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2023 31st National Conference with International Participation, TELECOM 2023

作者： Sofotasios, Paschalis C. Yoo, Seong Ki Simmons, Nidhi Cotton, Simon L. Valkama, Mikko Khalifa University Center for Cyber Physical Systems Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Tampere University Department of Electrical Engineering TampereFI-33101 Finland Coventry University Centre for Future Transport and Cities Faculty of Engineering Environment and Computing United Kingdom Queen's University Belfast Institute of Electronics Communications and Information Technology BelfastBT3 9DT United Kingdom

ISBN: (纸本)9798350303292

The μ / inverse gamma and μ / inverse gamma distributions constitute two of the most distinct composite fading models. The present work offers novel results on the achievable ergodic capacity over these composite multipath/shadowing conditions. The derived expressions are corroborated with respective simulation results and are shown to be useful in practical wireless scenarios in the context of device to device communications, such as off-body, and vehicle-to-vehicle communications. © 2023 IEEE.

关键词： Vehicle to vehicle communications

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Deep Learning-Based Approaches for Accurate Brain Tumor Detection in MRI Images

Deep Learning-Based Approaches for Accurate Brain Tumor Dete...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Mohammed Noori Mohsin Ahmed Amer Ibrahim Ali Saeed Mohammad Khishe Mahmoud Mahfuri Al-ma'moon University College Baghdad Iraq Department of Computer Science and Software Engineering College of Information Technology Computer Center Northern Technical University Ninevah Iraq Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara research center Jadara University Jordan Computer and information Technology Department International University for Science and Technology

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Brain tumor classification is considered one of the major tasks in medical image analysis, where correct and timely diagnosis could be achieved to serve as the key to effective treatment. This research paper proposes a deep learning-based approach for automatic classification of brain tumors from MRI images by fine tuning the ResNet50V2 CNN model. The dataset is made up of four classes, namely: Glioma Tumor, Meningioma Tumor, No Tumor, and Pituitary Tumor, totaling 2,844 images. We have done some strategy data augmentation and class weighting in order to prevent class imbalance, which ensures good performance in all tumor classes. Pre-processing and augmentation of the input images are done via rotation, shifting, zooming, and flipping. It yielded 95.29% on the validation set, with the major highlights of performance metrics for the minority class being class precision equal to 0.97 and class recall equal to 0.92, hence proving the efficiency of performed class balancing. The range of F1-scores, from 0.93 to 0.97, means fantastic predictive capability across all tumor types. Other techniques were also used to make this model optimal, such as early stopping, learning rate reduction, and checkpointing. The high performance of this model shows great promise in helping clinicians toward the right diagnosis of brain tumors in an effective way. Future efforts will involve incorporating larger datasets, exploring advanced augmentation techniques to enhance further model generalizability. Model explainability tools such as Grad-CAM will be used to extract insight into the model's decision-making process. This enhances the clinical interpretability of the results.

关键词： Accuracy Magnetic resonance imaging Decision making Brain tumors Predictive models Brain modeling Data augmentation Data models Optimization Medical diagnostic imaging

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3D printed multicore fiber-tip discriminative sensor for magnetic field and temperature measurements

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Light(Advanced Manufacturing) 2024年第1期5卷 82-92页

作者： Cong Xiong Caoyuan Wang Ruowei Yu Wei Ji Yu Qin Yichun Shen Wei Chen Ai-Qun Liu Limin Xiao Advanced Fiber Devices and Systems Group Key Laboratory of Micro and Nano Photonic Structures(MoE)Key Laboratory for Information Science of Electromagnetic Waves(MoE)Shanghai Engineering Research Center of Ultra-Precision Optical ManufacturingSchool of Information Science and TechnologyFudan UniversityShanghaiChina Zhongtian Technology Advanced Materials Co. Ltd.NantongJiangsuChina Key laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai University.ShanghaiChina Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong KongChina

Miniaturized fiber-optic magnetic field sensors have attracted considerable interest owing to their superiorities in anti-electromagnetic interference and ***,the intrinsic thermodynamic properties of the material make temperature cross-sensitivity a challenging problem in terms of sensing accuracy and *** this study,an ultracompact multicore fiber(MCF)tip sensor was designed to discriminatively measure the magnetic field and temperature,which was subsequently evaluated *** novel 3D printed sensing component consists of a bowl-shaped microcantilever and a polymer microfluid-infiltrated microcavity on the end-facet of an MCF,acting as two miniaturized Fabry-Perot *** magnetic sensitivity of the microcantilever was implemented by incorporating an iron micro ball into the microcantilever,and the microfluid-infiltrated microcavity enhanced the capability of highly sensitive temperature *** this tiny fiber-facet device in the two channels of an MCF allows discriminative measurements of the magnetic field and temperature by determining the sensitivity coefficient matrix of two *** device exhibited a high magnetic field intensity sensitivity,approximately 1805.6 pm/mT with a fast response time of~213 ms and a high temperature sensitivity of 160.3 pm/℃.Moreover,the sensor had a low condition number of 11.28,indicating high reliability in two-parameter *** proposed 3D printed MCF-tip probes,which detect multiple signals through multiple channels within a single fiber,can provide an ultracompact,sensitive,and reliable scheme for discriminative *** bowl-shaped microcantilever also provides a useful platform for incorporating microstructures with functional materials,extending multi-parameter sensing scenarios and promoting the application of MCFs.

关键词： Two-photon polymerization Multicore fiber-tip sensor Magnetic field sensing Discriminative sensing

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Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

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Frontiers in Energy Research 2024年 12卷

作者： Alhussan, Amel Ali Khafaga, Doaa Sami El-kenawy, El-Sayed M. Eid, Marwa M. Ibrahim, Abdelhameed Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia MEU Research Unit Middle East University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences. Copyright © 2024 Alhussan, Khafaga, El-kenawy, Eid and Ibrahim.

关键词： Charging stations

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Development of Low Resource Machine Learning Models for Child Cognitive Ability Assessments

Development of Low Resource Machine Learning Models for Chil...

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Advancements in Computing (ICAC), International Conference on

作者： Adithya Kahawanugoda Kulanika Gnanarathna Nilan Meegoda Randika Monarawila Pradeepa Samarasinghe Asiri Gawesha Lindamulage Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

Automated cognitive assessment tools are state-of-the-art in assessing cognition development. Due to the low availability of resources, building automated cognitive ability evaluation tools is challenging. This study focuses on developing machine learning models using a limited amount of data to assess Reasoning IQ, Knowledge IQ, Mental Chronometry and Attention-levels of Sinhala-speaking children between the age of 7 to 9 years. Our solution includes Sinhala speech recognition systems, image classification models, gaze estimation, blink count detection and facial expression recognition models to evaluate the above four cognitive ability measuring factors. Open domain speech recognition has been used to evaluate complex Sinhala child verbal responses and limited vocabulary responses were assessed using an end-to-end speech recognition system, respectively achieving 40.1% WER and 97.14% accuracy. Additionally, the image classification models for handwritten Sinhala letter recognition and two shape recognition models have gained 97%, 89% and 99% accuracy. The linear regression model for attention level evaluation that utilizes the inputs from a combination of eye-gaze estimation, facial expression recognition and blink rate detection models has gained 85% accuracy.

关键词： Handwriting recognition Vocabulary Image recognition Computational modeling Face recognition Estimation Speech recognition

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The Rain Prediction in Australia Based Big Data Analysis and Machine Learning technology 16

The Rain Prediction in Australia Based Big Data Analysis and...

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16th IEEE International Conference on Computer Science and information Technologies, CSIT 2021

作者： Polishchuk, Bohdan Berko, Andrii Chyrun, Lyubomyr Bublyk, Myroslava Schuchmann, Vadim Lviv Polytechnic National University Information Systems and Networks Department Lviv Ukraine Ivan Franko National University of Lviv Software Department Lviv Ukraine Lviv Polytechnic National University Management AndInternational Business Department Lviv Ukraine West Ukrainian National University Software Engineering Department Ternopil Ukraine

To prevent wildfires, Australian meteorological data over a ten-year period were analysed to determine the days with the highest risk of uncontrolled fires using machine learning algorithms and statistical data. ©... 详细信息

ISBN: (纸本)9781665442572

关键词： Global warming

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A Integrated Estimation Scheme for Tire Road Friction Coefficient Using EKHF and AUKF

A Integrated Estimation Scheme for Tire Road Friction Coeffi...

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Fengjiao Zhang Shile Zhang Hui Zhang Hongtu Yang Hongdang Zhang Yan Wang College of Transportation Engineering Changzhou Vocational Institute of Mechatronic Technology Changzhou China Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Industrial and Systems Engineering Hongkong Polytechnic University Hongkong China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

The tire-road friction coefficient (TRFC) is essential for vehicle stability control and motion planning, particularly in four-wheel distributed drive electric vehicles. Estimating the TRFC typically requires obtaining the tire force first. However, onboard sensors usually do not directly measure tire force, making it common practice to use a tire model for calculation. The accuracy of tire force calculation is affected by time-varying sensor noise, which in turn impacts the TRFC estimation. To address this, we propose a method that combines the extended H-infinity Kalman filter (EHKF) with the adaptive Unscented Kalman filter (AUKF). The EHKF is used to obtain the tire force, and then the AUKF, along with the vehicle model, estimates the TRFC. Empirical data reveal that the Suggested algorithm surpasses the traditional UKF in terms of estimation accuracy and has lower volatility.

关键词： Accuracy Heuristic algorithms Friction Roads Force Noise Estimation Tires Sensors Kalman filters

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Oral cavity carcinoma detection using BAT algorithm-optimized machine learning models with transfer learning and random sampling

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Computers in Biology and Medicine 2025年第Pt B期192卷 110250-110250页

作者： Folorunso, Sakinat O. Abdulwarith, Akinshipo Adeniyi, Abidemi Emmanuel Aworinde, Halleluyah Oluwatobi Awotunde, Joseph Bamidele Artificial Intelligence Systems Research Group Department of Computer Science Olabisi Onabanjo University Ago-Iwoye Nigeria Department of Oral and Maxillofacial Pathology and Biology Faculty of Dentistry University of Lagos Lagos Nigeria Department of Computer Science College of Computing and Communication Studies Bowen University Iwo Nigeria Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MIVA Open University Abuja Nigeria Department of Information Technology Durban University of Technology KZ Durban South Africa Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Nigeria

Background: Oral cavity carcinoma remains a major public health concern, where early and accurate detection is vital for improving patient outcomes and survival rates. Current diagnostic systems often face challenges such as limited feature selection capabilities, imbalanced datasets, and computational inefficiencies. Methods: This study proposes a novel diagnostic framework TR-ROS-BAT-ML that integrates transfer learning, random sampling, and a BAT algorithm-based optimization strategy with ensemble machine learning classifiers. A dataset comprising 1224 hematoxylin and eosin (H&E)-stained histological images (at 100x and 400x magnifications) of normal oral epithelium and Oral Squamous Cell Carcinoma (OSCC) was collected from 230 patients using a Leica ICC50 HD microscopy camera. Pre-trained deep learning models (NANSNetLarge, EfficientNetB7, EfficientNetV2L, EfficientNetV2S, EfficientNetV2M) were employed for feature extraction. To address class imbalance, random oversampling techniques were applied. The BAT algorithm, inspired by bat echolocation behavior, was used for feature selection and hyperparameter tuning. Optimized features were classified using ensemble methods, including XGBoost, AdaBoost, Extra Trees (ET), Histogram-Based Gradient Boosting (HBGC), and MultiLayer Perceptron (MLP). Results: The proposed approach achieved high diagnostic performance across multiple model combinations. The best performance was recorded with the optimized ET model using random oversampling, achieving a recall of 0.992, demonstrating its efficacy in detecting oral lesions. In contrast, the combination of EfficientNetV2S + ROS + MLP yielded the lowest accuracy at 50.8 %. These results confirm the robustness of the TR-ROS-BAT-ML framework in handling imbalanced datasets and optimizing classification performance. Conclusions: This study demonstrates the effectiveness of combining nature-inspired optimization, transfer learning, and ensemble machine learning for enhanced detecti

关键词： Adaptive boosting

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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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Everything you wanted to know about ChatGPT: Components, capabilities, applications, and opportunities

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Internet technology Letters 2024年第6期7卷

作者： Heidari, Arash Navimipour, Nima Jafari Zeadally, Sherali Chamola, Vinay Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan College of Communication and Information University of Kentucky LexingtonKY United States Pilani India

Conversational Artificial Intelligence (AI) and Natural Language Processing have advanced significantly with the creation of a Generative Pre-trained Transformer (ChatGPT) by OpenAI. ChatGPT uses deep learning techniques like transformer architecture and self-attention mechanisms to replicate human speech and provide coherent and appropriate replies to the situation. The model mainly depends on the patterns discovered in the training data, which might result in incorrect or illogical conclusions. In the context of open-domain chats, we investigate the components, capabilities constraints, and potential applications of ChatGPT along with future opportunities. We begin by describing the components of ChatGPT followed by a definition of chatbots. We present a new taxonomy to classify them. Our taxonomy includes rule-based chatbots, retrieval-based chatbots, generative chatbots, and hybrid chatbots. Next, we describe the capabilities and constraints of ChatGPT. Finally, we present potential applications of ChatGPT and future research opportunities. The results showed that ChatGPT, a transformer-based chatbot model, utilizes encoders to produce coherent responses. © 2024 John Wiley & Sons Ltd.

关键词： Taxonomies

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