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International Conference on Advancements in Computational Sciences (ICACS)

作者： Uzair Rasheed Sadia Khalid Mishal Muneer Israr Ahmad Ibraheem Khalil Faisal Masood Department of Computer and Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Organizations normally focus on delivering the product that could earn maximum profit for the client. They do not focus on time, cost and quality. So the resultant product is of poor quality, over budget, over time, poorly satisfies the client’s requirements, and is high in cost. In this research paper, we will use lean concepts in software project management. It will not only remove all the waste in project management but also improve the project’s quality, time, and cost. We will also compare the project management concept with the lean project management concept and how combining these two will give us a perfect idea to increase software quality, reduce time, and deliver faster delivery. The only drawback is that current standards of software project management are not mature enough to adopt lean management to its extent.

关键词： Waste management Costs Standards organizations Decision making Project management software quality Organizations software reliability software development management

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FER-Former: Multimodal Transformer for Facial Expression Recognition

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IEEE Transactions on Multimedia 2025年 27卷 2412-2422页

作者： Li, Yande Wang, Mingjie Gong, Minglun Lu, Yonggang Liu, Li Lanzhou University School of Information Science and Engineering Lanzhou730000 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada University of Alberta Department of Electrical & Computer Engineering EdmontonABT6G 1H9 Canada Zhejiang Sci-Tech University School of Science Hangzhou310018 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China

The ever-increasing demands for intuitive interactions in virtual reality have led to surging interests in facial expression recognition (FER). There are however several issues commonly seen in existing methods, including narrow receptive fields and homogenous supervisory signals. To address these issues, we propose in this paper a novel multimodal supervision-steering transformer for facial expression recognition in the wild, referred to as FER-former. Specifically, to address the limitation of narrow receptive fields, a hybrid feature extraction pipeline is designed by cascading both prevailing CNNs and transformers. To deal with the issue of homogenous supervisory signals, a heterogeneous domain-steering supervision module is proposed to incorporate text-space semantic correlations to enhance image features, based on the similarity between image and text features. Additionally, a FER-specific transformer encoder is introduced to characterize conventional one-hot label-focusing and CLIP-based text-oriented tokens in parallel for final classification. Based on the collaboration of multifarious token heads, global receptive fields with multimodal semantic cues are captured, delivering superb learning capability. Extensive experiments on popular benchmarks demonstrate the superiority of the proposed FER-former over the existing state-of-the-art methods. © 1999-2012 IEEE.

关键词： Semantics

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Automated software Requirements Prioritization Using Natural Language Processing

Automated Software Requirements Prioritization Using Natural...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Israr Ahmad Uzair Rasheed Ibrahim Khalil Zaeem Anwaar Wasi Haider Butt Department of Computer and Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

The software requirements specification (SRS) can become a problem & barrier to the successful completion of a project, this can become an obstacle to the successful delivery of a project through the software requirements specification (SRS). In some cases, this results in not addressing real needs. The SRS dataset can include duplicate data or disputed content which cause high costs and wastage of the time and the general effectiveness of the project will be reduced. Latest advancements in machine learning have led to active endeavors to formulate automated approaches for generating seamless software requirements specification [6]. We thus apply transformer models, such as BERT and RobERTa in this study, for classification. We examine multiclass text classification with RoBERTa and classification tasks involving the prediction of type, priority, and severity of user-specified requirements. Along with that, we compare its performance to other natural language processing (NLP) models like LSTM and BiLSTM. Our experiments on the DOORS dataset demonstrate higher accuracy using RoBERTa compared to existing approaches. Assessment parameters included accuracy, F1 score, recall, and precision.

关键词： Deep learning Accuracy Costs Computational modeling Text categorization Bidirectional long short term memory Transformers software Natural language processing

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Decentralized Medical Image Sharing: A Blockchain Based Approach with Subject Sensitive Hashing for Enhanced Privacy and Integrity

IET Blockchain

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IET Blockchain 2025年第1期5卷

作者： Arafat, Yeasir Siam, Abu Sayem Md. Chowdhury, Md Muzadded Hasan, Md Mehedi Jobayer, Sayed Hossain Shatabda, Swakkhar Islam, Salekul Mukta, Saddam Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Electrical and Computer Engineering Department North South University Dhaka Bangladesh Department of Software Engineering LUT University Lappeenranta Finland

This research presents a blockchain-based framework for secure and efficient medical image sharing, prioritizing data integrity and privacy. The framework involves two key phases: image compression with feature extraction and image encryption with storage on the InterPlanetary File System (IPFS). Medical images are compressed using the JPEG algorithm to reduce file size while maintaining diagnostic value. A deep neural network-based subject sensitive hashing (SSH) algorithm ensures feature map integrity by extracting consistent features from both original and compressed images. Encrypted images, along with SSH-generated hashes, are securely stored in the IPFS server. The encryption key and hash sequence are used for secure image retrieval, with smart contracts validating access requests based on the hash sequence. This multi-stage feature extraction approach demonstrates robust image integrity, security, and privacy, as verified by experimental results. Achieving an average correctness rate of 98% across multiple datasets, the framework significantly enhances healthcare data management by addressing the challenges of secure, scalable, and private medical image sharing. This research contributes to the development of more efficient, reliable, and privacy-conscious solutions for medical image handling in healthcare systems. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Data integrity

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When Generative AI Meets Semantic Communication: Optimizing Radio Map Construction and Distribution in Future Mobile Networks

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IEEE Network 2025年第3期39卷 47-55页

作者： Zhou, Li Deng, Xinfeng Ning, Zhaolong Zhao, Haitao Wei, Jibo Leung, Victor C. M. National University of Defense Technology College of Electronic Science and Technology Changsha410073 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shenzhen MSU-BIT University AI Research Institute Shenzhen518000 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518000 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

With the rapid development of the internet of things and smart cities, the demand for effective spectrum collaboration has grown *** maps play a crucial role in understanding the spatial radio environment, which is essential for wireless applications such as cell planning and radio resource management. However, generating radio maps is a resource-intensive task, as the construction process is complex and the distribution process consumes substantial bandwidth. This article proposes a method that combines semantic communication (SemCom) and generative artificial intelligence (GAI) to optimize the construction and distribution processes of radio maps. By incorporating SemCom, our method transmits only key semantic information during the distribution process, significantly reducing bandwidth usage while ensuring accurate and efficient information transfer. While the diverse generative capabilities of GAI introduce some instability, which could be a limitation in radio map construction, this article achieves precise content decoding through prompts based on multi-modal semantic information, enabling accurate construction and restoration of radio maps. By utilizing SemCom and GAI technologies, the cost of applications of radio maps can be significantly reduced, which is beneficial for stimulating the pace of intelligence evolution in future mobile networks. © 1986-2012 IEEE.

关键词： Bandwidth

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Renewable energy-powered DC-converted refrigerator based on a supercapacitor-assisted technique

Renewable energy-powered DC-converted refrigerator based on ...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Gallage, Nirashi Polwaththa Kularatna, Nihal Steyn-Ross, Alistair Kularatna-Abeywardana, Dulsha University of Waikato School of Engineering Hamilton3240 New Zealand University of Auckland Department of Electrical Computer and Software Auckland1010 New Zealand

ISBN: (纸本)9798331516116

In six ICDCM conferences, the value of future DC systems, and DC homes for reducing losses in our electrical systems was discussed indicating the need for a DC-based future. DC outputs from renewable sources contribute to reducing CO2 emissions and lowering electrical energy consumption. In modern white goods, internal circuits are based on DC power rails derived from AC mains followed by a rectifier. Powering AC households from renewable requires maximum power point tracking based on battery chargers and inverters. End-to-end efficiency is related to the number of individual converters inside an equipment. Here we describe a low-frequency supercapacitor-assisted converter system coupled with an "inverter-driven"refrigerator converted to DC operation by eliminating the rectifiers. With this modification, whiteware becomes more tolerant of variable DC sources, allowing continuous operation without battery buffers in solar or wind energy systems. A modified 230 V, 50 Hz refrigerator for DC operation was connected with a supercapacitor module rated at 110 V DC, allowing it to run directly from a renewable source and eliminating the need for battery buffering. This is another supercapacitor-assisted technique based on the relatively new supercapacitor-assisted loss management concept and it eliminates the requirement for electromagnetic compatibility filtering. © 2025 IEEE.

关键词： Power converters

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Design of Flexure Robotic Hand for Teleoperation 6th

Design of Flexure Robotic Hand for Teleoperation

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6th Joint International Conference on Multibody System Dynamics and 10th Asian Conference on Multibody Systems, IMSD-ACMD 2020

作者： Zubair, Mohammad Bhandari, Shubham Chatterjee, Indranath Department of Mechanical Engineering Adamas University Kolkata India Software Development Engineer-I HashedIn by Deloitte Bengaluru India Department of Computer Science Manchester Metropolitan University Manchester United Kingdom

ISBN: (纸本)9789819775248

Robots are getting deployed at home and in industries, becoming an integral part of our daily lives. Sophisticated mechanisms are required to make the joints of the robots compact and simple. In this paper, we have developed a flexure joint for the fingers of the robotic hand. The flexure joint is arranged in series to make a three-DOF underactuated finger. Five fingers are arranged to make a robotic hand. Castigliano’s theorem is used to develop the deformation related to the applied loads, using the equation the flexure mechanism was designed. Pseudo-rigid model is developed to determine the stiffness of the flexure mechanism. A virtual model of the flexure mechanism is developed in a CAD environment. Finite Element (FE) simulation is conducted to the different loading conditions to evaluate the performance of the flexure mechanism. It was seen that the flexure mechanism is flexible about the rotational axis and stiff about the other axes. The stiffness model developed using the pseudo-rigid model has a good correlation with the FE simulation model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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An Intelligent Machine Learning Approach for Prediction of Road Accident Severity 3

An Intelligent Machine Learning Approach for Prediction of R...

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3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Ragam, Prashanth Rahul, Namana Sri Balaji, Pandi Jyothi, Sankati Danaveni, Pavithra School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India VIT-AP University Software Development Cell Andhra Pradesh Amaravati522237 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798331533205

Road accidents are a primary global concern for public safety, with India having a very high death toll. This study presents an intelligent machine learning approach to predict the severity of road accidents, contributing to the development of more effective and preventive measures. We utilized a comprehensive dataset of road accidents in India, employing advanced machine learning algorithms, including Histogram-based Gradient Boosting (HGB), Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). Our methodology involved extensive data pre-processing, feature engineering, and model optimization. The Random Forest algorithm demonstrated superior performance, achieving an accuracy of 94.7% and an F1-score of 0.947. This research provides a robust predictive model and information on the key factors influencing the accident's severity. The novelty of our approach lies in the comprehensive feature engineering tailored to the Indian context and the application of ensemble learning techniques to capture complex, nonlinear relationships in accident data. The findings of this study have significant implications for policymakers, urban planners, and law enforcement agencies in their efforts to improve road safety. © 2025 IEEE.

关键词： Highway accidents

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Detecting Cross-Domain Ambiguity In Requirements Through Natural Language Processing, A Systematic Literature Review

Detecting Cross-Domain Ambiguity In Requirements Through Nat...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Ibrahim Khalil Israr Ahmad Uzair Rasheed Wasi Haider Butt Zaeem Anwaar Department of Computer and Software Engineering CEME NUST Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

A strong foundation for a project is the first step to measuring or estimating the success parameters of a project. Requirement engineers gather and elicit the requirements of the project in detail after the feasibility study. A project’s success can be measured/estimated if and only if the initially collected requirements are clear, unambiguous, and well-understood. Similarly, ambiguous or not understandable requirements can lead to failure or closure of the project in disastrous form. An initial step in requirement elicitation is usually gathering requirements in natural language. This study analyzes different tools, techniques, and approaches in different research articles used for detecting ambiguities in requirements in the natural language. We identified 33 research articles, published during 2012-24 through a Systematic Literature Review. For automated ambiguities detection in requirements engineering, we identified 17 tools & techniques and 12 NLP approaches. We analyzed the better approach for cross-domain ambiguity detection. SGNS (Skipgram Negative Sampling) variant of Word2Vec for the cross-domain ambiguity detection in the review of the study, resulted to be the most efficient technique implemented with the usage view and easily scalable for multiple requirements and domains.

关键词： Scalability software algorithms Natural language processing software Requirements engineering Systematic literature review

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Performance Analysis of Tesseract and EasyOCR for Bangla Optical Character Recognition on the Novel Bangla CrossHair Dataset 3

Performance Analysis of Tesseract and EasyOCR for Bangla Opt...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Chowdhury, Abdulla Nasir Sami, Aftar Ahmad Mamun, Shah Masud Parvej Absar, Shakib Rahman, Fuad Kohinoor, Saidur Rahman Leading University Department of Electrical and Electronic Engineering Sylhet Bangladesh Leading University Department of Computer Science and Engineering Sylhet Bangladesh Taylor's University Department of Software Engineering Kualalampur Malaysia King Fahd University of Petroleum and Minerals Department of Information and Computer Science Dhahran Saudi Arabia

ISBN: (纸本)9798331523893

This paper presents a comparative study of key metrics for OCR engines in Bangla language processing. PyTesseract (a Python wrapper for Tesseract OCR) and EasyOCR were benchmarked on a novel dataset, "Bangla-CrossHair,"created for testing OCR engines. This dataset combines samples from "Bangla Text Detection and Recognition,""Bangla Handwritten Characters,"and "Bangla Handwritten Words"datasets and includes diverse image types, such as blurred, clear, torn, and tilted. Results show EasyOCR outperforms PyTesseract in several scenarios, while PyTesseract consistently demonstrated faster processing. Since many OCR engines provide pre-trained capabilities, traditional metrics like training and validation accuracy are challenging to measure for some models, including PyTesseract. Instead, Character Level Accuracy, Word Level Accuracy, Levenshtein Distance, Character Error Rate, Word Error Rate, Precision, Recall, F1-Score, and Inference time were measured for an unbiased evaluation. © 2025 IEEE.

关键词： Bangla-CrossHair Comparison EasyOCR Levenstein Distance PyTesseract

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