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Emerging Technology (INCET), International Conference for

作者： Salitha Tennakoon Raeesul Islam Danuja Wijerathna Rimzan Deen Prasanna Sumathipala Lakmini Abeywardhana Department of Data Science Faculty of Computing (SLIIT) Malabe Sri Lanka

Recent studies show that there is a big gap between industry and academia in the field of software engineering. This means that undergraduates often do not have the knowledge and skills that companies are looking for. When they first enter the workforce, undergraduates from our educational institutions frequently fall short of standards set by the industry, and the majority of these undergraduates are unskilled. To institutionalize them, the industry had to frequently mobilize additional resources to provide them with some internal education. The majority of these businesses inside industries have had to provide graduates with the knowledge and abilities necessary for them to perform their jobs well. Even though many computer science and software engineering courses mandate that students complete practical assignments, these are typically frivolous endeavors that fall far short of true professional growth. As a result, undergraduates frequently fall short of what employers are looking for and are turned down for interviews. This research paper identifies and investigates strategies for instructing university students in presentation, technical, and interview-facing skills to close this knowledge gap. One of the undergraduates’ biggest challenges is the lack of information and expertise required to work on a real industrial project. Some of the problems we discovered from our surveys included a lack of interviewing skills, a lack of presentational skills. The majority of undergraduates also have trouble locating reliable resources for learning new skills. Thus, our main goal is to develop a (UniHub) platform that overcomes the aforementioned problems and gives undergraduates virtual work experience so they may get ready for the workforce before they graduate.

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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

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Computer Systems science & Engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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A Study of Current Trends Writer Identification in Large-scale across Three World Major Languages with Retrieval Approaches 6

A Study of Current Trends Writer Identification in Large-sca...

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6th International Conference on Interactive Digital Media, ICIDM 2020

作者： Tan, Gloria Jennis Kumoi, Rosely Rahim, Mohd Shafry Mohd Wee, Tan Chi Sulong, Ghazali Universiti Malaysia Sarawak Centre for IT Development and Services Sarawak Malaysia Universiti Teknologi Malaysia School of Computing Faculty of Engineering Johor Malaysia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Malaysia Centre for Cyber Security and Big Data Management and Science University Malaysia

ISBN: (纸本)9781728149288

Recent developments in writer identification have led new directions, which is writer identification integrated with retrieval mechanism for boosting the performance applications of writer identification when a database is getting larger in size and retrieval of documents from expanding databases is becoming difficult. Issues on datasets used by previous studies directly impacts accuracy of the writer identification deteriorates as database size increases. Until now, in the research on writer identification researchers have been mainly interested in 'writer identification without retrieval mechanism' approach. This paper provides a review on the state of the art for writer identification with retrieval mechanism throughout three major languages, namely English, Chinese, and Arabic. Up to date, truly little attention has been paid to the role of retrieval mechanism or documents in English language identification;the growth on Arabic and Chinese is far from satisfactory in comparison to its extensive usage. A search of the literature revealed few studies for writer identification with single retrieval mechanism. Seamless integration of such strategies in current forensic handwriting experience is yet unclear. A consistently reliable and efficient, fully automated writer recognition (AWR) and a powerful handwritten document retrieval system as an emerging technology are real challenging issues might be taken by upcoming researchers. It is still considered a fresh field and still has large room for research. © 2020 IEEE.

关键词： database systems

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Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks

Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimize...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Aly, Salah A. Bakhiet, Ali Balat, Mazen Badya University Faculty of Computing & Data Science Giza Egypt Fayoum University Faculty of Science Computer Science Dept. Fayoum Egypt Computer Science Dept. October 6th City Giza Egypt E-Japanese University of Science & Tech. Cs & It Dept. Alexandria Egypt

ISBN: (纸本)9798350367560

Lymphoma diagnosis, particularly distinguishing between subtypes, is critical for effective treatment but remains challenging due to the subtle morphological differences in histopathological images. This study presents a novel hybrid deep learning framework that combines DenseNet201 for feature extraction with a Dense Neural Network (DNN) for classification, optimized using the Harris Hawks Optimization (HHO) algorithm. The model was trained on a dataset of 15,000 biopsy images, spanning three lymphoma subtypes: Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). Our approach achieved a testing accuracy of 99.33%, demonstrating significant improvements in both accuracy and model interpretability. Comprehensive evaluation using precision, recall, F1-score, and ROC-AUC underscores the model's robustness and potential for clinical adoption. This framework offers a scalable solution for improving diagnostic accuracy and efficiency in oncology. © 2024 IEEE.

关键词： Lung cancer

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MG-Net: Learn to Customize QAOA with Circuit Depth Awareness 38

MG-Net: Learn to Customize QAOA with Circuit Depth Awareness

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qian, Yang Wang, Xinbiao Du, Yuxuan Luo, Yong Tao, Dacheng School of Computer Science Faculty of Engineering University of Sydney NSW2008 Australia Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling combinatorial optimization challenges. However, their practical realization confronts a dilemma: the requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. To address this dilemma, here we first analyze the convergence behavior of QAOA, uncovering the origins of this dilemma and elucidating the intricate relationship between the employed mixer Hamiltonian, the specific problem at hand, and the permissible maximum circuit depth. Harnessing this understanding, we introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians tailored to distinct tasks and circuit depths. Systematic simulations, encompassing Ising models and weighted Max-Cut instances with up to 64 qubits, substantiate our theoretical findings, highlighting MG-Net's superior performance in terms of both approximation ratio and efficiency. © 2024 Neural information processing systems foundation. All rights reserved.

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E-Learning: A Successful Learning Environment 1

E-Learning: A Successful Learning Environment

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1st International Conference on Computer science and Emerging Technologies, CSET 2023

作者： Momani, Fayiz Hatamleh, Amer Ahmad Amir Fazamin Wan Hamzah, Wan Mohd Jusoh, Julaily Aida Zakaria, Muhammad D. Das, Sunanda Faculty of Management and Financial Sciences E-business & Commerce Department Jordan MIS Bi and Data Analysis Irbid National University Faculty of Business and Financial Science Irbid Jordan Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain University Cyber Security Bangalore India

ISBN: (纸本)9798350341737

Education is developing with the economy. This led to the creation of e-Iearning tools for instructors and students. In e-learning, electronic resources are used to teach. E-learning can be taught in or out of school, but computers and the Internet are essential. That is, e-learning involves delivering education to a big group of people at once or over time. So, overall, in this research is about E-learning that make a successful environment. We chose this term since it is widely used in E-learning research to improve student academic achievement. Due to the difficulties of accessing adequate study materials during the Covid-19 epidemic, distance learning has grown more popular among students. This study proposes a unique learning technique that involves choosing and organizing important learning items using a recommender system. We also studied our method's efficacy. Using a recommended system to help online learning activities was proven to be effective. Software development methodology is a collection of principles and processes used to build software. Systematic software development is the goal. The research approach utilized for this topic is Lean Development Methodology. We also examine the advantages and disadvantages of e-learning. This research also introduces various new strategies and methodologies that may be employed in the future to improve system-learning. It also presents system that we choose and why we choose it. So, it aims to eliminate waste and increase production. Following the principles can help developers avoid nonproductive chores while still producing high-quality results. The ultimate objective is to develop an efficient and error-free system. Finally, E-learning is a social revolution. It's part of a bigger rethinking of how we teach future employees and students. The eLearning platform enabled the learning data visualization dashboard beautifully. User-centered design lets the dashboard adapt to its users' demands. © 2023 IEEE.

关键词： Learning systems

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Multi-Modal data Fusion Approach Using the Structured and Sparse Principal Component Analysis (MMSSCA)

Multi-Modal Data Fusion Approach Using the Structured and Sp...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Kandpal, Jyoti Jonitha, A. Sivakumar, Janaki Ray, Rejon Kumar School of Computing Graphic Era Hill University Uttarakhand Dehradun India Universiti Malaysia Faculty of Data Science & Computing Kelantan Malaysia Global College of Engineering and Technology Computer Science and Creative Technologies Muscat Oman Gannon University Department of Business Analytics PA Erie United States

ISBN: (纸本)9798350329773

There are many different sorts of data that can be gathered and analyzed, including pictures, videos, texts, speeches, music, and other noises, Video content, for example, generally includes minimum some types of audible sounds or any type of visual image, and often also useful text data that can be of some specific type of titles or subtitles, which may consist of some unique type text language which is different type of speech. 'Modality' is basically defines each different type of acquisition strategy. As natural phenomena tend to be quite complex, it is unusual for a single modality to provide exhaustive understanding of the phenomenon of interest. The process of combining and interpreting the useful data between the same modal or in different modal, and also interaction with the human beings with multi-modal data fusion &the accurate and deep interpretation and understanding of data obtained from the combining process from it, are central to the research challenge on multimodal data analysis. We contend that many of these challenges are shared by researchers in different fields. Both the 'why' and the 'how' of data fusion are addressed in this paper. Various examples from the realms of science and technology serve as inspiration for the first problem, which is then suggested by a various types of mathematical based model framework demonstrating the utility of data fusion. To solve the second problem, we introduce the idea of 'diversity' as a central concept and discuss several data-driven solutions based on matrix and tensor decompositions, highlighting how they take into account diversity in the underlying data sets to address the aforementioned issues, this research suggests a novel approach for modeling multi data fusion implemented using sparse technique and structural analysis. © 2023 IEEE.

关键词： Modal analysis

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Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation 23

Dual Intent Enhanced Graph Neural Network for Session-based ...

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2023 World Wide Web Conference, WWW 2023

作者： Jin, Di Wang, Luzhi Zheng, Yizhen Song, Guojie Jiang, Fei Li, Xiang Lin, Wei Pan, Shirui College of Intelligence and Computing Tianjin University China Department of Data Science and Ai Faculty of It Monash University Australia School of Intelligence Science and Technology Peking University China Meituan China Professional China Griffith University Australia

ISBN: (纸本)9781450394161

Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations normally can only recommend items existing in users' historical sessions. As a result, these GNNs have difficulty recommending items that users have never interacted with (new items), which leads to a phenomenon of information cocoon. Therefore, it is necessary to recommend new items to users. As there is no interaction between new items and users, we cannot include new items when building session graphs for GNN session-based recommender systems. Thus, it is challenging to recommend new items for users when using GNN-based methods. We regard this challenge as "GNN Session-based New Item Recommendation (GSNIR)". To solve this problem, we propose a dual-intent enhanced graph neural network for it. Due to the fact that new items are not tied to historical sessions, the users' intent is difficult to predict. We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item. To solve the challenge that new items cannot be learned by GNNs, inspired by zero-shot learning (ZSL), we infer the new item representation in GNN space by using their attributes. By outputting new item probabilities, which contain recommendation scores of the corresponding items, the new items with higher scores are recommended to users. Experiments on two representative real-world datasets show the superiority of our proposed method. The case study from the real-world verifies interpretability benefits brought by the dual-intent module and the new item reasoning module. © 2023 ACM.

关键词： Recommender systems

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Geometric Optimisation of Unmanned Aerial Vehicle Trajectories in Uncertain Environments

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Vehicular Communications 2025年 54卷

作者： Akshya, J. Sundarrajan, M. Amutha, S. Dhanaraj, Rajesh Kumar Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Networking and Communications SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University LS6 3HF Leeds United Kingdom Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The problem of efficient trajectory optimisation for Unmanned Aerial Vehicles (UAVS) in dynamic and constrained environments is one where energy efficiency, spatial coverage, and path smoothness need to be balanced. The existing methods, namely RRT*, A*, and Dijkstra, are popular but generally heuristic and do not provide globally optimal solutions. They face significant limitations while dealing with complex geometries, dynamic obstacles, and multi-objective requirements. These challenges call for a mathematically sound framework that seamlessly integrates convex analysis and computational geometry to provide an optimal trajectory planning framework. This research work introduces a convex optimisation framework for UAV trajectory planning which unifies multiple objectives, like minimising energy consumption, maximising spatial coverage, and ensuring the smoothness of the path, into a single convex objective function. More importantly, it indicates that obstacle dynamics and uncertain environmental conditions are handled better by it, so it is relatively easier for safe and efficient navigation. Proven to converge faster and with higher precision than RRT*, A*, and Dijkstra, the approach proposed here enjoys intrinsic convex properties, which ensure global optimality. Qualitative measurements show the efficiency of the proposed framework. The result is energy efficiency of 90%, with 92% coverage, 98% constraint satisfaction, and 95% path smoothness, which is 15-25% better on all metrics than traditional approaches can offer. By bridging between theory in convex optimisation and practice for solving multi-objective problems in a dynamic setting, this study provides a more robust solution for UAV trajectory planning. © 2025 The Author(s)

关键词： Computational Geometry Convex Optimisation Delaunay Triangulations Spatial Coverage UAV Trajectory Voronoi Diagrams

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