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International Conference on Software engineering - Companion, ICSE, Companion

作者： Renyi Zhong Dept. of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798331536831

ISBN: (纸本)9798331536848

The quality of code in software development is critical to ensuring robust, functional, and maintainable applications. While static analysis techniques can improve code quality, they often overlook the natural language (NL) components of code, such as comments and logging statements, which play an essential role in program development and maintenance. In this thesis, we aim to address quality assurance for these NL elements through two primary objectives. First, we plan to propose a new benchmark to facilitate consistent comparisons among existing methods. Second, we tackle logging statement quality by identifying a broader range of defect types and introducing an automated detection and repair tool. This work addresses critical limitations in current research, which often focuses on single defect types and lacks repair functionality, by enabling a comprehensive and practical approach to NL quality in code. Our contributions provide a foundation for ongoing improvements in code maintainability and contribute to a more reliable software development lifecycle.

关键词： Codes Quality assurance Large language models Natural languages Static analysis Maintenance engineering Software reliability Maintenance Software development management Software engineering

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Child and Women Safety Measures using iBeacon Technology

Child and Women Safety Measures using iBeacon Technology

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Shanker Shalini Keerthana E Nishanthi P Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

A smart real-time tracking system featuring a compact wearable device is designed to enhance the safety of children and women by utilizing IoT-driven technology and a secure database for efficient movement monitoring. This system operates on four key pillars: outdoor localization, cross-platform communication, server integration, and user engagement. The tracking mechanism comprises a wearable device embedded with an iBeacon and a built-in MEMS accelerometer, seamlessly linked to a smartphone application. The system begins with sensor calibration to establish reference values, followed by continuous tracking of sensor variations, particularly accelerometer data, to detect motion patterns with precision. Once paired with the designated Android application, the wearable device analyze movement data and transmits GPS coordinates to a centralized server, ensuring real-time location tracking of both the device and the associated iBeacon. Planned future improvements include the integration of Google Maps for enhanced real-time visualization, interactive monitoring, geofencing alerts, and emergency response notifications, making the system more robust and responsive to safety concerns.

关键词： Accelerometers Tracking Web services Real-time systems Threat assessment Software Safety Servers Wearable devices Monitoring

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Optimizing Web Server Performance: A Comparative Analysis of Central Manager and Round Robin Load Balancing Algorithms

Optimizing Web Server Performance: A Comparative Analysis of...

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International Conference on Electrical engineering and computer science (ICECOS)

作者： Fahmi Permana Arrasyid Deris Stiawan Nurul Afifah Ahmad Heryanto Iman Saladin B. Azhar Ali Bardadi Bhakti Yudho Suprapto Iwan Pahendra Anto Saputra COMNETS Rerearch Group Faculty of Camp. Science Sriwijaya University Palembang Indonesia Computer Engineering Dept. Faculty of Comp. Science Sriwijaya University Palembang Indonesia Information System Dept. Faculty of Comp. Science Sriwijaya University Palembang Indonesia Electrical Engineering Dept. Faculty of Engineering Sriwijaya University Palembang Indonesia

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

Central manager algorithm has a lower average throughput compared to the round robin algorithm. This is evidenced by a significant decrease in throughput values during measurements. Central manager algorithm provides a faster average response time than round robin algorithm, despite experiencing an increase in response time values over time. Central manager algorithm shows a higher request loss rate and many requests are not served. Under normal performance conditions, both algorithms yield the same average request loss rate, achieving stable values without failed requests. The central manager algorithm ensures relatively balanced resource availability, including CPU and memory usage, across each slave web server compared to the round robin algorithm. The results show that increasing the number of clients and client requests during system testing to better observe server resource availability and load distribution performance. For future research, it is recommended to increase the number of clients in the system testing to enhance server load and to introduce client request actions on the web server to further highlight resource availability on each web server.

关键词： Electrical engineering computer science system testing Throughput Load management Web servers Loss measurement Time measurement Time factors Round robin

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An Artificial Intelligence based Machine Learning Approach for Automatic Blood Glucose Level Identification of Diabetes Patients

An Artificial Intelligence based Machine Learning Approach f...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Maguluri, Lakshmana Phaneendra Santhosh, K. Meenakshi, K. Farooqui, Muhammad Ahtesham Parveen Sultana, H. Saravanan, V. Department of Computer Science and Engineering Andhra Pradesh Guntur India Kombolcha Institute of Technology Wollo University Department of Mechatronics Ethiopia Cmr Institute of Technology Department of Mathematics Bengaluru Karnataka India Sam Global University Dept. of Computer Science & Engineering Madhya Pradesh Raisen India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India Dambi Dollo University Department of Computer Science Dambi Dollo Ethiopia

ISBN: (纸本)9798350323887

In general, treatments and medications for diabetics are usually prescribed based on the level of glucose-level in the human-blood. Blood glucose-level was calculated by performing two separate tests: before meals and after meals. The practical functions present in this test enable physicians to carry out appropriate treatment modalities. This paper introduces an improved method of running a machine learning system. Its main task is to accurately analyze the given input data and calculate the correct point of blood-glucose in the blood of diabetics. It was designed to do so and then list the appropriate control methods and drugs and share that data with the user in a paperless digital manner. Thus it is enough for patients to go to laboratories and do tests. Only their results will be calculated and sent to them for treatment. © 2023 IEEE.

关键词： Glucose

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FA-Net: A Fuzzy Attention-aided Deep Neural Network for Pneumonia Detection in Chest X-Rays

arXiv

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arXiv 2024年

作者： Roy, Ayush Bhattacharjee, Anurag Oliva, Diego Ramos-Soto, Oscar Alvarez-Padilla, Francisco J. Sarkar, Ram Dept. of Electrical Engineering Jadavpur University Kolkata India Dept. of Computer Science and Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Universidad de Guadalajara CUCEI Guadalajara Mexico Dept. of Computer Science and Engineering Jadavpur University Kolkata India

Pneumonia is a respiratory infection caused by bacteria, fungi, or viruses. It affects many people, particularly those in developing or underdeveloped nations with high pollution levels, unhygienic living conditions, overcrowding, and insufficient medical infrastructure. Pneumonia can cause pleural effusion, where fluids fill the lungs, leading to respiratory difficulty. Early diagnosis is crucial to ensure effective treatment and increase survival rates. Chest X-ray imaging is the most commonly used method for diagnosing pneumonia. However, visual examination of chest X-rays can be difficult and subjective. In this study, we have developed a computer-aided diagnosis system for automatic pneumonia detection using chest X-ray images. We have used DenseNet-121 and ResNet50 as the backbone for the binary class (pneumonia and normal) and multi-class (bacterial pneumonia, viral pneumonia, and normal) classification tasks, respectively. We have also implemented a channel-specific spatial attention mechanism, called Fuzzy Channel Selective Spatial Attention Module (FCSSAM), to highlight the specific spatial regions of relevant channels while removing the irrelevant channels of the extracted features by the backbone. We evaluated the proposed approach on a publicly available chest X-ray dataset, using binary and multi-class classification setups. Our proposed method achieves accuracy rates of 97.15% and 79.79% for the binary and multi-class classification setups, respectively. The results of our proposed method are superior to state-of-the-art (SOTA) methods. The code of the proposed model will be available at: https://***/AyushRoy2001/FA-Net. © 2024, CC BY.

关键词： Medical imaging

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A Low-Power 2-to-7 Modulus Programmable Prescaler with 50% Output Duty Cycle

A Low-Power 2-to-7 Modulus Programmable Prescaler with 50% O...

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Kumar, Ravi Nagulapalli, Rajasekhar Hake, Rushikesh Vishvakarma, S.K. Institute of Technology Indore Dept. of Electrical Engineering Indian India Oxford Brookes University Dept. of Electrical and Computer Science Oxford United Kingdom

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

This paper presents a novel optimized low-power multi-modulus programmable frequency divider with a modulus range of 2-to-7 with 50% output duty cycle for high-speed applications. The proposed divider is demonstrated with the division range from 2-127, which can be extended by adding more stages of the 2/3 Prescaler in the divider chain. The whole design is implemented using 0.18- μm CMOS process with a supply of 1.8 V. From simulations result, the proposed design achieves a maximum operating frequency of 5 GHz with 6.5 mW of power consumption in divide-by-127 mode of operation while providing the 50% output duty cycle. © 2022 IEEE.

关键词： Flip flop circuits

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Facial Emotional Recognition Using Convolutional Neural Network

Facial Emotional Recognition Using Convolutional Neural Netw...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Deepak Raj Md. Abdul Wassay Dept. of Computer Science Chandigarh University Mohali India Dept. of Computer Science Engineering Chandigarh University Mohali India

The face of a person is his identity. Most of his emotions, issues can be derived from his face. Face is the window to the soul which was said by famous French doctor Duchenne de Boulogne. He used several techniques to support his theory like giving shocks of electric impulses to understand how a person reacts to muscular contractions. He also tried to induce some of the expressions of bizarre looking. The ultimate aim is to analyze how much muscles contribute to emotion. He successfully derived lots of human emotions which is hidden. After 200 years this field is still active as it requires more experiments to extract invisible truths of human truths. Lots of Emotion Recognition which is automatic have been seen in the field of Marketing and Advertising. It is also seen in the field of Medical, Law and order$\ldots$etc. The main fundamental question arrives whether it is good to enter into our personal space. This a question which has lots of dimension. Those who are against this practice is claiming that this is a violation of human rights by which we can use the stored data to harm the society in future. Even though it is a major concern if we can rectify the above the scope in this area is phenomenal. Lots of researchers are still working on this area because of mainly one reason that is its wide application in the area of Medical science. It can be used by a doctor for diagnosing a person with certain psychological disorders, children with Autism, person with Parkinson’s disease. It can be widely used to diagnose children with Autism so that particular child can be motivated at early stage leads to his success in future and thus provide a good citizen to the society. In our research we are using several datasets such as FER2013, CK+$\ldots$etc. These datasets have been given to a model which includes Deep Convolutional Neural Network. Here input image is given to a model using camera. The particular image has to done preprocessing to extract fine features in th

关键词： Emotion recognition Pediatrics Autism Technological innovation Face recognition Psychology Medical services

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Monocular UAV Motion Planning Method Based on Deep Reinforcement Learning

Monocular UAV Motion Planning Method Based on Deep Reinforce...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Man-Chen Hsueh Chun Ku Xiu-Zhi Chen Yen-Lin Chen Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper presents a monocular UAV motion planning method based on the DRL method SDDPG. By processing image data with the self-proposed lightweight dept. estimation model and a pretrained encoder, the information is sent into an improved Actor-Critic Network, which addresses overestimation issues and enhances learning efficiency. The experiments were conducted in Unreal Engine 4 and Airsim, which demonstrate the improvement of the motion planning ability, enabling UAVs to autonomously navigate and safely reach the defined destination.

关键词： Visualization Image coding Navigation Computational modeling Atmospheric modeling Network architecture Autonomous aerial vehicles Deep reinforcement learning Planning Resource management

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Revolutionizing Mango Crop Health: A Deep Learning Approach for Precise Identification of Leaf Diseases

Revolutionizing Mango Crop Health: A Deep Learning Approach ...

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International Conference on Advanced Computing and Communication systems (ICACCS)

作者： Navadharani N Mouthami K R. Devipriya S. Anandamurugan Dept. of Computer Science Engineering KPR Institute of Engineering and Technology Coimbatore India Dept. of Information Technology Kongu Engineering College Erode India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Plant diseases pose significant challenges to the agricultural sector, leading to decreased crop production and financial losses. Mango, being a popular fruit worldwide, is susceptible to various leaf diseases. Traditional diagnosis of these disorders requires labor-intensive and time-consuming manual picture interpretation. To address this issue, deep learning models have been utilized to enhance the precision and effectiveness of identifying leaf illness in mango crops. In this study, ResNet50, ResNext50, Convnet, and Conventv2 deep learning models were compared to find the most successful approach. The results demonstrated that the Conventv2 model achieved the highest accuracy of 98.21%. The major aim of the research work is improvising sustainable agricultural practices by leveraging advanced technology for accurate plant disease classification. The findings have implications for improving mango crop management and ensuring a more sustainable and efficient agricultural industry.

关键词： Deep learning Economics Plant diseases Accuracy Biological system modeling Crops Production Manuals Residual neural networks Image classification

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FloraNet: An Approach to Prediction of Sea Buckthorn Disease based on a Convolutional Neural Network and a Random Forest Method

FloraNet: An Approach to Prediction of Sea Buckthorn Disease...

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Emerging Technology (INCET), International Conference for

作者： J. Pushpakumari Nuthanakanti Bhaskar G. Divya Murali Kanthi K. Srujan Raju Jonnadula Narasimharao Dept. of Computer Science and Engineering (AIML & IOT) VNRVJIET Hyderabad Telangana India Dept. of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Dept. of Information Technology CMR Technical Campus Hyderabad Telangana India Department of Computer Science & Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In the current study, FloraNet was used to present an improved method for diagnosing leaf diseases in Sea Buckthorn. In the proposed model, Convolutional Neural Networks (CNN) and Random Forests (RF) are integrated. Essentially, CNN architecture is composed of a pair of convolutional layers, followed by a max-pooling layer; it is then followed by another pair of convolutional layers, before finishing off with a final pair of convolutional layers. Performance indicators such as Precision, Recall, F1-Score, or Accuracy are used to determine the performance of the model in terms of its performance. There were five disorders under investigation: Phakopsora, Erysiphe, Septoria, Colletotrichum, Phoma, as well as viral infections. Overall, the results show that the Micro Average accuracy for all classes is 95.11 %. According to the results, the individual accuracy ranges from 0.98 to 0.99 for each illness, while the F1-Scores range from 94.02% to 95.91% for each illness. This study concludes that the weighted average accuracy of 95.11 demonstrates a high level of proficiency of the model in identifying diseases, and indicates that the model is capable of identifying a wide variety of diseases. By using this integrated approach, agricultural scientists and researchers are able to detect Sea Buckthorn leaf diseases.

关键词： Measurement Accuracy Sea measurements Forestry Predictive models Solids Convolutional neural networks

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