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A Lightweight CNN-Based Pothole Detection Model for Embedded Systems Using Knowledge Distillation 21

A Lightweight CNN-Based Pothole Detection Model for Embedded...

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21st International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2022

作者： Musa, Aminu Hassan, Mohammed Hamada, Mohamed Kakudi, Habeebah Adamu Amin, Md. Faizul Ibne Watanobe, Yutaka Department of Computer Science Federal University Dutse Nigeria Faculty of Computer Sci. & Info. Technology Bayero University Kano Nigeria Software Engineering Lab University of Aizu Japan Graduate School of Computer Science University of Aizu Japan Division of Information System University of Aizu Japan

ISBN: (纸本)9781643683164

Recent breakthroughs in computer vision have led to the invention of several intelligent systems in different sectors. In transportation, this advancement led to the possibility of proposing autonomous vehicles. This recent technology relies heavily on wireless sensors and Deep learning. For an autonomous vehicle to navigate safely on highways, the vehicle needs equipment to aid with detecting road anomalies such as potholes ahead of time. The massive improvement in computer vision models such as Deep Convolutional Neural networks (DCNN) or vision transformers (ViT) resulted in many success stories and tremendous breakthroughs in object detection tasks;this enabled the use of such models in different application areas. But many of the reported results are theoretical and unrealistic in real-life. Usually, the nature of these models is extensive;they are trained on High-performance computers or cloud computing environments with GPUs, which challenge their usage on edge devices. However, to come up with a light model that can fit into embedded devices, the model size has to be reduced significantly so that the performance will not be affected. Therefore, this paper proposes a lightweight model of pothole detection for an embedded device. The model achieved a state-of-the-art accuracy of 98%, with the number of parameters reduced to more than 70% compared with a deep CNN model;the model can be trained and deployed on embedded devices such as smartphones efficiently. © 2022 The authors and IOS Press. All rights reserved.

关键词： Autonomous vehicles

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SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain Management 4

SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain ...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Amarasinghe, A.A.B.G. Gamagedara, G.M.H.S. Pitigala, K.K. Subawickrama, H.D.A.E. Abeywardena, Kavinga Yapa Yapa, Kanishka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application. © 2022 IEEE.

关键词： Smart contract

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Sentiment Analysis on Students’ Evaluation of Higher Educational Institutions

Sentiment Analysis on Students’ Evaluation of Higher Educat...

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International Conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Toçoğlu, Mansur Alp Onan, Aytuğ Department of Software Engineering Faculty of Technology Manisa Celal Bayar University Manisa45400 Turkey Department of Computer Engineering Faculty of Engineering and Architecture İzmir Katip Çelebi University İzmir35620 Turkey

ISBN: (纸本)9783030511555

Sentiment analysis is the method of identifying and classifying the views of users from text documents into different sentiments, such as positive, negative or neutral. Sentiment analysis can be employed to extract structured and informative knowledge from unstructured text pieces. This knowledge can serve as an important source of information for decision support systems and individual decision makers. Sentiment analysis plays an important role in many fields like education, where student input is crucial for assessing the effectiveness of educational institutions. In this paper, we present a machine learning based approach to sentiment analysis on students’ evaluation of higher educational institutions. We analyze a corpus containing approximately 700 student reviews written in Turkish, with the use of conventional text representation schemes and machine learning classifiers. In the experimental analysis, three conventional text representation schemes (i.e., term-presence, term-frequency, TF-IDF scheme) and three N-gram models (1-gram, 2-gram and 3-gram) have been considered in conjunction with four classifiers (i.e., support vector machines, Naïve Bayes, logistic regression, and random forest algorithm). The predictive performance of four ensemble learners (i.e., AdaBoost, Bagging and Random Subspace and voting algorithm) have been also evaluated. The empirical results indicate that machine learning based approach yields promising results on students’ evaluation of higher educational institutions. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Multi-task Scheduling Algorithm Based on Self-adaptive Hybrid ICA–PSO Algorithm in Cloud Environment 8th

Multi-task Scheduling Algorithm Based on Self-adaptive Hybri...

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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： Tabrizchi, Hamed Kuchaki Rafsanjani, Marjan Balas, Valentina Emilia Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania

ISBN: (纸本)9783030521899

Cloud is a new concept in Internet technology and has brought many benefits, particularly, in the field of computing. Cloud has changed how on-demand resources are allocated to the different user requests and has provided more optimal resource utilization. Tasks (user requests) have different kinds, including deadline-based tasks having time-related limitations. This kind of task has various parameters and scheduling them is more challenging than scheduling the individual tasks. In this regard, many papers have been published according to these parameters. The objective of this paper is to propose a novel self-adaptive hybrid ICA–PSO algorithm for dealing with associate multi-task scheduling problem. To improve the exploration, two algorithms, namely, the imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) were combined in this study. The results of the present study outperform those of the existing mechanisms. © 2021, Springer Nature Switzerland AG.

关键词： Scheduling algorithms

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Enhancing Password Security: Analyzing Password Management Practices Among IT Students

Enhancing Password Security: Analyzing Password Management P...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Anita Mihalovicsne Kollar Jozsef Katona Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kando Kalman Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

Today, with the digital world in its full swing, strong password management is key to protecting sensitive information from both internal and external threats. The current research explores the passwords managing behavior of the IT students at the University of Dunaujvaros. A survey made in May 2024 questioned 68 students, 59 males and 9 females, about behaviors such as using passwords with complexity, not repeating them for multiple accounts or machines, changing them regularly, and which applications they use for password management. The findings have depicted that, as a whole, students appear to be moderately sensitive about the password security instructions. Nonetheless, most continue to indulge in unsafe practices, including easily remembered or weak passwords and infrequent password changes. Furthermore, approximately half of the responders use password management applications, indicating room for significant improvement in the future. The results suggest that intervention and special education measures should be taken to enhance the practice of password security among future IT experts. This research has good potential to contribute toward strategies designed for people behaving safely in password management, and hence promote a secure digital environment.

关键词： Surveys Continuing education Power engineering Correlation engineering profession Education Passwords Complexity theory Security Protection

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Integrating Design Thinking into software Process Improvement 4

Integrating Design Thinking into Software Process Improvemen...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Khalid, Anum Hashmi, Amber Sarwar Kiani, Azaz Ahmed Muzammal, Syeda Mariam Jhanjhi, N.Z. Bibi, Ruqia Kumar, Sandeep Foundation University Department of Software Engineering Islamabad Pakistan Rawalpindi Women University Department of Software Engineering Rawalpindi Pakistan National University of Modern Languages Rawalpindi Pakistan School of Computer Science Taylor's University Faculty of Innovation and Technology Subang Jaya Malaysia University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Pakistan Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

The need for software improvement is growing widely due to the demand for technology development. The issues that arise by expanding the software processes in the workplace are expense, lack of understanding, time constrained, and lack user experience. This research paper focuses on proposing a framework for integrating design thinking into software process improvement. The secret to project success may lie in fusing methodological and reusable approaches with human-centric techniques. A mixed-methods research strategy is proposed to compile thorough information and reach conclusions. In addition, it incorporates components of both qualitative and quantitative research (secondary research and descriptive analysis). The assumption is that the existing model significantly affects the user satisfaction variable. This indicates a statistically significant association between the individual's experience (number of years) and design thinking integration into the software process improvement. The variables in the proposed model and the dependent variable user satisfaction accounts for 6.1 © 2024 IEEE.

关键词： software design

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Classification of Plants Leave Using Image Processing and Neural Network 8th

Classification of Plants Leave Using Image Processing and Ne...

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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： Bagherinezhad, Hashem Kuchaki Rafsanjani, Marjan Balas, Valentina Emilia Koles, Ioan E. Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania

ISBN: (纸本)9783030521899

Plants are one of the most widely used resources for humans in different fields. Therefore, the distinction between the plant species is important and it is referred to as the plant detection system. Until now, this task has been done by the expert botanists which is an overwhelming and time consuming task. Moreover, there is a lack of the memory and the human fault, so the researchers endeavored to solve these disadvantages using the AI algorithms. For this goal, in this paper, a system is proposed that includes four phases: pre-processing, feature extraction, training, and test. In this method, we use the combination of the useful features of the leave shape, the leave texture, the leave color, and we provide a method for the classification of a number of the plant species. Finally, the feature vectors will be created and then, the classification is performed by using feed-forward back-propagation multi-layer perceptron artificial neural network algorithm. Then, the results of this method compare with other methods. The obtained results show the high accuracy of this method for a large number of the species in different conditions (such as pests, season changes and lighting). © 2021, Springer Nature Switzerland AG.

关键词： Feature extraction

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Machine Learning Approaches for Efficient Traffic Flow in Smart Cities 3

Machine Learning Approaches for Efficient Traffic Flow in Sm...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Kulkarni, Aniket Anitha, P. Valluri, John Yesudas Sunena Rose, M.V. Hemavathi, U. Hussein, Omar M. Software Qa Manager Drata Inc San DiegoCA United States Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu Srivilliputhur India Faculty of Management Studies Bengaluru India Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Avadi Chennai India Al-Farahidi University Medical Technical College Medical Instrumentation Engineering Baghdad Iraq

ISBN: (纸本)9798350354379

These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data and dynamic behaviour over time, they are replacing analytical and statistical methods in the solving of real-world problems. This research presents a convergence of machine-learning (ML) and deep-learning (DL) calculations for traffic stream prediction;this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accordance with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML and DL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML and DL models are created using data from four of the six junctions. The excellent performance metrics obtained by all ML and DL computations suggest that their application to intelligent traffic light management is feasible. © 2024 IEEE.

关键词： Traffic congestion

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Database Scaling on Kubernetes 3

Database Scaling on Kubernetes

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Perera, H.C.S. De Silva, T.S.D. Wasala, W.M.D.C. Rajapakshe, R.M.P.R.L. Kodagoda, N. Samaratunge, Udara Srimath S. Jayanandana, H.H.N.C. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe10115 Sri Lanka

ISBN: (纸本)9781665408622

Kubernetes is a hot topic in the field of software engineering and Distributed Computing. When compared to previous methods, the principle underlying Kubernetes, which is containerization, has altered how applications are created and delivered. However, when considering the state, particularly the databases, with Kubernetes, there is a scalability and data synchronization barrier. The most frequently used approach is to host the database outside of Kubernetes and maintain connectivity with the cluster. Kubernetes inherent capabilities are sufficient for hosting databases. But that requires high domain knowledge to do the configurations and maintain the databases on Kubernetes. The purpose of this research is to fulfil that gap by introducing a solution for managing highly available databases on Kubernetes. The solution is limited to managing PostgreSQL databases on Kubernetes using auto-scaling. A novel algorithm is proposed for auto-scaling, as previous algorithms do not take database requests into account when determining the scaling need. The drawbacks of data synchronization and auto-scaling will be solved in this research, and the end user will be able to access the service without interruption for the majority of the time, as the final solution makes the database cluster highly available for the service layer. © 2021 IEEE.

关键词： Database systems

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm

Web Application to predict Skin Disease: An Approach Using D...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Eelanila Thayalan Ayingaran Arumugavel Sharujan Ganeshan Makenthiran Dalaxshan Harinda Fernando Kaushalya Rajapakse Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system’s proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife.

关键词： Transfer learning Medical services Skin Pattern recognition Medical diagnostic imaging Standards Robots

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