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2024 International Conference on engineering and Computing, ICECT 2024

作者： Ahsan, Zoha Ghumman, Shahwaiz Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9798350349719

Cricket is a popular sport worldwide, played with a bat and balls. This paper used classification and regression to predict the T20 and Test matches results and scores because cricket fans and analysts always want to predict which team will win the match and how much score a team will make. We have used a supervised machine learning technique to predict the score and result for T20 and Test matches. Cricket match prediction dataset comprising of 13 features and 7827 instances utilized for training the model. For the prediction of winning and losing, we have used Naïve Bayes, Gradient Boosted Trees, Logistic Regression, Deep learning, Decision Tree, Random Forest, and Generalized Linear Model. For Score Prediction, we have used Decision Trees, Random Forests, Gradient Boosted Trees, Generalized Linear Models, and Deep Learning. We've shown the most accurate model after evaluating the accuracy percentages of the several classifiers listed above. Our introduced model has gained an accuracy rate of 96.15% using the Gradient Boosted Trees classifier for Match Score Prediction and 71.72% accuracy by using the Generalized Linear Model for match result prediction. The Rapid Miner tool has been used to perform machine learning techniques to train models. This paper discusses the effectiveness and use of machine learning methods to develop highly accurate models for Cricket match prediction dataset comprising of 13 features and 7827 instances utilized for training the model. © 2024 IEEE.

关键词： Forecasting

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The Impact of Stuttering Event Representation on Detection Performance 2

The Impact of Stuttering Event Representation on Detection P...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Banna, Abedal-Kareem Fang, Hui Shubita, Ahmad F. Arafah, Mohammad Eqteeshat, Amani Abuaisheh, Saif B. University Of Petra Department Of Ai And Data Science Jordan Loughborough University Department Of Computer Science United Kingdom University Of Petra Department Of Software Engineering Jordan University Of Petra Department Of Computer Science Jordan Malaya University Department Of Information System Malaysia

ISBN: (纸本)9798350394962

Stuttering or stammering is a common neurogenic, psychogenic fluency disorder wherein people have uncontrolled blocks on the natural flow of speech. Automatic Stuttering Event Detection (SED) is a widely recognised challenge due to the heterogenic and overlapped nature of stuttering speech. Therefore, this comparative study investigates the impact of stuttering event representation on detection performance. The paper evaluates the performance of SED and shows the impact of employing ASR pre-trained feature extractions on each stuttering event. Moreover, the paper provides different groups of experiments that evaluate the impact of three acoustic features, the Zero Crossing Rate (ZCR), the Spectral Flux Onset Strength Envelope (SFO), and Fundamental Frequency (FF) features, on SED performance. The paper demonstrates that the fusion of spectral and ASR features improves SED performance across all stuttering events by 8%. The F1 score of the Block class increased by approximately 10%, from 15% to 25%, while the word repetition and interjection increased by 8%. In addition, our experiments proved that the fusion of temporal features increased the average F1 score of SED by 2% across all stuttering events. The F1 score of the prolongation and sound repetition classes increased by 6% and 3%, respectively. © 2024 IEEE.

关键词： Deep learning

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Analysis of Research Trends in Retransmission Techniques in Data Communications 15

Analysis of Research Trends in Retransmission Techniques in ...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Han, Deajin Na, Woongsoo Kongju National University Department of Computer Science and Engineering Cheonan Korea Republic of Kongju National University Department of Software Cheonan Korea Republic of

ISBN: (纸本)9798350364637

In data communications, data retransmission mechanisms are essential to ensure reliable data transmission. In this paper, we analyze various data retransmission algorithms and recent research trends. Traditional algorithms such as ACK and timeout-based retransmission, NACK-based retransmission, and selective repetition are reviewed, and modern retransmission techniques utilizing machine learning and network coding are discussed. These techniques contribute to increasing adaptability to the network environment and maximizing the efficiency of data transmission. This work highlights the importance of retransmission mechanisms in ensuring the reliability of data communication and provides directions for future research. © 2024 IEEE.

关键词： Data assimilation

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Federated Multiarmed Bandits Under Byzantine Attacks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1488-1501页

作者： Saday, Artun Demirel, Ilker Yildirim, Yigit Tekin, Cem Bilkent University Department of Electrical and Electronics Engineering Ankara06800 Turkey Massachusetts Institute of Technology Computer Science & Artificial Intelligence Laboratory CambridgeMA02139 United States

Multiarmed bandits (MAB) is a sequential decision-making model in which the learner controls the trade-off between exploration and exploitation to maximize its cumulative reward. Federated multiarmed bandits (FMAB) is an emerging framework where a cohort of learners with heterogeneous local models play an MAB game and communicate their aggregated feedback to a server to learn a globally optimal arm. Two key hurdles in FMAB are communication-efficient learning and resilience to adversarial attacks. To address these issues, we study the FMAB problem in the presence of Byzantine clients who can send false model updates threatening the learning process. We analyze the sample complexity and the regret of β-optimal arm identification. We borrow tools from robust statistics and propose a median-of-means (MoM)-based online algorithm, Fed-MoM-UCB, to cope with Byzantine clients. In particular, we show that if the Byzantine clients constitute less than half of the cohort, the cumulative regret with respect to \beta-optimal arms is bounded over time with high probability, showcasing both communication efficiency and Byzantine resilience. We analyze the interplay between the algorithm parameters, a discernibility margin, regret, communication cost, and the arms’ suboptimality gaps. We demonstrate Fed-MoM-UCB's effectiveness against the baselines in the presence of Byzantine attacks via experiments. © 2020 IEEE.

关键词： Federated learning

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A Process Oriented Integration Model for Smart Health Services

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Intelligent Automation & Soft Computing 2023年第2期35卷 1369-1386页

作者： Farzana Kausar Gondal Syed Khuram Shahzad Muhammad Arfan Jaffar Muhammad Waseem Iqbal Department of Computer Science Superior UniversityLahore54600Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Faculty of Computer Science&IT Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan

Cities are facing challenges of high rise in population number and con-sequently need to be equipped with latest smart services to provide luxuries of life to its *** integrated solutions are also a need to deal with the social and environmental challenges,caused by increasing ***,the development of smart services’integrated network,within a city,is facing the bar-riers including;less efﬁcient collection and sharing of data,along with inadequate collaboration of software and *** to resolve these issues,this paper recommended a solution for a synchronous functionality in the smart services’integration process through modeling *** this integration modeling solution,atﬁrst,the service participants,processes and tasks of smart services are identiﬁed and then standard illustrations are developed for the better understand-ing of the integrated service group *** process modeling and notation(BPMN)language based models are developed and discussed for a devised case study,to test and experiment i.e.,for remote healthcare from a smart *** research is concluded with the integration process model application for the required data sharing among different service *** outcomes of the modeling are better understanding and attaining maximum automation that can be referenced and replicated.

关键词： Process modeling business process business process integration business process modeling and notation

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Low-temperature metal–oxide thin-film transistor technologies for implementing flexible electronic circuits and systems

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Journal of Semiconductors 2023年第9期44卷 3-10页

作者： Runxiao Shi Tengteng Lei Zhihe Xia Man Wong State Key Laboratory of Advanced Displays and Optoelectronics and Technologies Department of Electronic and Computer EngineeringThe Hong Kong University of Science and TechnologyHong KongChina

Here we review two 300℃metal–oxide(MO)thin-film transistor(TFT)technologies for the implementation of flexible electronic circuits and ***-enhanced TFTs for suppressing the variation and shift of turn-on voltage(VON),and dual-gate TFTs for acquiring sensor signals and modulating VON have been deployed to improve the robustness and performance of the systems in which they are *** circuit building blocks based on fluorinated TFTs have been designed,fabricated,and characterized,which demonstrate the utility of the proposed low-temperature TFT technologies for implementing flexible electronic *** construction and characterization of an analog front-end system for the acquisition of bio-potential signals and an active-matrix sensor array for the acquisition of tactile images have been reported recently.

关键词： flexible electronics metal-oxide semiconductor thin-film transistor dual gate fluorination analog front-end system sensors

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Modeling belief-desire-intention reasoning agents for situation-aware formalisms

Modeling belief-desire-intention reasoning agents for situat...

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作者： Mahfooz Ul Haque, Hafiz Saleem, Kiran Salman Khan, Ahmad Department of Software Engineering University of Lahore Lahore Pakistan Department of Computer Science University of Lahore Lahore Pakistan

A natural disaster is an inevitable situation that can occur at any time and anywhere along with varied forms such as earthquakes, floods, hurricane, wildfire, and so forth, and different level of occurrences has been recorded from mild to an intense level. Timely disaster response plays an important role in reducing its deteriorate after-effects and can save countless lives. Over the years, people have been developing guidelines and processes to cope up with such kinds of hazardous situations. In recent years, with the advent of the pervasive computing paradigm, situation-awareness has been considered to be the most fascinating approach for situation assessment and provides decision support accordingly. Situation-aware systems observe/perceive dynamic changes in the environment, understand/comprehend the situation, and perform actions according to the environment. Although state-of-the-art formalisms have been developed to handle such kinds of hazardous situations intelligently and rescue the victims. However, belief-desire-intention (BDI) reasoning mechanism with the incorporation of situation-awareness is still the thirsty area of research to manage hazardous situations. In this article, we propose a temporal epistemic situation-aware formalism for BDI reasoning agents to model the context-aware decision support system. To demonstrate the work of the proposed formalism, we develop a case study based on a disaster situation, in which BDI agents are modeled and simulated to present the results in terms of agents' reasoning processes. We demonstrate the scalability in temporal aspects of the system using different levels of disasters to monitor the hazardous situations and evaluate the overall behavior of the system. © 2021 John Wiley & Sons Ltd.

关键词： Multi agent systems

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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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Classification of Human Physical Activities and Postures During Everyday Life 18

Classification of Human Physical Activities and Postures Dur...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Khan, Sheharyar Noorani, Sadam Hussain Arsalan, Aamir Mahmood, Awais Rauf, Usman Ali, Zohaib University of Engineering & Technology Department of Computer Engineering Taxila Pakistan Fatima Jinnah Women University Department of Software Engineering Rawalpindi Pakistan Szabist University Department of Computer Science Islamabad Pakistan Bahria University Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350328172

Nowadays Wearable sensor-based Human Activity Recognition (HAR) is gaining popularity for its affordability and low computational demands. These sensors are widely used in healthcare and surveillance. However, using smartphone sensors for HAR can be inaccurate due to their non-fixed position. This study uses the publicly available Human Activity Recognition Trondheim dataset (HARTH) to develop a HAR model. The proposed HAR model is capable to recognize diverse human daily life activities in the free-living environment. Identifying individual natural behaviour in the wild (free living) remains challenging because humans engage in unscripted daily activities. While working with controlled conditions (scripted data) can produce optimal results, this system often struggles when applied in real-life scenario. Multiple machine learning classifiers are tested on time domain features extracted from sensor data, with the Multilayer Perceptron (MLP) classifier achieving an impressive 92.92% accuracy, making a significant contribution to the HAR field. © 2023 IEEE.

关键词： Wearable sensors

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Ensemble based unsupervised anomaly detection with concept drift adaptation for time series data 10

Ensemble based unsupervised anomaly detection with concept d...

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10th IEEE Smart World Congress, SWC 2024

作者： Li, Danlei Nair, Nirmal Wang, Kevin I-Kai Sakurai, Kouichi University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Department of Informatics Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798331520861

Anomaly detection is the identification of instances that substantially deviate from the majority of the data and do not conform to a well-defined normal behavior. Investigating time series anomalies has become increasingly popular in recent years due to widely deployed IoT devices and sensors. Although most existing methods apply image anomaly detection algorithms directly on time series and commonly assume the data is IID (independent and identically distributed). However, in most realworld applications, the underlying data distribution alters over time. Changes in data over time are referred to as concept drift and can significantly degrade the efficacy of an anomaly detection model. Existing research also indicates that the majority of anomaly detection frameworks ignored concept drift, resulting in poor long-term model performance. This paper presents an ensemble-based, unsupervised anomaly detection framework that is adaptive to the concept drift in time series data. Both distribution and performance-based techniques are used to detect concept drift and update the model to ensure the framework's longterm performance reliability. The proposed method is evaluated using a real-world univariate time series data that captures sea surface temperature data over a 2 -year period. By comparing our proposed framework to other state-of-the-art algorithms, we demonstrate our proposed method has obvious benefits in accuracy and long-term stability. © 2024 IEEE.

关键词： Anomaly detection Concept drift adaptation Ensemble method Time series Unsupervised method

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