohammad Zubair Khan Sukumar Rajendran Ayman Noor A.Stephen Dass J.Prabhu School of Information Technology and Engineering Vellore Institute of TechnologyVelloreTamil Nadu632014India Department of Computer Science and Information Taibah UniversityMedinaSaudi Arabia College of Computer Science and Engineering Taibah UniversityMedinaSaudi Arabia

Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood *** risk of diabetics can be lowered if the diabetic is found at the early *** recent days,several machine learning models were developed to predict the diabetic presence at an early *** this paper,we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of *** proposed method uses both the concept of over-sampling and under-sampling along with model weighting to increase the performance of *** measures such as Accuracy,Precision,Recall,and F1-Score are used to evaluate the *** results we obtained using K-Nearest Neighbor(kNN),Naïve Bayes(NB),Support Vector Machines(SVM),Random Forest(RF),Logistic Regression(LR),and Decision Trees(DT)were 89.32%,91.44%,95.78%,89.3%,81.76%,and 80.38%*** SVM model is more efficient than other models which are 21.38%more than exiting machine learning-based works.

关键词： Diabetics classification imbalanced data split-vote instance duplication

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Web Data Mining with Organized Contents Using Naive Bayes Algorithm 2

Web Data Mining with Organized Contents Using Naive Bayes Al...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Ravinder, Bathini Seeni, Senthil Kumar Prabhu, V.S. Asha, P. Maniraj, S.P. Srinivasan, C. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Cognizant Technology Solutions Buffalo Grove Department of Mobile Application Development Illinois United States R.M.D. Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Data mining on the web has developed into a simple and crucial tool for finding relevant information. When it comes to file transfers, the World Wide Web is the user's first choice. Finding useful information and trends in the ever-expanding amount of data available online is becoming ever more challenging and time-consuming. When dealing with massive amounts of textual data often seen online, there are a number of benefits to using the Naive Bayes method for web data mining. Web data mining using the Naive Bayes algorithm seeks to mine massive amounts of textual material on the web for useful patterns, insights, and knowledge. Text classification and categorization tasks are the most common uses of the Naive Bayes method in online data mining. Expert and user-requested data may be difficult to mine from the sea of unorganized and contradictory material that is the World Wide Web. Relevant data (hyperlinks, contents, web use records) is extracted from the web using a variety of mining methods. Internet-centric data mining is a subfield of data science. Structure mining, content mining, and use mining are the three main categories of online data mining. Each of these categories employs a unique set of methods, instruments, strategies, and Naïve Bayes algorithm to mine the web's vast data stores for useful insights. The results show the density and velocity of web mining using Naïve Bayes algorithm. © 2024 IEEE.

关键词： Data mining

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