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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Menaka, C. Sharma, Sunil Naval, Preeti Brar, Khushmeen Anand, Rohit Alabdeli, Haider Pant, Shivani Department Of Computer Science And Information Technology Bangalore India Vivekananda Global University Department Of Computer Science & Application Jaipur India Maharishi University Of Information Technology Maharishi School Of Engineering & Technology Uttar Pradesh India Chandigarh University Department Of Electronics And Communication Engineering Punjab Mohali India G.B Pant Okhla-1 Campus Department Of Ece New Delhi India The Islamic University Najaf Iraq Uttaranchal University Uttaranchal Institute Of Technology Uttarakhand India

ISBN: (纸本)9798350366846

The present research introduces a deep learning method for automatically recognizing and evaluating tissue areas that correspond to invasive ductal carcinoma (IDC) in whole slide images (WSI) of breast cancer (BCa). Learn-from-data techniques which utilize computational simulations of the learning process are called deep learning approaches. It has been demonstrated that these methodologies outperform conventional methods in resolving the challenging issues in a number of domains, particularly object and speech recognition. The main reason why making an invasive breast cancer evaluation is an exhausting and laborious process is that, it necessitates a pathologist to scan a huge number of benign regions with the aim to determine the spots that are malignant. Effectively characterizing IDC in WSI is a prerequisite for evaluating the severity of tumors and predicting patient prognosis. In order to support diagnosis, the DL approach in this study draws out many convolutional neural networks (CNNs) for perceptible semantic examination of tumor locations. A WSI database of 162 patients with IDC diagnoses is exploited to assess the technique, and 170 slides were chosen for training. The objective of the experimental analysis was to assess the accuracy of the classifier in identifying IDC tissue areas in WSI. A fusion of optimization techniques namely Best First and Evolutionary Search has been employed for feature reduction and improvement. When using DenseNet169, our approach produced the highest quantitative outcomes for the computerized identification of IDC sections in WSI with regard to accuracy 87.46% in comparison with an approach using EfficientNetB0 and ML classifiers for invasive tumor categorization employing Random Forest, BayesNet, SVM, Naïve Bayes and Decision Tree with accuracy of 80.65%. © 2024 IEEE.

关键词： Diagnosis

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AI-Driven Software Reuse: Optimization and Comparative Performance Analysis

AI-Driven Software Reuse: Optimization and Comparative Perfo...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Aditya Pai H Chandrappa S Guru Prasad M S Atul Thakare Sharon Christa Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India School of Computer Science and Engineering JAIN Deemed to be University Bengaluru India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Application Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Software reuse improves productivity and ensures that there is less time delivered to the market by enabling the proper usage of reusable code from the software repositories. The study provides the collaboration between the AI techniques, which includes Convolution Neural Networks (CNN), Random Forest (RF), and Support Vector Machines (SVM), to predict software components' reusability. The comparative analysis shows the effectiveness of different AI- driven algorithms, where CNN has shown the highest accuracy of 92%, thus reducing the human effort in finding the reusable modules. The Case studies in e-commerce and health care show the benefit of using AI-driven reuse of software; this means that the development time is reduced by 25%, and the maintenance cost is decreased by 20%. The paper highlights how different AI algorithm integration with software engineering has some limitations. The findings show that AI has greatly revolutionized software reuse. Thus bringing innovation and efficiency in software engineering.

关键词： Support vector machines Computational modeling Medical services Software Data models Convolutional neural networks Artificial intelligence Software reusability Random forests Software engineering

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Research on Ornamental Fish Recognition Base on Improved Shufflenet V2 Network 4

Research on Ornamental Fish Recognition Base on Improved Shu...

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4th International Conference on Big Data and Artificial Intelligence and Software engineering, ICBASE 2023

作者： Zhang, Xingyun Chen, Hongming Zhang, Longqing Chen, Qian Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China Guangdong University of Science and Technology School of Computer Science Dongguan523083 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

ISBN: (纸本)9798350329490

With the advancement of computer vision technology, fish recognition plays a crucial role in various fields such as aquaculture processing and disseminating fish knowledge. In this study, we propose an enhanced and lightweight fish recognition algorithm to accomplish fish classification recognition with limited data samples. Based on the ShuffleNet V2 network, we incorporate Transfer Learning and the SE attention mechanism. Additionally, we replace the ReLu activation function with the AM-Softmax activation function. Comparative evaluations are conducted against Resnet50, MobileNet V2, MobileNet V3, and the original ShuffleNet V2 network model. Our experimental findings demonstrate that the proposed algorithm outperforms the aforementioned models, resulting in recognition accuracy improvements of 16.01%, 19.05%, 15.04%, and 14.44% respectively. The final recognition accuracy achieved is 97.20%, successfully attaining the desired outcome. © 2023 IEEE.

关键词： computer vision

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An In-Depth Analysis Of Local Mechanic Support For Immediately Apparent Repairs And Scheduled Maintenance Services

An In-Depth Analysis Of Local Mechanic Support For Immediate...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Manik Rakhra Tiyas Sarkar Vikrant Sharma Prikshat Kumar Angra Ashwani Kumar Pritpal Singh School of Computer Science and Engineering Lovely Professional University Phagwara India School of Computer Science and Engineering Graphic Era Hill University Adjunct Professor Grahic Era Deemed to be University Dehradun India School of Computer Application Lovely Professional University Phagwara India Mittal School of Business Lovely Professional University Phagwara India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This As technology advances, innovative systems are emerging to address the need for rapid auto repairs, especially in cases of unexpected vehicle breakdowns. This analysis delves into a sophisticated system designed to provide immediate on- site assistance by connecting vehicle owners with skilled mechanics. The system, developed using cutting-edge technologies such as Figma, React JS, Tailwindcss, Nodejs, Express JS and MongoDB, offers a seamless experience by incorporating features like real-time location tracking and efficient job assignment. These features significantly reduce wait times and alleviate the stress associated with roadside assistance. The article highlights the development and architecture of the application, focusing on its user-friendly interface and the strong technological framework that ensures its reliability and quick response to contemporary automotive challenges.

关键词： Warranties Technological innovation Electric breakdown Processor scheduling Focusing Road safety Real-time systems Maintenance Stakeholders Stress

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AvoidPwd: A Mnemonic Password Generation Strategy Based on Keyboard Transformation

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China Communications 2022年第10期19卷 92-101页

作者： Shunying Lyu Qian Yao Jianhua Song School of Computer Science and Information Engineering Hubei University430062 WuhanChina Engineering and Technical Research Center of Hubei Province in Educational Informatization 430062 WuhanChina Engineering and Technical Research Center of Hubei Province in Software Engineering 430062 WuhanChina Engineering Research Center of Hubei Province in Intelligent Government Affairs and Application of Artificial Intelligence 430062 WuhanChina

Identity authentication is the first line of defense for network *** have been the most widely used authentication method in recent *** there are security risks in passwords,they will be the primary method in the future due to their simplicity and low *** the security and usability of passwords,we propose AvoidPwd,which is a novel mnemonic password generation strategy that is based on keyboard *** helps users customize a“route”to bypass an“obstacle”and choose the characters on the“route”as the final ***“obstacle”is a certain word using any language and the keys adjacent to the“obstacle”are typed with the“Shift”key.A two-part experiment was conducted to examine the memorability and security of the AvoidPwd strategy with other three password strategies and three leaked password *** results showed that the passwords generated by the AvoidPwd strategy were more secure than the other leaked password ***,AvoidPwd outperformed the KbCg,SpIns,and Alphapwd in balancing security and *** addition,there are more symbols in the character distribution of AvoidPwd than the other *** is hopeful to solve the security problem that people are difficult to remember symbols and they tend to input letters and digits when creating passwords.

关键词： password generation strategy password security mnemonic passwords

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Prediction of Fetal Brain Anomalies Using Deep Learning Models

Prediction of Fetal Brain Anomalies Using Deep Learning Mode...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Venkateshwari Subburaj Pandiselvam Pandiyarajan Department of Computer Application School of Computing Kalasalingam Academy of Research and Education Krishnankoil TamilNadu India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil TamilNadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Fetal brain anomaly prediction is important for fetal medicine, as well as for prenatal health care. Fetal anomalies are classified into two types, anomalies in the fetus' body parts including heart, lung, and kidney anomalies in facial features. Thus, to predict the anomalies in the fetal brain, various deep-learning models are used in this paper. The LeNet, AlexNet, ResNet, VGGNet, GoogLeNet and ZFNet were used. From the results obtained, the ResNet method achieved 99% accuracy with the given 3D ultrasound images of the fetal brain compared with other models and benchmark algorithms like SVM, Logistic regression, and KNN. The results of this paper are useful to identify fetal anomalies. Hence, this approach will be useful for improving outcomes for individuals and society as much as possible.

关键词： Deep learning Support vector machines Solid modeling Pediatrics Three-dimensional displays Accuracy Ultrasonic imaging Predictive models Brain modeling Prediction algorithms

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TB Chest X-Ray Diagnostic Technique Using Deep Learning 3

TB Chest X-Ray Diagnostic Technique Using Deep Learning

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Goyal, Manish Kumar Yadav, Dhananjay Kumar Brar, Khushmeen Kaur Mittal, Manisha Ojha, Ananta Alzubaidi, Laith H. Pathani, Ashish Vivekananda Global University Dept. of Computer Science & Application Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chandigarh University Dept. of Electronics and Communication Engineering Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Bangalore India The Islamic University Najaf Iraq Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehradun India

ISBN: (纸本)9798331518592

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained. © 2024 IEEE.

关键词： Deep learning

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Anomaly Detection Technology for Cloud Manufacturing System based on Data Denoising and Feature Optimization 19

Anomaly Detection Technology for Cloud Manufacturing System ...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Zhao, Longbo Li, Bo Hu Jia, Juan Wu, Tong School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Institute of Computer Technology and Application Department of Intelligent Manufacturing Beijing100854 China

ISBN: (纸本)9781665472432

Aiming at the problem that the traditional anomaly detection method based on threshold cannot effectively detect sensor numerical anomalies in cloud manufacturing system, this work proposes a new method to detect some sensor numerical anomalies form the industrial control system. It is the central part of a cloud manufacturing system. Firstly, this work constructs a Savitzky-Golay (S-G) filter to reduce data noises. Furthermore, an extreme learning machine based on genetic algorithm (GA-ELM) model is proposed to detect sensor numerical anomalies form the industrial control system. The genetic algorithm (GA) is used to reduce feature dimensions from 51 to 10 and the extreme learning machine algorithm (ELM) is used for classification to achieve the purpose of anomaly detection. Finally, using the public dataset called Secure Water Treatment (SWaT), the classification accuracy is 98.96%. It shows a better performance of the proposed method. © 2022 IEEE.

关键词： Genetic algorithms

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Improved Power Conversion Efficiency and Stability of Perovskite Solar Cells Induced by Dual Interface Passivation

Improved Power Conversion Efficiency and Stability of Perovs...

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2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compression Framework

MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compressio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jiang, ZhaoYi Han, Dong Song, Cao Nan, FangZhe Yang, Bailin School of Computer Science and Technolgy Zhejiang Gongshang University China School of Statistics and Mathematics Zhejiang Gongshang University China Economic Forecasting and Policy Simulation Laboratory Zhejiang Gongshang University China Collaborative Innovation Center of Statistical Data Engineering Technology & Application Zhejiang Gongshang University China

ISBN: (纸本)9798350368741

The increasing data volume and the demand for real-time transmission highlight the necessity for efficient compression of dynamic point cloud data. Existing methods primarily focus on reducing inter-frame redundancy by calculating per-point motion information, overlooking the computational and storage costs involved. In this paper, we propose a novel motion proxy-based dynamic point cloud compression framework to enhance the efficiency and accuracy of motion information utilization. Specifically, we introduce a feature proxy module to adaptively locate proxy points, which represent the overall motion through the motion of proxy points. Additionally, a motion enhancement module is employed to refine motion details and prevent local information loss caused by dense motion trajectories. Extensive experiments demonstrate the superiority of our approach. Compared with baseline methods, it achieves an average BD-rate improvement of 12.07% (D1) and 11.90% (D2). © 2025 IEEE.

关键词： compression deep learning neural network point cloud

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