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arXiv 2024年

作者： He, Lang Chen, Kai Zhao, Junnan Wang, Yimeng Pei, Ercheng Chen, Haifeng Jiang, Jiewei Zhang, Shiqing Zhang, Jie Wang, Zhongmin He, Tao Tiwari, Prayag The School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China The Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China The Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China The College of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Xi’an710016 China The Institute of Intelligent Information Processing Taizhou University Zhejiang Taizhou318000 China The School of Education Shenzhen University Shenzhen518060 China The School of Information Technology Halmstad University Halmstad Sweden

Depression can significantly impact many aspects of an individual’s life, including their personal and social functioning, academic and work performance, and overall quality of life. Many researchers within the field of affective computing are adopting deep learning technology to explore potential patterns related to the detection of depression. However, because of subjects’ privacy protection concerns, that data in this area is still scarce, presenting a challenge for the deep discriminative models used in detecting depression. To navigate these obstacles, a large-scale multimodal vlog dataset (LMVD), for depression recognition in the wild is built. In LMVD, which has 1823 samples with 214 hours of the 1475 participants captured from four multimedia platforms (Sina Weibo, Bilibili, Tiktok, and YouTube). A novel architecture termed MDDformer to learn the non-verbal behaviors of individuals is proposed. Extensive validations are performed on the LMVD dataset, demonstrating superior performance for depression detection. We anticipate that the LMVD will contribute a valuable function to the depression detection community. The data and code will released at the link: https://***/helang818/LMVD/. © 2024, CC BY-NC-ND.

关键词： Deep learning

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Social engineering attack types and prevention techniques- A survey

Social engineering attack types and prevention techniques- A...

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Computational intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Shruti Bhatla Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Turkey Department of Computer Science and Engineering Graphic Era Hill University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) Greater Noida India

The term "Cyber Security" is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim’s social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article.

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A Model Transformation Approach for Converting a Trait-based Model to a Markov Chain Model

A Model Transformation Approach for Converting a Trait-based...

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Recent Advances and Innovations in engineering (ICRAIE)

作者： Nur Diana Madinah Binti Ab Hadi Azlan Ismail Norizan Mat Diah Mastura Md Saad School of IT Faculty of Engineering Built Environment & Information Technology SEGI University & Colleges Petaling Jaya Selangor Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA (UiTM) Shah Alam Selangor Malaysia Informatics and Mathematics Institute for Big Data Analytics and Artificial Intelligence (IBDAAI)) University Teknologi MARA (UiTM) Shah Alam Selangor Malaysia

This paper discusses the difficulty of transforming a trait-based model to a Markov Chain Model when the source model is a behavioral model. Therefore, we propose an innovative approach aimed at transforming trait-based models into Markov Chain Models. Our method hinges on the utilization of the Trait Component Ensemble Language (TCOEL) and the establishment of a unified model transformation specification at the meta-model level. Through this approach, we facilitate the seamless conversion of trait-based models into Markov Chain Models, thereby enhancing the adaptability and scope of the original models. The trait-based model presents quite challenging for transforming the behavioral traits directly into verification language. To overcome this difficulty, we analyzed the language behaviors using the fire brigade case study. Then, we used the description to identify and map the appropriate component in order to develop a CTMC model. Finally, we used PRISM model checker to undertake verification.

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Design of a Fuzzy Type-2 Controller for 1-DOF Pitch Axis Twin Rotor System

Design of a Fuzzy Type-2 Controller for 1-DOF Pitch Axis Twi...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Abdelrahman Sayed Sayed Walid K. Shaker Hossam Hassan Ammar Ahmad Taher Azar Chakib Ben Njima Department of Marine Technology Norwegian University of Science and Technology (NTNU) Trondheim Norway Université de Toulon Toulon France School of Engineering and Applied Science Nile University Giza Egypt Hosted by Global Academic Foundation new administrative capital School of Engineering and Computer Sciences University of Hertfordshire Egypt College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial intelligence Benha University Benha Egypt University of Sousse Tunisia

This paper presents the design, manufacturing, modeling, and control of a novel 1-DOF pitch axis Twin Rotor system. An IMU sensor is used to precisely access the desired pitch angle of the system. A type-2 fuzzy controller is designed and implemented to achieve fast rise time and low overshoot, which are the desired response specifications for the system. In terms of overshoot and settling time, the performance of the type-2 fuzzy-PID controller is compared to that of the type-l fuzzy-PID controller and the conventional PID controller. The type-2 fuzzy-PID controller's performance is promising when compared to that of the other controllers, proving that it is useful for taming the Twin Rotor system's non-linearities.

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Short-term load forecasting of building electricity consumption using NARX neural networks model 6

Short-term load forecasting of building electricity consumpt...

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6th International Conference on Smart and Sustainable Technologies, SpliTech 2021

作者： Zuazo, Irati Zapirain Boussaada, Zina Aginako, Naiara Curea, Octavian Camblong, Haritza Sierra, Basilio Faculty of Engeneering of Gipuzkoa Department of Systems Engineering and Control Plaza de Europa 1 Donostia-San Sebastián20018 Spain Univ. Bordeaux ESTIA INSTITUTE of TECHNOLOGY BidartF-64210 France Faculty of Engeneering of Gipuzkoa Department of Computer Science and Artificial Intelligence Plaza de Europa 1 Donostia-San Sebastián20018 Spain

ISBN: (纸本)9789532901122

Electric grid, as we nowadays know it, is undergoing a significant transformation. What we are now witnessing is an undoubted change of trend towards a decentralized and decarbonized electric grid, where the electric generation based on local resources will take on special relevance. In this context, the encouragement of collective self-consumption becomes one of the key issues when it comes to taking steps forward to this end. One of the aspects that will contribute to this aim is the development of a consumption-forecasting tool. Hence, a load-forecasting model based on NARX Neural Network is proposed in the following paper. The prediction of the next day (24h) load profile of an individual building is carried out aiming an optimal management of the flexible loads so to achieve the maximum self-consumption. To ensure a consistent behavior of the NARX Neural Network model, identification and removing of outliers, together with data normalization and fixing common time interval has been carried out. The first results of the research are promising, being obtained a 17, 6% MAPE in NARX and 25, 19% with LSTM model, both evaluated during a regular week on winter in adverse conditions. © 2021 University of Split, FESB.

关键词： Distributed power generation

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Dynamic Patient Triage Optimization in Healthcare Settings Using RNNs for Decision Support

Dynamic Patient Triage Optimization in Healthcare Settings U...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Chitra Sabapathy Ranganathan Chethan Chandra S Basavaraddi V. Saillaja Pramod Pandey B. Sundaramurthy S. Murugan Mphasis Corporation Chennai Tamil Nadu India Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology VTU Belagavi Karnataka India Faculty of Management Studies SRM Institute of Science and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

The article presents a sophisticated healthcare system that uses recurrent neural networks (RNNs) to optimize real-time patient triage. The developed model integrates patient data from the Internet of Things (IoT), and it performs dynamic assessments of vital indicators like heart rate, blood pressure, and temperature to prioritize pre-operation care based on the urgency and severity of diseases. The RNN architecture considers the temporal connections included in the data, which enables a more sophisticated comprehension of the changing patient states. The training and evaluation of the model make use of a large dataset. In comparison to more conventional triage methods, the model displays considerable gains in both accuracy and efficiency. The system not only reduces the amount of time needed to respond and allocate resources, but it also improves the flexibility to react to shifting patient conditions. This research represents a significant step toward the development of intelligent decision support systems in the healthcare industry. It demonstrates the potential of more sophisticated machine learning approaches to transform patient care processes in environments with high stakes and a dynamic nature.

关键词： Industries Training Temperature distribution Accuracy Recurrent neural networks Medical services Transforms Real-time systems data models Internet of Things

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Multimodal brain tumor segmentation and classification from MRI scans based on optimized DeepLabV3+ and interpreted networks information fusion empowered with explainable AI

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Computers in Biology and Medicine 2024年 182卷 109183-109183页

作者： Ullah, Muhammad Sami Khan, Muhammad Attique Albarakati, Hussain Mubarak Damaševičius, Robertas Alsenan, Shrooq Department of Computer Science HITEC University Taxila47080 Pakistan Department of Artificial Intelligence College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Computer and Network Engineering Department College of Computing Umm Al-Qura University Makkah24382 Saudi Arabia Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Information Systems Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Explainable artificial intelligence (XAI) aims to offer machine learning (ML) methods that enable people to comprehend, properly trust, and create more explainable models. In medical imaging, XAI has been adopted to interpret deep learning black box models to demonstrate the trustworthiness of machine decisions and predictions. In this work, we proposed a deep learning and explainable AI-based framework for segmenting and classifying brain tumors. The proposed framework consists of two parts. The first part, encoder-decoder-based DeepLabv3+ architecture, is implemented with Bayesian Optimization (BO) based hyperparameter initialization. The different scales are performed, and features are extracted through the Atrous Spatial Pyramid Pooling (ASPP) technique. The extracted features are passed to the output layer for tumor segmentation. In the second part of the proposed framework, two customized models have been proposed named Inverted Residual Bottleneck 96 layers (IRB-96) and Inverted Residual Bottleneck Self-Attention (IRB-Self). Both models are trained on the selected brain tumor datasets and extracted features from the global average pooling and self-attention layers. Features are fused using a serial approach, and classification is performed. The BO-based hyperparameters optimization of the neural network classifiers is performed and the classification results have been optimized. An XAI method named LIME is implemented to check the interpretability of the proposed models. The experimental process of the proposed framework was performed on the Figshare dataset, and an average segmentation accuracy of 92.68 % and classification accuracy of 95.42 % were obtained, respectively. Compared with state-of-the-art techniques, the proposed framework shows improved accuracy. © 2024 Elsevier Ltd

关键词： data fusion

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Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios

arXiv

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arXiv 2024年

作者： Masri, Sari Ashqar, Huthaifa I. Elhenawy, Mohammed Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University 500 W 120th St New YorkNY10027 United States CARRS-Q Centre for Data Science Queensland University of Technology 130 Victoria Park Rd Kelvin GroveQLD4059 Australia

Urban traffic management faces significant challenges due to the dynamic environments, and traditional algorithms fail to quickly adapt to this environment in real-time and predict possible conflicts. This study explores the ability of a Large Language Model (LLM), specifically, GPT-4o-mini to improve traffic management at urban intersections. We recruited GPT-4o-mini to analyze, predict position, detect and resolve the conflicts at an intersection in real-time for various basic scenarios. The key findings of this study to investigate whether LLMs can logically reason and understand the scenarios to enhance the traffic efficiency and safety by providing real-time analysis. The study highlights the potential of LLMs in urban traffic management creating more intelligent and more adaptive systems. Results showed the GPT-4o-mini was effectively able to detect and resolve conflicts in heavy traffic, congestion, and mixed-speed conditions. The complex scenario of multiple intersections with obstacles and pedestrians saw successful conflict management as well. Results show that the integration of LLMs promises to improve the effectiveness of traffic control for safer and more efficient urban intersection management. © 2024, CC BY.

关键词： Traffic congestion

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A Monetary Incentive DTN Routing for Wireless Sensor Networks

Research Square

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Research Square 2021年

作者： Lin, Zhi Wang, Xia Bie, Rongfang Shi, Hongwei School of Artificial Intelligence Beijing Normal University Beijing China School of Statistics and Data Science Faculty of Science Beijing University of Technology Beijing China Center of Information & Network Technology Beijing Normal University Beijing China

The DTN (Delay/Interrupt Tolerant Network) protocol that relies on nodes to handle network interruptions is one of the important components of the wireless sensor network (WSN) routing protocol. However, due to resource consumption, nodes may be unable to unconditionally relay data. To address this issue, several incentive mechanisms have recently been proposed to encourage node participation. However, the existing solutions either do not fully consider the sender’s budget or do not consider the relay cost limitation, which violated the practical incentive mechanism requirements of the DTN protocol. In this paper, we focus on developing a new incentive mechanism for DTN routing that specifically address the challenges brought up by budget and relay cost limitation. Our core idea is to define the payoff functions of the sender and the next hops, then optimize the strategies under the constraints of the sender’s budget and the relay’s cost. Our experimental results demonstrate that the maximized social welfare for all participants can be realized under these constraints. © 2021, CC BY.

关键词： Network routing

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