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5th International Conference on Informatics and Computing, ICIC 2020

作者： Fatchan, Muhamad Hery Purnomo, Mauridhi Affandy Zainul Fanani, Ahmad Marlinda, Linda Doctoral Program of Computer Science Universitas Dian Nuswantoro Semarang Indonesia Institut Teknologi Sepuluh Nopember Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9781728180472

One of the obstacles to detecting facial emotions is the lack of analysis in the process of emotional expression on human faces based on a photo camera. Identifying features and implementing different feature combinations can improve accuracy, in most cases. Trial detection through the use of feature vectors with higher dimensions that contain facial emotions can provide a lot of information on the accuracy of the data. The purpose of this study is to determine the highest accuracy results from the comparison of Support Vector Machine and Neural Network Algorithms. It can be seen that the Support Vector Machine has the highest accuracy value, which is 87%, while the Neural Network algorithm only has an accuracy of 85%. The results of the ROC Curve show that the Support Vector Machine achieves the best AUC value, namely 0.97. Comparison between Neural Network algorithm and Support Vector Machine for prediction of emotions using data types with many variations of emotions including anger, sadness, happy and surprise. The Support Vector Machine method is an accurate algorithm and this method is also very dominant over other methods. Based on the Accuracy, AUC, and T-test method, this method falls into the best classification. © 2020 IEEE.

关键词： Face recognition

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On a Pickup to Delivery Drone Routing Problem: Models and Algorithms

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SSRN 2022年

作者： Candia-Véjar, Alfredo Gómez-Lagos, Javier Rojas-Espinoza, Benjamín Doctoral Program in Engineering Systems Faculty of Enginnering Universidad de Talca Curicó3340000 Chile School of Civil Industrial Engineering Faculty of Engineering Universidad de Talca Curicó3340000 Chile Dept. of Industrial Engineering Faculty of Engineering Universidad de Talca Curicó3340000 Chile

We present a new variant of the Pickup and Delivery Routing problem. Given a set of customers, facilities, a depot, and a homogeneous fleet of drones, the Pickup to Delivery Drone Routing Problem (PDDRP) aims to find a drone scheduling such that a drone serves the customer’s order from a set of available facilities. Each drone starts in the depot, flies to pick the customer’s order in a facility, and continues its flight to deliver the parcel to a customer. Then, the drone begins another service, and once its last service is completed, it returns to the depot. The objective is to minimize the makespan associated with the drone fleet. The layer of facilities forcing drones to visit one of them to pick the parcel makes the problem different from traditional pickup and delivery routing problems. We present threemixed-linear programming models to obtain optimal solutions for the problem. The first model is related to the multiple Traveling Salesman Problem (m-TSP), the second is associated with the Parallel Machine Scheduling Problem (PMS), and the third was developed specifically for the new *** the high computational complexity of the PDDRP, we design a Greedy Randomized Adaptive Search Procedure (GRASP) to find near-optimal solutions when exact approaches cannot achieve (near) optimal solutions. Computational experiments show that a commercial solver could solve only small problem instances. GRASP can find reasonable solutions in a short time when medium and large instance sizes need to be solved. Finally, we show that some routing problems for delivery, allowing truck-drone collaboration, could be formulated as an extension of PMS. © 2022, The Authors. All rights reserved.

关键词： Pickups

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied Science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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A Survey on the Impact of Data Analytics and Machine Learning Techniques in E-commerce 5

A Survey on the Impact of Data Analytics and Machine Learnin...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Dheva Rajan, S. Vavilapalli, Sivajee Hasan, Shahriar Kumar, Ritesh Rafa, Nafisa Muda, Iskandar University of Technology and Applied Sciences Almusannah Information Technology Oman Duvvada Department of Management Studies Visakhapatnam India University of Dhaka Department of Finance Bangladesh Maharishi School of Engineering and Technology Uttar Pradesh Lucknow India BBA in Marketing Institute of Business Administration University of Dhaka Dhaka Bangladesh Universitas Sumatera Utara Faculty Economic and Business Department of Doctoral Program Medan20222 Indonesia

ISBN: (纸本)9798350398267

Current article has been developed for understanding the impact of data analytics and machine learning technique in e-commerce. If the observation has been developed for the current market of this technology it can be detected that real-life industry increases their investment which showed the interest in getting advantages. Identification of the problem of costing and different algorithm suitability objectives has been set for understanding the real impact of e-commerce. Secondary data collection has been used for understanding the different usability of machine learning and data analytics for e-commerce. It has been detected that using machine learning brings the benefits of automation and improves continuous growth. Data analytics provide the scope of accuracy build-up. Apart from this different area of application and the different techniques has been analyzed. Understanding become easier and the scope of future benefits can be identified. © 2022 IEEE.

关键词： Data Analytics

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An Improved Particle Swarm Approach for Energy-Aware Location-Aided Routing in Mobile Ad-Hoc Network

An Improved Particle Swarm Approach for Energy-Aware Locatio...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sandeep Kumar Khemraj Sharma P. M. Ashok Kumar Arpit Jain Sukesh Kumar Bhagat Pranita Singh Tula’s institute Dehradun U.K India Department of Business Administration Doctoral Program in Management Sciences Nanhua University Dalin Chiayi R.O.C Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science & Engineering Uttaranchal University Dehradun Uttarakhand India Department of Computer Science and Engineering Roorkee Institute of Technology Roorkee Uttarakhand India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

A network with no infrastructure, multiple hops, and dispersed nodes called a MANET. Nodes can communicate directly or through intermediaries without the need for permanent infrastructure. In dynamic networks like MANETs, routing procedures enhance the overall appearance of the network by ensuring Quality of Service (QoS). A routing protocol may use a composite routing measure to determine the best path for transferring data. Optimal location-based routing for MANETs based on energy-awareness is presented in this paper. Optimal routes are selected using this fast algorithm. Particle swarm optimization (PSO) is used instead of location-aided routing to select the normal data transmission route. Last but not least, the network is tested by simulating its packet delivery ratio, its energy level, its delay, and its overhead.

关键词： Scalability Transportation Quality of service Routing Particle measurements Routing protocols Real-time systems Delays Security Particle swarm optimization

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids

Integrating Noise Classification and Speech Enhancement Mode...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Shin-Chi Lai Wen-Ho Juang Ming-Hwa Sheu Jeng-Dao Lee Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm.

关键词： Accuracy Noise Noise reduction Speech recognition Speech enhancement Hearing aids Classification algorithms Convolutional neural networks Human voice Older adults

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Gelatine-Based Phantom For Simulating A-Line Reverberations in Lung Ultrasonogram

Gelatine-Based Phantom For Simulating A-Line Reverberations ...

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International Conference on Instrumentation Control and Automation (ICA)

作者： Citra Alfa Nabila Saeful Bahri Damar Rastri Adhika Suprijanto Suprijanto Master Program of Instrumentation and Control Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Doctoral Program of Engineering Physics Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Advanced Functional Material Research Group Faculty of Industrial Technology Research Center of Nanosciences and Nanotechnology Institut Teknologi Bandung Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institute Teknologi Bandung Bandung Indonesia

To understand typical features that could be imaged using ultrasonography (USG) in the training phases of medical students or practitioners, a lung phantom is required. The research team suggests using a gelatine-based phantom to demonstrate A-line reverberations in lung ultrasound. The ordinary gelatine-based phantom has the disadvantage of being too fragile and prone to breaking. The specific process was attempted on gelatine with a plasticizer and carrier agent to increase the gelatine's strength and elasticity. To simulate the A-lines, two different textures of gelatine layers are used, with other processes in between. A special procedure was used to set off the various acoustic impedances affecting reverberation physics with those varied textures. Based on USG image analysis, the proposed lung phantom material has some advantages: it is inexpensive and easy to find, despite its complicated procedures. Furthermore, it could be processed to have the desired properties and recycled after hardening with water bath melting.

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On Max Pressure Urban Traffic Control with Learning

On Max Pressure Urban Traffic Control with Learning

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Information Technology International Seminar (ITIS)

作者： Adiyana Putri Endra Joelianto Herman Y Sutarto Faculty of Industrial technology Institut Teknologi Bandung Doctoral Program of Engineering Physics Bandung Indonesia Faculty of Industrial Technology Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia

Managing the traffic system is a multifaceted challenge, imposing various solutions to prevent congestion. Traffic management aims to create an efficient control system with numerous advantages, including reduced fuel emissions, lower stress levels, and enhanced driving discipline. Max Pressure stands as one of the techniques for traffic control, designed to adapt and enhance network throughput by considering pressure, defined as the difference between upstream and downstream queue lengths. Recent developments have yielded improved performance in Max Pressure through learning. In this adaptive control mechanism, the concept of Max Pressure is integrated as a reward function within reinforcement learning. This study introduces the “Max Pressure learning” concept, revealing its potential for creating adaptable traffic controllers. By modifying the pressure equation and transforming it into a performance metric, the original Max Pressure concept effectively responds to diverse traffic conditions. This paper showcases research findings demonstrating the surprising effectiveness of pressure as a control mechanism in the context of the learning process. Ultimately, using pressure enables controllers to efficiently alleviate gridlock situations in traffic, highlighting its promising role in optimizing traffic management and enhancing overall traffic system performance.

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Open innovation: A technology transfer alternative from universities. A systematic literature review

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Journal of Open Innovation: Technology, Market, and Complexity 2023年第3期9卷

作者： Bejarano, José Bestier Padilla Sossa, Jhon Wilder Zartha Ocampo-López, Carlos Ramírez-Carmona, Margarita Program in Electronic Instrumentation Technology Faculty of Basic Sciences and Technologies Universidad del Quindío Armenia 30004 Colombia Doctoral Program in Management of Technology and Innovation at Universidad Pontificia Bolivariana Medellin 050031 Colombia Department of Engineering Faculty of Agro-Industrial Engineering Universidad Pontificia Bolivariana Medellín 050031 Colombia Center of Studies and Research on Biotechnology (CIBIOT) Faculty of Chemical Engineering Universidad Pontificia Bolivariana Medellín 050031 Colombia

This article presents a study that analyzes open innovation and its contribution to technology transfer processes in universities, addressing as key questions: What elements or factors of open innovation contribute to the performance of technology transfer in universities? What could be the role of open innovation in the technology transfer process of universities? The study was conducted from a systematic literature review in the Scopus, Web of Science, ScienceDirect, and SpringerLink databases on 95 peer-reviewed articles selected from the last 20 years. From an exploratory approach, the critical analysis of the state of knowledge permitted the identification of the 36 factors of open innovation contributing to the performance of technology transfer in universities, from knowledge management and innovation, resources, and capabilities, R&D management with universities, and the university-industry relationship. According to the findings and success cases, open innovation from the use of internal and external knowledge boosts R&D+i projects, encouraging universities to keep close relations with actors from regional innovation systems, contributing positively to the performance of technology transfer in institutions and stimulating the commercialization of the developments generated from research. In the future, we propose delving into outbound open innovation, its systematization, contribution, and impact on university technology transfer processes. © 2023 The Authors

关键词： Entrepreneurial university Higher education institutions Knowledge flow Outbound open innovation Research and development Technology transfer

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Capability of short Vis-NIR band tandem with machine learning to rapidly predict NPK content in tropical farmland: A case study of Aceh Province agricultural soil dry land, Indonesia

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Case Studies in Chemical and Environmental engineering 2024年 9卷

作者： Mustaqimah Devianti Munawar, Agus Arip Sufardi, Sufardi Doctoral Program of Agricultural Science Syiah Kuala University Banda Aceh 23111 Indonesia Department of Agricultural Engineering Faculty of Agriculture Syiah Kuala University Banda Aceh 23111 Indonesia Department of Soil Science Faculty of Agriculture Syiah Kuala University Banda Aceh 23111 Indonesia

Developing a rapid, accurate, and cost-effective method for predicting constituents related to soil nitrogen (N), phosphorus (P), and potassium (K) content is considered substantial. One procedure that can work with these criteria and is currently popularly researched is the visible and near-infrared (Vis-NIR) method. Furthermore, machine learning algorithms have been proven to increase the accuracy and robustness of calibration models developed based on short Vis-NIR spectra. Therefore, the potential of combination using short Vis–NIR band (450–1050 nm) in tandem with machine learning algorithm (k-nearest neighbors (kNN), adaboost, random forest) was investigated and evaluated deeply in this study to predict the soil content of N, P, and K using 122 tropical dry farmland samples of Aceh Province origin, Indonesia. Within the investigated variables, the parameter RPD achieved excellent classification (RPD>4.1) for any application for predicted tropical dry farmland soil samples N, P, and K using min-max normalization followed by the kNN algorithm. The short Vis–NIR tandem with machine learning provided promising calibration predictions of N, P, and K content for agricultural soil dry land, especially from Aceh Province, Indonesia. © 2024 The Authors

关键词： Chemometrics Non-destructive measurement Soil nutrients Spectroscopy

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