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检索条件"任意字段=18th International Conference on Software Engineering and Knowledge Engineering"

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Grey Wolf Optimizer and Deep Neural Network based Feature Selection and Classification in Medical Image Analysis 18

Grey Wolf Optimizer and Deep Neural Network based Feature Se...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chandrashekhar, Katha Leela, P. Madhavi, K. Reddy Avanija, J. Tangudu, Naresh Indur Institute of Engineering and Technology Computer Science and Engineering Siddipet India Mohan Babu University School of Computing Dept. of Data Science Tirupati India Mohan Babu University School of Computing Tirupati India Aditya Institute of Technology and Management Tekkali India

ISBN: (纸本)9789380544519

Accurate and efficient medical image analysis is required for modern healthcare applications, facilitating timely disease diagnosis and treatment. Considering the importance of optimization algorithms and deep learning methodologies, this work presents a novel approach, Grey Wolf Optimizer-Assisted Deep Neural Networks (GWO-DNN), for feature selection and classification in medical image analysis. the proposed framework utilizes the power of the Grey Wolf Optimizer to intelligently select the most relevant features from complex medical images. the selected features are then integrated into Deep Neural Networks, to create accurate models for medical image classification tasks. this integrated approach is designed to improve diagnostic accuracy and enhance patient care. the experimentation and evaluation, reveal that the proposed GWO-DNN based model for image analysis is more efficient than the existing methods. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Neural Networks (DNN) Disease Classification Feature Selection Grey Wolf Optimization (GWO) Medical Image Analysis

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Natural Language Processing (NLP) based Extraction of Tacit knowledge from Written Communication during software Development 4

Natural Language Processing (NLP) based Extraction of Tacit ...

引用

4th international conference on Advancements in Computational Sciences, ICACS 2023

作者： Noor, Maham Rana, Zeeshan Ali University of Central Punjab Faculty of Information and Technology Lahore Pakistan FAST School of Computing FAST-NUCES Lahore Pakistan

ISBN: (纸本)9781665463775

software engineering, in general, and Global software engineering (GSE), in particular, face challenges such as handling communication and collaboration issues and inappropriate knowledge management. It is difficult to ensure availability of right knowledge at the right time to the right person during software engineering activity. the knowledge needs to be shared across the organization but limitations of knowledge sharing tools or dispersed knowledge sharing media and improper handling of tacit knowledge makes it more challenging to share knowledge. Significant amount of communication during software engineering process takes place via emails and discussion forums. Decisions taken during planning activities are also communicated through emails. these emails and the discussions do not necessarily cover the rationale behind a decision or approach adopted. this leaves a substantial portion of shared knowledge as tacit. Mostly, due to undefined tacit knowledge handling procedures, organizations suffer with the loss of critical knowledge and information. One way to handle this loss of knowledge is to store every information in knowledge repository but this can increase the size of the knowledge repository. this paper presents a three step approach to identify tacit knowledge in the written communication such that the organizations can save the tacit knowledge for future use. the presented approach does not only extract up to 57% of tacit knowledge but also indicates that only 20% of the whole communication need to be externalized hence saving 80% capacity of the knowledge repository, if every communication was to be preserved as potential tacit knowledge. © 2023 IEEE.

关键词： knowledge management

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Wideband Microstrip Antenna Design Using LoRa and GPS for Collision Warning Application 18

Wideband Microstrip Antenna Design Using LoRa and GPS for Co...

引用

18th international conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Izzuddin, Ahmad Gaffar, Muhammad Andhika Almuqaddami School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798331527686

this paper provided a design for a microstrip antenna with a wide bandwidth covering the LoRa and GPS working frequencies. the antenna is designed for vehicle-to-vehicle communication needs, enabling data sharing of location information as a form of collision warning. the designed microstrip antenna operated at frequencies of 915 MHz and 1575 MHz by trimming most of the ground plane to achieve bandwidth widening, followed by fine-tuning to achieve optimal results. the final design of the microstrip antenna, measuring 60 mm x 75.1 mm, exhibited a return loss of around -20 dB both at 915 MHz and 1575 MHz. the obtained VSWR values are around 1.22 both at 915 MHz and 1575 MHz. the proposed and fabricated antena has wideband characteristic and has operational frequency range from 822 MHz to 1820 MHz. © 2024 IEEE.

关键词： Bandwidth

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Enhancing Change Impact Prediction by Integrating Evolutionary Coupling with software Change Relationships 24

Enhancing Change Impact Prediction by Integrating Evolutiona...

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18th ACM/IEEE international Symposium on Empirical software engineering and Measurement, ESEM 2024

作者： Zhou, Daihong Zhang, Jiyue Yu, Ping Guo, Wunan School of Computer Science and Information Engineering Shanghai Institute of Technology China Huawei Technologies Co. Ltd China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology China

ISBN: (纸本)9798400710476

Background: Changes on source code may propagate to distant code entities through various relationships, making related changes obligatory. Identifying change impacts is challenging due to the complexity of how changes spread. Although association rules are widely used for change impact prediction, they rely solely on historical co-changes, which limits their accuracy when entities rarely or never co-change. Aims: this study explores the integration of evolutionary coupling with software change relationships among changed code entities to enhance the state-of-the-art association rule mining technique, TARMAQ. Method: We integrate evolutionary coupling with 12 types of software change relationships, such as structural dependencies and code clones, to better capture associated changes. Results: Analyzing thousands of commits from six open-source systems, we observed: (1) Incorporating software change relationship analysis significantly improves TARMAQ's prediction recall and mean average precision (MAP), (2) the top-5 predictions exhibit notable increasing in precision, recall, F1-score, and MAP, and (3) Based on our implementation, the integrated method is practically applicable. Conclusions: Combining evolutionary coupling and software change relationships can improve the recall and prioritization of impact predictions in association rule-based techniques. © 2024 ACM.

关键词： Prediction models

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A Hybrid Deep Learning Approach for Accurate and Efficient Classification for Vegetable Recognition 18

A Hybrid Deep Learning Approach for Accurate and Efficient C...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Annepu, Visalakshi Bagadi, Kalapraveen Chirra, Venkata Rami Reddy Prashanth, Yalamanchili Nikhil, Akula Chaitanya, Anguluri Krishna VIT-AP University School of Computer Science and Engineering Amaravati India VIT-AP University School of Electronics Engineering Amaravati India

ISBN: (纸本)9789380544519

In this research work, we address the formidable challenge of automating the recognition and classification of vegetables by applying cutting-edge deep learning techniques. the manual identification of vegetables is a time-intensive process with the possibility of errors. To mitigate these challenges, we propose a hybrid deep learning approach that combines the strengths of VGG-16, VGG-19, and Inception V3 models. By leveraging the complementary features of these architectures, our hybrid model achieves enhanced accuracy in vegetable classification. Integrating multiple architectures strengthens the model's ability to discern intricate patterns and features, thereby improving its adaptability to a diverse range of vegetables, irrespective of colour, texture, and size variations. Moreover, the hybrid deep learning model exhibits remarkable efficiency, facilitating rapid processing and reducing the time-consuming nature of manual identification. Our research presents a notable advancement in vegetable recognition, offering a reliable and efficient solution for automated vegetable classification systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Learning Techniques Inception V3 Vegetable Recognition VGG-16 VGG-19

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L∗-Based Learning of Probabilistic Timed Automata with One Clock 35

L∗-Based Learning of Probabilistic Timed Automata with One ...

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35th international conference on software engineering and knowledge engineering, SEKE 2023

作者： Wu, Rong School of Software Engineering Tongji University Shanghai China

Probabilistic timed automata (PTAs) allow for analyzing systems which operate under the existence of both probability and time. Probabilistic model checking for PTAs requires formal models as a basis. In this paper, we consider a learning algorithm for PTAs with one clock (OPTAs) which extends Angluin’s L∗ algorithm. For the black-box systems, we estimate the probabilities by sampling system traces, and apply the framework of Probably Approximately Correct (PAC) learning to provide correctness guarantees respect to error and confidence parameters. Experiments with the implementation of our learning algorithm include the sender of CSMA/CD protocol and randomly generated examples. Evaluating results show the effectiveness of our learning algorithm. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Learning algorithms

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Performance Comparison of MANETs under Congestion and Packet Loss using Distance based Geographic Routing Protocols 18

Performance Comparison of MANETs under Congestion and Packet...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Ganesan, T. Murali, N. Manojkumar, K. Priya, D. Mohana Alagarsamy, Manjunathan Manikandan, S. E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam India Government College of Engineering Department of Computer Science and Engineering Tamil Nadu Thanjavur India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India

ISBN: (纸本)9789380544519

the performance of Mobile Ad-Hoc Networks (MANETs) can be verified based on number of input packets and life time of protocol requirements. Distance based geographic routing protocol are used to provide secure routing under congestion control and identifies packet loss. this paper presents performance evaluation and comparison of MANETs by using congestion on limited buffer requirements and malicious packet loss route discovery. We proposed distance based geographic routing protocol to monitor the routing requests, polling response and reduce the routing delays. In this paper analyze path control, available resources, and conditional delays and simulate the networks. this paper uses to reveal both buffer size and network performance characteristics. Analyze the exact frame work of throughput capacity, packet loss ratio and end-to-end delay in MANETs. In this mechanism reduce the overhead of routing strategies and calculate estimated distance over propagation of each node. Simulation experiments are verified by various expected values based on changes in number of hops and routing paths. In the protocol, every node evaluates the link quality and eliminates weak node or malicious node in MANET. Based on ready queue random early detection methods are used to reduce the network traffic delays and propagation delays. So this paper clearly indicates the MANETs performance under congestion control, packet loss and gives clear details about the life time of the networks and packet delay. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Congestion Control Distance Based Geographic Routing Protocol MANET Packet Loss Ready queue based Random early detection

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Machine Learning based Phishing Website Detection 18

Machine Learning based Phishing Website Detection

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kawale, Manav Maru, Bhavik Dagu, Sakshi Varghese, Maya Gupta, Vikas Vidyavardhini's College of Engineering & Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9789380544519

Phishing websites are those where a person can imitate a legitimate website and trick an innocent user into thinking he is visiting a legitimate website while stealing sensitive information. Statistics from the Anti-Phishing Website Group (APWG) show that in the second quarter of 2023, 1,286,208 phishing attacks were recorded. this was the APWG's third-highest quarterly total ever. To address this issue, a Phishing website detection system was made that automatically detects and notifies the user of any phishing website. the system will be based on supervised learning wherein machine learning models such as k-nearest Neighbors, Logistic Regression, Support Vector Classifier, Naive Bayes, Decision Tree, and Random Forest are selected for training. Random Forest was found to have the highest accuracy among them. As a result, Randomforest was chosen with an accuracy of 96.7 and a combination of 32 features, among which features like status bar customizing, website forwarding, stats report, pop windows, server form handler were uniquely identified and implemented to the proposed system to improve the overall accuracy of the model. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning technique Phishing phishing feature extraction Random Forest

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Time-Series based phase-ordering selection for code-size reduction 36

Time-Series based phase-ordering selection for code-size red...

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36th international conference on software engineering and knowledge engineering, SEKE 2024

作者： Peng, ZhenBang Zhang, Yi Xing, Mingjie Wu, Yanjun Zhao, Chen Institute of Software Chinese Academy of Sciences HIAS UCAS HangZhou China College of Intelligent Science and Technology HIAS UCAS HangZhou China Nanjing Institute of Software Technology NanJing China BeiJing China

ISBN: (纸本)1891706594

Recent advancements in networking, embedded systems, mobile computing, and artificial intelligence (AI) have brought renewed focus on code-size reduction. And within this domain, the sequence of optimization passes, known as phase-ordering, considered as a critical determinant. While traditional methods often fall short in addressing complex optimization landscapes, reinforcement learning (RL) algorithms have evolved as a formidable tools to tackle these challenges. According to existing research, previously applied optimization passes can significantly influence subsequent decisions—highlighting a gap in traditional RL algorithms’ ability to account for temporal dynamics inherent to the phase-ordering problem. In response to this, our paper proposes a strategy employing Long Short-Term Memory (LSTM) in conjunction with the Proximal Policy Optimization (PPO) RL algorithm, leveraging the temporal relationships between optimization passes to discern an enhanced phase-ordering scheme. the experimental results shows that our model reaches a 3000 iterations fewer in convergence and an additional 5.758% reduction in code size in entire benchmark than PPO alone. Furthermore, our approach also demonstrates robust generalization capabilities and superior performance across various benchmarks and RL algorithms. © 2024 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Reinforcement learning

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Analysis on Performance of Semi-Real-Time Transcription on Health App 18

Analysis on Performance of Semi-Real-Time Transcription on H...

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18th international conference on Telecommunication Systems, Services, and Applications, TSSA 2024

作者： Indrasary, Yoenie Nugraha, Daniel Prihatmanto, Ary Setijadi Sarief, Ivany Gunawan, Rudy Yunautama, Dhema School of Electrical Engineering and Informatics Institut Teknologi Bandung Doctoral Program of Electrical Engineering and Informatics Bandung Indonesia State Polytechnic of Banjarmasin Banjarmasin Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Universitas Sangga Buana Faculty of Engineering Dept. of Electrical Engineering Bandung Indonesia Universitas Sangga Buana Directorate of Vocational Dept. of Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798331527686

the fragmentation of health information systems has hindered seamless data exchange in healthcare services, posing challenges in achieving efficient, real-time access to patient health data. this study addresses these issues by evaluating the implementation of a semi-real-time transcription system using a distributed architecture with a message broker to enhance the documentation of clinical examination processes in primary health services. the research focuses on assessing the feasibility of this approach by measuring key performance indicators such as latency and word error rate (WER) during clinical conversation transcription. Audio recordings were segmented into shorter fragments to simulate real-time transcription, and data transmission was conducted through the Advanced Message Queuing Protocol (AMQP). the findings demonstrate that the average latencys achieved by semi-real-time transcription system is 7.7s, indicating its potential suitability for clinical documentation applications that do not require strict real-time processing. However, glitches observed in the message broker's forwarding mechanism suggest the need for further optimization to ensure consistent performance. Future work aims to develop strategies to minimize transcription errors, enhance reliability, and improve the overall integration of this system in healthcare settings. this study contributes to the advancement of health information systems by offering architecture into scalable and effective methods for semi-real-time data transcription and interoperability. © 2024 IEEE.

关键词： Audio recordings

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