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Robust Facial Emotion Recognition System via De-Pooling Feature Enhancement and Weighted Exponential Moving Average

Robust Facial Emotion Recognition System via De-Pooling Feat...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Win, Khin Cho Akhtar, Zahid Mohan, C. Krishna Indian Institute of Technology Dept. of Artificial Intelligence Hyderabad India State University of New York Polytechnic Institute Dept. of Network and Computer Security UticaNY United States Indian Institute of Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350350821

Facial expression recognition (FER) in an uncontrolled environment presents a formidable challenge in affective computing and human-machine interaction domains. Existing FER models fail to generalize due to the innate expression nature of the intra-class separability and inter-class compactness. Although most state-of-the-art FER research focuses on more advanced frameworks and well-separated discriminative loss functions, further exploration is needed to obtain high-quality expression features. This paper proposes a robust feature enhancement approach for FER by integrating the De-pooling Feature Enhancement and Weighted Exponential Moving Average (WEMA) of Stochastic Gradient Descent (SGD). The proposed method utilizes De-Pooling Feature Enhancement to capture and enhance the high-quality expression features, while WEMA of SGD optimizes the training process for improved stability and convergence. Our extensive experimental analysis of benchmark datasets signifies that our proposed method prevails over state-of-the-art methods, achieving superior accuracy performances of 86.43% on FER2013, 94.97% on FERPlus, 94.71% on RAF-DB, as well as 80.62% and 72.90% on AffectNet, of 7 class and 8 class, respectively. © 2024 IEEE.

关键词： Stochastic systems

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Evaluation and Comparison of Facial Recognition technology in Drawn Portraits 13

Evaluation and Comparison of Facial Recognition Technology i...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Chang, Cheng Fanjiang, Yong-Yi Hung, Chi-Huang Chen, Jung-Hua Phung, Kiem-Trinh Trieu, Van-Duc Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei Taiwan Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan Lee-Ming Institute of Technology Dept. Electronical Engineering New Taipei City Taiwan

ISBN: (纸本)9798350355079

Numerous studies have proposed various methods and applications for facial recognition. In camera-equipped environments, these methods can easily identify real faces. However, recognizing faces on printed or Portrait Drawing becomes more complex. In other words, real faces are three-dimensional, and while algorithms can sample feature values, identifying portrait drawing face on paper is relatively more challenging. This study evaluates and compares the effectiveness of various facial recognition techniques on paper-based images, including Haar-cascade, MTCNN, and Dlib 68 feature points. © 2024 IEEE.

关键词： Face recognition

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Predict of the Hidden Drug Interactions Based on Similarity Utilizing the Knowledge Graph

Predict of the Hidden Drug Interactions Based on Similarity ...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Elfiky, Mariam Gamal Ghoniemy, Samy Eldin, Ahmed Sharaf University of Hertfordshire hosted by Gaf Dept. of Information Technology Management for Business Cairo Egypt Galala University Faculty of Computer Science and Engineering Cairo Egypt Sinai University Faculty of Information Technology and Computer Science Cairo Egypt

ISBN: (纸本)9798350367560

Drug-drug interactions (DDIs) can significantly impact treatment outcomes by causing adverse effects or reducing therapeutic efficacy. Whereas many DDIs are already known, there are numerous hidden interactions. Therefore, this study tries to predict unknown interactions based on drug structure and target information from the Drug Bank dataset. This paper trains the random forest model integrated with a knowledge graph on a dataset containing 922 unique drugs and 731,196 drug interactions. To illustrate the complex interactions between these drugs, we created a knowledge graph with Neo4j. This graph integrates information gathered from the dataset and extracts relevant data using Cypher queries. The inference engine was created according to the model's predictions on unknown DDIs. The random forest model, with an F1-score of 0.82, achieved a recall of 0.99 and a precision of 0.70. Also, 34,580 potential DDIs were extracted from the sample dataset of drugs. By embedding the knowledge network, our inference engine significantly enhanced predicting the hidden interactions and was found strong in the balance of precision and recall. Our research is applicable to the enhancement of drug safety and effectiveness via mitigating potential DDIs by identifying them. © 2024 IEEE.

关键词： Knowledge graph

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Faceswap Deepfakes Detection using Novel Multi-directional Hexadecimal Feature Descriptor 19

Faceswap Deepfakes Detection using Novel Multi-directional H...

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19th International Bhurban Conference on Applied sciences and technology, IBCAST 2022

作者： Qurat-Ul-Ain Javed, Ali Mahmood Malik, Khalid University of Engineering and Technology Dept. of Computer Science Taxila Pakistan University of Engineering and Technology Dept. of Software Engineering Taxila Pakistan Oakland University Dept. of Computer Science and Engineering United States

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

With the growing number of sophisticated deep learning algorithms and fake video generation applications, it is now possible to create highly realistic deepfake videos. Faceswap is the most commonly employed deepfakes approach, which is challenging to detect due to variations in the facial skin tone, illumination conditions, presence of accessories like glasses on the face, compression artifacts, etc. Existing local texture descriptors have achieved better performance on face recognition applications;however, they compute only the limited directional information while ignoring the magnitude details. This motivated us to develop a robust local texture descriptor to extract more directional and magnitude details from the adjacent pixels to effectively represent the video frames. For this purpose, we proposed a robust multi-directional hexadecimal feature descriptor (MDHFD) by combining the local hexadecimal pattern (LHeXDP) and Local Adjacent Neighborhood Magnitude Pattern (LANMP). LHeXDP calculates the orientation-based pattern by computing 1st- and 2nd-order derivatives at 0°, 45°, 90°, and 135° angles from each center pixel. LANMP computes the magnitude information from each central pixel to its adjacent pixels in horizontal, vertical, diagonal, and diagonal-back directions. Histograms of both the LHeXDP and LANMP are fused to compute a multi-directional feature vector, which is used to train a support vector machine to classify between the original or faceswap deepfakes video. We measured the performance of our system on the challenging faceswap subset of a diverse and large-scale Face Forensic++ and World Leaders datasets. Experimental results illustrate that the proposed method outperforms state-of-art methods for the detection of faceswap deepfakes. © 2022 IEEE.

关键词： Pixels

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10-point connector formed by two snap buttons

10-point connector formed by two snap buttons

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Kao, Rally Liao, Kuo-Cheng Wu, Ze-We Chen, Shih-Hung Chen, Chi-Chun Ming Young Biomedical Corp Miaoli Taiwan National Chin-Yi University of Technology Program Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan Bonanza Biotechnology Co.Ltd National Chin-Yi University of Technology Dept. of Computer Science and Information Engineering Taichung Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

With the rapid advancement of electronic textiles, the design and integration of connectors have emerged as pivotal challenges. This study emphasizes the development of a connection solution that amalgamates miniaturization, efficiency, and reliability, specifically tailored for Flexible Conductive Silicone Circuits. A diversified connection scheme is proposed, incorporating elements such as snaps, Pogo pins, and conductive hook-and-loop fasteners, thereby achieving an innovative and pragmatic design. Through meticulous manufacturing processes, including the utilization of hot-melt adhesive, PET films, conductive fabric, and other multilayer materials, a connection system with low impedance and commendable conductivity was successfully engineered. Impedance test outcomes have indicated that the resistance at each connection point is less than 0.7 ohms, substantiating its superior electrical properties. In terms of assembly, this research demonstrates the precise interconnection of the controller and conductive fabric circuit, ensured by metal pin technology, leading to connection stability. This solution not only furnishes appropriate connection technology for specific application scenarios but also opens new avenues and directions for connection design within the domain of electronic textiles. © 2024 IEEE.

关键词： Connectors E-Textiles Flexible Conductive Silicone Circuit PET films

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Constructing Disease Comorbidity Networks Using Statistical Methods from Electronic Health Records

Constructing Disease Comorbidity Networks Using Statistical ...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Bramesh, S.M. Anil Kumar, K.M. P. E. S. College of Engineering Dept. of Information Science & Engineering Mandya India Jss Science and Technology University Dept. of Computer Science & Engineering Mysuru India

ISBN: (纸本)9798350300826

Disease comorbidity is a prevalent and significant concern in modern medical practice. Patients in such circumstances will need specialized medical interventions. This study proposes a network-based computation model to extract the disease comorbidity patterns by utilizing the electronic health records of a 'Diabetes 130-US hospitals for years 1999-2008' dataset. The estimate of disease-disease relationships using statistical methods is a crucial step in this paradigm. A Disease Comorbidity Network (DCN) was constructed based on these relationships. This model can then be used to extract the corresponding illness comorbidity patterns for various races and genders, using the endocrine disease (diabetes) as an example to determine the associations between various diseases and their comorbidities. The outcomes of this research demonstrated that the strength of some interesting and or clinically relevant disease associations (Example: musculoskeletal disorder and endocrine disorder, nervous disorder and endocrine disorder) is stronger in male patients than in female patients. Also, observed that the endocrine-specific comorbidity patterns will vary based on the race and gender of a patient. Finally, extracted comorbidities patterns are validated with medical literature as well as with the state of art method. Notably, this study exploits the network-level relationships between diseases, which will improve our perception about disease associations at the level of the patient population. © 2023 IEEE.

关键词： Statistical methods

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Anomaly Detection from Web Log Data Using Machine Learning Model 7

Anomaly Detection from Web Log Data Using Machine Learning M...

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7th International Conference on computer Applications in Electrical engineering-Recent Advances, CERA 2023

作者： Mishra, Amit Kumar Bagla, Piyush Sharma, Ravi Pandey, Neeraj Kumar Tripathi, Neha Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India National Institute of Technology Department of Computer Science & Engineering Jalandhar India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305005

The information in the logs produced by the servers, devices, and applications can be utilized to assess the system's health. It's crucial to manually review logs, for instance, during upgrades, to verify whether the update and data movement went smoothly. Manual testing is insufficiently trustworthy, and manual log examination takes much time and effort. In this paper, we propose to search log files for anomalous sequences using the machine learning methods K-Means and DBSCAN. The two data representation approaches examined in this study were feature vector representation and IDF representation. The effectiveness of the deployed machine learning algorithms was examined using evaluation measures like F1 score, recall, and precision. The study found considerable differences in the algorithms' capacities to spot anomalies, with some algorithms being better at seeing various types of abnormal arrangements than their overall prevalence. By using the study's findings, the user might be able to spot strange arrangements after manually sifting through the log file. © 2023 IEEE.

关键词： Anomaly detection

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Energy and Time-Optimized Task Scheduling with Simulated-Annealing-Based Firefly Algorithm in Hybrid Cloud Edge Computing

Energy and Time-Optimized Task Scheduling with Simulated-Ann...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Zhou, Xinmin Yuan, Haitao Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

In a cloud-edge system, data analysis, processing, and storage can be performed in edge servers, avoiding transferring data to more distant cloud servers. This greatly improves the efficiency of data processing, saves network bandwidth and cloud resources, and reduces operating and maintenance costs. However, it is a challenge of how to perform task scheduling. It is difficult to schedule tasks for joint optimization of the total energy consumption and completion time of a task sequence within a limited time in a resource-constrained cloud-edge system. The work proposes an improved Simulated-Annealing-based Firefly Algorithm with Linear position update, called SAFAL for short. SAFAL incorporates a simulated annealing mechanism and an efficient position update strategy into the firefly algorithm, enabling fireflies to find the optimal solution more quickly and avoid getting trapped in local optima. SAFAL adopts a probabilistic mapping operator to map the position of each firefly to a task scheduling sequence, thus linking the firefly space and the task space. Several test instances in cloud-edge systems are designed to validate the superiority of SAFAL over the firefly algorithm, simulated annealing, and firefly algorithm with a self-adaptive strategy. Results show that the weighted cost of total energy consumption and completion time of SAFAL is reduced by 16.32%, 17.62%, and 14.21%, respectively, with 20 tasks. © 2024 IEEE.

关键词： Simulated annealing

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Multi-User Computation Offloading in Mobile Edge Computing with Hybrid Whale Optimization

Multi-User Computation Offloading in Mobile Edge Computing w...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bil, Jing Li, Ning Yuan, Haitao Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

With the increasing amount of data and the need for real-time processing, Mobile Edge Computing (MEC) is growing rapidly, driving the shift from traditional cloud computing to distributed edge architectures. When offloading these applications with large amounts of data on mobile devices, a lot of computing and storage resources and high energy consumption are required. Yet, mobile devices' computing power, resource storage, and battery power are often limited and cannot meet these needs. To solve a computation offloading problem for joint optimization of time, cost, and energy, this work proposes an improved hybrid algorithm called Chaos and Lévy flights-based Whale Optimization Algorithm (CLWOA) to solve the multi-user offloading problem in an MEC-Cloud system. Each task is offloaded to local processors of mobile devices, edge servers, and cloud servers in proportion to jointly minimize the completion time, energy consumption, and total cost. Finally, compared with the whale optimization algorithm, lévy flight whale optimization algorithm, refined whale optimization algorithm, and chaos-based whale optimization algorithm, CLWOA reduces the weighted cost by 1.89%, 0.31%, 0.19%, and 0.42%, respectively. © 2024 IEEE.

关键词： Mobile edge computing

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An Evolutionary Framework with Improved Variance-Stabilized Multi-Objective Proximal Policy Optimization and NSGA-II

An Evolutionary Framework with Improved Variance-Stabilized ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Yue, Caiheng Yuan, Haitao Zhai, Jiahui Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Multi-objective optimization algorithms are essential for addressing real-world challenges characterized by conflicting objectives. Although conventional algorithms are effective in exploring solution spaces and generating non-dominated solutions, solution quality and dynamic adaptability of true Pareto fronts need to be improved. This work proposes a multi-objective algorithm that integrates Non-dominated sorting genetic algorithm II (NSGA-II) and Multi-Objective Reinforcement Learning (N-MORL). N-MORL consists of two parts including upstream and downstream components. In the upstream component, this work improves the Variance-stabilized Multi-objective Proximal Policy Optimization (VMPPO) for enhanced convergence stability by adjusting its iteration mechanism. Additionally, this work optimizes variance networks and action sampling to balance exploration and exploitation, which improves experience sampling efficiency. This work adopts high-quality solution sets yielded by MORL as the initial solution set for downstream NSGA-II, guiding the exploration space and increasing the solution number. High-quality initial solutions significantly accelerate the iterative convergence speed of N-MORL. N-MORL provides the quality and the number of solutions, better covering or approaching the true Pareto front. Experimental results with five benchmark multi-objective functions demonstrate that N-MORL outperforms the other three multi-objective evolutionary algorithms regarding high-quality solutions with the same iterations. © 2024 IEEE.

关键词： Genetic algorithms

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