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作者： Tezgider, Murat Yildiz, Beytullah Aydin, Galip Department of Computer Engineering Faculty of Engineering Firat University Elazig Turkey Department of Software Engineering School of Engineering Atilim University İncek Ankara Turkey

Text data have an important place in our daily life. A huge amount of text data is generated everyday. As a result, automation becomes necessary to handle these large text data. Recently, we are witnessing important developments with the adaptation of new approaches in text processing. Attention mechanisms and transformers are emerging as methods with significant potential for text processing. In this study, we introduced a bidirectional transformer (BiTransformer) constructed using two transformer encoder blocks that utilize bidirectional position encoding to take into account the forward and backward position information of text data. We also created models to evaluate the contribution of attention mechanisms to the classification process. Four models, including long short term memory, attention, transformer, and BiTransformer, were used to conduct experiments on a large Turkish text dataset consisting of 30 categories. The effect of using pretrained embedding on models was also investigated. Experimental results show that the classification models using transformer and attention give promising results compared with classical deep learning models. We observed that the BiTransformer we proposed showed superior performance in text classification. © 2021 John Wiley & Sons, Ltd.

关键词： Text processing

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Dynamic knowledge graph completion through time-aware relational message passing 28

Dynamic knowledge graph completion through time-aware relati...

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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Baqinejadqazvini, Amirhossein Tahery, Saedeh Farzi, Saeed K. N. Toosi University of Technology Faculty of Computer Engineering Department of Software Engineering Tehran Iran K. N. Toosi University of Technology Faculty of Computer Engineering Department of Artificial Intelligence Tehran Iran Tehran Iran

ISBN: (纸本)9798350338195

As the structure of knowledge graphs may vary over time, static knowledge graph completion methods do not deal with time-varying knowledge graphs. However, examining the paths between entities and entities' context information can lead to more accurate completion methods. This paper attempts to complete dynamic (time-varying) knowledge graphs by combining time-aware relational paths and relational context. The proposed model can improve dynamic knowledge graph completion methods by leveraging neural networks. Experimental results conducted on two standard datasets, ICEWS14 and ICEWS05-15, indicate our model's superiority in terms of Mean Reciprocal Rank (MRR) and Hit@k over its well-known counterparts, such as DE-TransE and DE-DistMult. © 2023 IEEE.

关键词： Knowledge graph

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Real-Time CNN-Based Driver Distraction&Drowsiness Detection System

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Intelligent Automation & Soft Computing 2023年第8期37卷 2153-2174页

作者： Abdulwahab Ali Almazroi Mohammed A.Alqarni Nida Aslam Rizwan Ali Shah University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia University of Jeddah College of Computer Science and EngineeringDepartment of Software EngineeringJeddahSaudi Arabia Department of Computer Science National College of Business Administration&EconomicsBahawalpur Campus63100Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjab64200Pakistan

Nowadays days,the chief grounds of automobile accidents are driver fatigue and *** the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing *** paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor *** the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is *** of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement *** suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.

关键词： Deep learning convolutional neural network Tensorflow drowsiness and yawn detection seat belt detection object detection

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Boosting the Prediction of Brain Tumor Using Two Stage BiGait Architecture 25

Boosting the Prediction of Brain Tumor Using Two Stage BiGai...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Khan, Saif-Ur-Rehman Khan, Zia Hossain, Md Zakir Mayumu, Nicanor Yasmin, Farhana Aziz, Younas School of Computer Science and Engineering Central South University Changsha China Curtin University Perth Australia Nanjing University of information Science and Technology Department of Computer and Software Nanjing China

ISBN: (纸本)9798350379037

Deep learning (DL) techniques hold immense promise for revolutionizing medical diagnostics, including brain tumor detection. Detecting malignancies in the brain is fraught with challenges that carry critical implications for accurate and timely diagnosis. Early detection is paramount, and our paper presents a novel approach to address this issue. We introduce the BiGait ensemble transfer learning (TL) architecture tailored for brain tumor classification tasks. Our proposed methodology is a two-stage process that harnesses the power of TL. In the initial phase, we employ two established models, ResNet50 and EfficientNetB1, and fine-tune them using the Kaggle multiclass dataset. This includes the incorporation of supplementary feature layers. We also leverage a stack ensemble solution to combine these feature layers, enhancing the model's overall predictive capabilities. Our approach is designed to shift the input distribution positively, effectively mitigating overfitting and reducing hardware overhead. To validate the effectiveness of our method, we conducted extensive experiments and evaluation. The dataset utilized in this work, publicly available on Kaggle contains 3,264 brain MRIs categorized into four groups: normal (500 images), meningioma (937 images), glioma (926 images), and pituitary tumor (901 images). The experimental results demonstrate that our model surpasses traditional single-model approaches, achieving an impressive accuracy rate of 96% and exhibiting superior generalization capabilities. This advancement in brain tumor classification holds great promise for early diagnosis and improved patient outcomes. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Money Laundering Detection and Mitigation Using Decision Tree Ensembles

Money Laundering Detection and Mitigation Using Decision Tre...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Ebenezer, Adeoti Babajide Olutayo, Boyinbode Olumide O, Obe Akintola, K.G. Emmanuel, Mgbeahuruike Oluwayemisi Boye, Fatade Babcock University Software Engineering Department Ogun state Ilisan Remo Nigeria Federal University of Technology Akure Department of Computer Science Ondo state Akure Nigeria

ISBN: (纸本)9798350376838

Money laundering is an unlawful activity that has caused underdevelopment to Africa, it's a ravaging vices that cripples economic growth, increases criminality and leads to economic sabotage. Many works previously done on money laundering detection, it was observed that it takes times before fraudulent transactions can be detected, it is also less effective, meaning that is can easily be circumvents and the data sampling methods used in previous researches makes the model to suffer from overfitting and loss of information, however this research adopts SMOTE (Synthetic Minority Oversampling Techniques) algorithm method to balance the dataset so as to have a fair representation of the two classes in order for machine learning model to accurately evaluate the models. The Objective of this research is to adapt and implement decision tree ensemble learning algorithms to detect money laundering activities, also design and implement an Anti Money Laundering (AML) query system to mitigate money laundering activities based on detection results and finally to carry out the performance evaluation of decision tree ensemble algorithms using standard performance metrics. © 2024 IEEE.

关键词： Laundering

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Synthetic Audio Data Generation Algorithm for the Diarization Problem

Synthetic Audio Data Generation Algorithm for the Diarizatio...

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2023 Wave Electronics and its Application in Information and Telecommunication Systems, WECONF 2023

作者： Zulkashev, Ruslan Polyak, Mark Saint-Petersburg State University of Aerospace Instrumentation Department of Computer Technologies and Software Engineering St. Petersburg Russia

ISBN: (纸本)9798350348293

In this paper an approach to building a dataset for the diarization problem is considered. A pipeline is proposed for fine-tuning a pretrained diarization model on synthetic data in order to increase diarization accuracy for Russian language. The suggested algorithm for processing audio data allows to add random portions of silence between adjacent audio segments, make several segments partially overlap and add specific type of noise to the audio track. Diarization error rate metric is calculated on the generated audio files by utilizaing Python's pyannote diarization model. The results show that the artificially generated synthetic audio data is adequate and closely resembles real audio tracks. The proposed method involves speech extraction, concatenation, normalization, audio track formation, and noise overlaying. Our experiments have shown that generating longer and more diverse conversations leads to better quality data. © 2023 IEEE.

关键词： Python

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A Novel Machine Learning Approach to Delay Efficient Offloading Strategy for Mobile Edge Computing 1st

A Novel Machine Learning Approach to Delay Efficient Offload...

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1st International Conference on Microelectronics, Communication Systems, Machine Learning, and Internet of Things, MCMI 2020

作者： Bharti, Piyush Chaudhary, Srishti Snigdh, Itu Department of Computer Science and Engineering Birla Institute of Technology Mesra India Software Development Microland Limited Bengaluru India

ISBN: (纸本)9789811919053

There have been studies on the problems experienced in offloading. But, how to leverage machine learning for resource-efficient computation offloading is not completely explored yet. We present this paper with the motivation to leverage machine learning and backward induction to suggest a method for the resource-efficient computation offloading in IoT devices. In particular, we use machine learning to predict the most efficient device to which a task should be offloaded through the use of a two-model machine learning system deployed in the edge cloud itself, since most high power IoT devices are fixed in their positions and display a certain pattern in their CPU utilization data. This is followed by using backward induction to determine the optimal execution location for each task component. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computation offloading

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Detection of Misinformation Related to Pandemic Diseases Using Machine Learning 7th

Detection of Misinformation Related to Pandemic Diseases Usi...

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7th EAI International Conference on Robotics and Networks, ROSENET 2023

作者： Naeem, Javaria Gül, Ömer Melih Parlak, Ismail Burak Karpouzis, Kostas Kadry, Seifedine Nimer Salman, Yücel Batu Department of Computer Engineering Bahçeşehir University İstanbul Turkey Department of Computer Engineering Galatasaray University İstanbul Turkey Panteion Uninversity of Social and Political Sciences Kallithea Greece Department of Applied Data Science Noroff College Kristiansand Norway Ajman University Ajman United Arab Emirates Department of Software Engineering Bahcesehir University İstanbul Turkey

ISBN: (纸本)9783031644948

The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies SVM, decision tree, logistic regression, and neural networks to perform in-depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. After applying ensemble learning, accuracy reached 91% for Facebook content, 79% for Instagram, 80% for Twitter, and 95% for YouTube. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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Implementation of Multi-Agent Energy Management for Secondary Control of a Microgrid

Implementation of Multi-Agent Energy Management for Secondar...

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2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023

作者： Yoldas, Yeliz Onen, Ahmet Alawasa, Khaled El Haffar, Abdelsalam Ahshan, Razzaqul Islam, Md. Rabiul Muyeen, S.M. Noorfatima, Nadya Jung, Jaesung Kayseri University Department of Software Engineering Kayseri Turkey Sultan Qaboos University Department of Electrical and Computer Engineering Muscat Oman University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW Australia Qatar University Electrical Engineering Department Doha Qatar Ajou University Department of Energy Systems Research Suwon Korea Republic of

ISBN: (纸本)9798350301571

The multi-agent energy management system (EMS) is needed to coordinate multiple agents of a microgrid to provide efficient operation of the system. In this study, multi-agent EMS is seen as the middle controller between primary and tertiary control levels. It is responsible for determining the relevant set points based on data gathered from the tertiary control level and sending them to the appropriate primary control level agents. The main objective of this paper is to propose a new optimization model for multi-agent EMS to minimize errors between the forecasted and real-time data at every predetermined time interval. Rather than using a traditional centralized microgrid controller to determine the dispatch of resources, a new framework-based multi-agent coordination is proposed to achieve the following objectives: minimizing load shedding, maximizing load dispatch and reliability, providing frequency, and maintaining voltage stability. © 2023 IEEE.

关键词： Dynamic energy management microgrid optimal control secondary control

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Earlier Selection of Routes for Data Transfer In Both Wired and Wireless Networks 1

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5th International Conference on Smart Technologies in Data Science and Communication, SMART-DSC 2022

作者： NagaMallik Raj, S. Neeraja, S. Rao, N. Thirupathi Bhattacharyya, Debnath Andhra Pradesh Visakhapatnam India Department of Computer Science & Software Engineering Lendi Institute of Engineering and Technology Jonnada Andhra Pradesh Vizianagaram India Department of Computer Science & Engineering Koneru Lakshmaiah Education Vaddeswa- ram Andhra Pradesh Guntur522502 India

ISBN: (数字)9789811968808

ISBN: (纸本)9789811968792

In both wired and wireless communication networks, the transfer of huge data was the major issue. To solve this issue, we are in the process of working with both admission control and control transfer mechanisms such that to transfer huge amount of data in the current networks without disturbing the regular flow of data in the networks. A scheduling algorithm was also in the process to achieve this goal of transfer of substance amount of data from source to desti nation securely. In the current system, for every transfer of bulk amounts, a request needs to be sent to the central processing control for the request of blocking of some bandwidth by giving the starting and ending time for the transfer of data. As the time slot is given, then we can transfer any amount of data within that slot of time. The reassignment of bandwidth and the multiple routing configurations are present and utilized in the system. As per the results, the working of the system is excellent in the performance, and good encouraging results were obtained from the system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Advance reservations Bandwidth allocation Bulk transfers Multiple routing Routing Time slot Wired networks Wireless networks

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