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Diving into Brain Complexity: Exploring Functional and Effective Connectivity Networks 18

Diving into Brain Complexity: Exploring Functional and Effec...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Siddique, Abu Bakar Khalid, Hassan Bin Ali, Raja Hashim University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan

ISBN: (纸本)9798350328172

The human brain's intricate functions are under-pinned by a vast network of synapses that enable chemical impulses between neurons. Neuroscientists employ two key approaches, functional and effective connectivity, to understand the brain's complexity, focusing on its operations, cognition, and behavior. While both methods utilize graph theory for network analysis, functional connectivity, which investigates associated brain activity, has seen more substantial research efforts than effective connectivity, which examines information processing's inter-regional impacts. In this research paper, we aim to present an extensive examination of the emerging discipline that combines graph theory with the analysis of brain characteristics, shedding light on how these characteristics arise from the interactions among different groups of neurons. Our primary focus lies in exploring the diverse cognitive and neurological applications that leverage functional Magnetic Resonance Imaging (fMRI) as a tool. Additionally, we offer a comprehensive overview of the methods employed to construct brain networks based on functional and efficient connections. Throughout the discussion, we emphasize the advantages and limitations associated with these approaches. © 2023 IEEE.

关键词： Graph theory

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A Preliminary Result of Food Object Detection using Swin Transformer 22

A Preliminary Result of Food Object Detection using Swin Tra...

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8th International Conference on computer Technology Applications, ICCTA 2022

作者： Jung, Daeil Shim, Simon Choo, Charles Hwang, Doosung Nah, Yunmook Oh, Sejong Dept. of Computer Science Dankook University Korea Republic of Dept. of Computer Engineering San Jose State University United States Dept. of Electrical Engineering San Jose State University United States Dept. of Software Science Dankook University Korea Republic of Dept. of Applied Computer Engineering Dankook University Korea Republic of

ISBN: (纸本)9781450396226

An inappropriate diet is one of the main causes of poor health. However, it is difficult to sustain a quantitative diet assessment in the general living environment. Food object detection is a key method for solving this problem;still, it is difficult to find studies that apply recent object detection techniques. In addition, the currently used high-performance food object detection models have a special architecture that combines two deep learning models-food localization and food classification-in series to achieve high accuracy. The disadvantage of this architecture is that it is difficult to predict the scalability of a model. In this study, we built an end-to-end food object detection model using the Swin Transformer, which is one of the latest backbone models. The experiment was conducted to compare the performance of the UECFOOD dataset with other food object detection studies. For the UECFOOD-100 dataset, a mAP(mean Average Precision) of 0.522 was obtained;also, a mAP of 0.52 was obtained for the UECFOOD-256 dataset. The findings show that the proposed model that uses only end-to-end object detection produces better performance than previous studies using a combination of food localization and food classification. © 2022 ACM.

关键词： Object detection

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Frameworks, Applications and Challenges in Streaming Big Data Analytics: A Review 3

Frameworks, Applications and Challenges in Streaming Big Dat...

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3rd International Conference on Innovations in computer Science and software engineering, ICONICS 2022

作者： Nabeel Mustafa, Syed Muhammd Umer Farooque, Muhammad Tahir, Muhammad Khan, Shariq Mahmood Qamar, Rohail NED University of Engineering and Technology Dept. of Computer Science and I.T Karachi Pakistan Sir Syed University of Engineering and Karachi Dept. of Software Engineering Pakistan

ISBN: (纸本)9781665461412

Data output has increased dramatically in the twenty-first century. The speed at which data is introduced into the stream has increased as a result of the digitization of practically all industries. Big data refers to this enormous amount of data. A complex network of data with volume, velocity, diversity, authenticity, and value makes up this information. To derive useful insights from the data, it became necessary to examine the data. The term "streaming Big data"refers to data that changes very quickly. Big data analytics that are live or streaming have significantly improved analytics. Instantaneous analytics of the real-Time data are provided by streaming big data analytics, assisting the decision-makers. Big data analytics in real-Time has taken center stage in business. We thoroughly examine the phenomenon of streaming Big data analytics in this review study. We also look at the various streaming analytics frameworks in use. Additionally, we look into the fields in which streaming Big Data analytics are applied as well as the difficulties encountered. © 2022 IEEE.

关键词： Big data

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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GAN vs. LSTM: Network State Prediction 17

GAN vs. LSTM: Network State Prediction

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Byun, Gyurin Raza, Syed M. Yang, Huigyu Kim, Moonseong Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Seoul Theological University Dept. of It Convergence Software Bucheon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Network Traffic prediction is the prerequisite for proactive traffic management, where a longer duration and high accuracy of prediction ensures a more effective solution. This paper exploits Generative Adversarial Network (GAN) architecture to propose a model that utilizes spatiotemporal features in network traffic data for extended prediction of future traffic. The generator in GAN consists of a Convolutional Long Short-Term Memory (ConvLSTM) model for predicting network traffic, while the discriminator uses Convolutional Neural Network (CNN) model that provides feedback to the generator for training. The experiments based on network traces show that the proposed model reduces the error by 12% compared to the baseline model while predicting next 48 mins of network traffic. © 2023 IEEE.

关键词： Generative adversarial networks

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Fine-Tuning Vision Transformer for Arabic Sign Language Video Recognition on Augmented Small-Scale Dataset

Fine-Tuning Vision Transformer for Arabic Sign Language Vide...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gochoo, Munkhjargal Batnasan, Ganzorig Ahmed, Ahmed Abdelhadi Otgonbold, Munkh-Erdene Alnajjar, Fady Shih, Timothy K. Tan, Tan-Hsu Wee, Lai Khin United Arab Emirates University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of EECS National Central University Taoyuan Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan University of Malaya Dept. of Biomedical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350337020

With the rise of AI, the recognition of Sign Language (SL) through sign-to-text has gained significance in the field of computer vision and deep machine learning. However, there are only a few medium to large open datasets available for this task, as it requires a vast dataset of thousands of signs for words/phrases in different environments, which is a time-consuming and tedious process. Furthermore, there has been very little effort towards Arabic Sign Language Recognition (ArSLR). This research paper presents the results of fine-tuning the Vision Transformer (ViT) model on a small-scale in-house dataset of ArSL. The main goal is to attain satisfactory results by utilizing minimal computing power and a small dataset involving less than 10 individuals, with only one recording made for each sign in every environment. The dataset comprises 49 classes/signs, all of which were made with two hands and belong to the Level I category in terms of popularity. To enhance the dataset, three types of augmentations - translation, shear, and rotation were employed. The ViT model, pre-trained on the Kinetics dataset, was trained on the variation of augmented datasets with 2 to 40 times samples for each original video, where the training set includes original and augmented videos of 8 volunteers and the test set includes only original videos of one particular volunteer. Experimental results reveal that the combination of rotation and shear outperformed the others, achieving an accuracy of 93% on the 20 times augmented samples per class per signer dataset. We believe this study sheds light on small-scale dataset-based SLR tasks and video/action recognition in general. © 2023 IEEE.

关键词： Arabic Sign Language Augmentation Deep Learning Smale-scale dataset Vision Transformer ViT

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting 17

An Image Analysis-Based Automated Method using Deep Learning...

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17th IEEE International Conference on Industrial and Information Systems, ICIIS 2023

作者： Ajikaran, Ramesh Hewarathna, Ashen Iranga Palanisamy, Vigneshwaran Joseph, Charles Thuseethan, Selvarajah Sabaragamuwa University of Sri Lanka Dept. of Computing and Infromation Systems Sri Lanka Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sri Lanka Fau Dept. of Computer Science Germany School of Information Technology Deakin University Australia

ISBN: (纸本)9798350323634

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst. © 2023 IEEE.

关键词： Grain (agricultural product)

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Visualizing the Path of a Photo Taker from Image Metadata 4

Visualizing the Path of a Photo Taker from Image Metadata

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4th International Informatics and software engineering Conference, IISEC 2023

作者： Majid, Rasty Shakhawan Varol, Cihan Varol, Asaf Nasab, Ahad Firat University Department of Software Engineering Erbil Iraq Sam Houston State University Computer Science Department HuntsvilleTX United States College of Engineering and Computer Science Dept. of Engineering Management and Technology ChattanoogaTN United States

ISBN: (纸本)9798350318036

One of the most challenging investigations in forensic analysis is the work conducted on smartphones. Specifically using smartphone data to provide evidence for a case is becoming a norm nowadays. Many Android device applications use and work with location services that assist investigators in working with log files that store valuable information. Images captured on Android devices will save geolocation information, date, and time in the properties. In this work, a tool is developed to acquire the date and geolocation for each taken photo so that the path of the photo taker can be displayed on Google Maps. The work showed that the application can take about 4 and a half seconds to process four images and display the map to the investigator. © 2023 IEEE.

关键词： Metadata

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Leveraging a Hierarchical Selection of Communities in Social Networks to Maximize Diffusion 9

Leveraging a Hierarchical Selection of Communities in Social...

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9th International Conference on Web Research, ICWR 2023

作者： Heidari, Mohsen Fasahat, Mohamad Ali Afsharian, Sanaz Artishdar, Parinaz Esfarayen University Dept. of Electrical Computer and Industrial Engineering North Khorasan Iran Mehr Alborz Higher Education Institute Dept. of Computer Science Tehran Iran Islamic Azad University Damavand Branch English Department Tehran Iran Islamic Azad University Yadegar Emam Khomeini Branch Dept. of Software Engineering Tehran Iran

ISBN: (纸本)9798350399691

For businesses, discovering key nodes in social networks has a high computational overhead and cost. Because of this, in the problem of influence maximization, at least key nodes with the most influence in social networks are chosen at the lowest possible cost. Numerous algorithms have been proposed to find key nodes in social networks using the technique of influence maximization under the characteristics of submodularity and monotonicity;however, these algorithms struggle with the issue of optimal influence spread because some graph characteristics are ignored when considering diffusion and influence spread in the social network. The HSMD method, which gives special weight to the structure of communities and selects key nodes hierarchically utilizing the structure of communities, has been presented as a solution to this challenge. Based on the characteristics and strategic placement of the nodes within each community, weight is given to each community in this algorithm. Seed nodes are then chosen hierarchically using BFS traversal. This method enhanced the influence spread rate and execution time of recently introduced algorithms such as CTIM, IMBC, RNR, HEDVGreedy, and k-shell. © 2023 IEEE.

关键词： Diffusion

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AASH: A Lightweight and Efficient Static IoT Malware Detection Technique at Source Code Level 3

AASH: A Lightweight and Efficient Static IoT Malware Detecti...

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3rd Asia Conference on computers and Communications, ACCC 2022

作者： Glani, Yasir Ping, Luo Shah, Syed Asad Software School Tsinghua University Beijing China Dept. of Computer Engineering Bilkent University Ankara Turkey

ISBN: (纸本)9798350332957

IoT malware applications significantly threaten user privacy and security. Traditionally, IoT developers have focused primarily on hardware, but connectivity requires additional embedded software, usually developed by third-party developers. Unfortunately, third-party code is not always secure and trustworthy, and it frequently contains bugs and malicious code, which leaves IoT devices vulnerable. We propose the AASH technique (IoT Malware Detection) a novel technique that can detect malware at the source code level using the Adler-32 hash function and Fibonacci search. Previously, DROIDMD technique and SQVDT technique have been proposed to detect malware on Android and Linux devices. According to the authors, their schemes are scalable and can be deployed on IoT devices. However, their technique suffers from lower accuracy and takes longer to detect malicious code. The performance measurement shows that our proposed AASH technique is comparatively better than DROIDMD and SQVDT techniques in terms of accuracy and malware detection. AASH is reliable, efficient, and can be deployed on a large-scale level. © 2022 IEEE.

关键词： Malware

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