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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Ray, Palash Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Information Systems BITS Pilani Rajasthan Pilani India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9789819726134

Sport action recognition is an interesting area in computer vision. Categorization of sport actions, representing difficult and complex body postures, is regarded as a fine-grained visual classification problem. The Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors in recognizing various sport activities. In general, though decent improvement has been gained using deep learning for sport action recognition, however, recognition of women’s sport activities is not widely explored. Even, no benchmark dataset depicting women’s sport action with sufficient variations is available yet for study. Hence, fine-grained image classification of diverse sport categories involving female/women athletics requires immediate research attention. To overcome this limitation, this paper proposes an image dataset comprising worldwide popular 50 sport categories of women players only. A simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. Then, these features are pooled from a collection of regular regions representing local discriminative information. The spatial pyramid pooling is applied for mining semantic information and enhancing feature aggregation for classification. The proposed method has achieved satisfactory performance on the Women Sports dataset using four standard backbone CNNs. Moreover, our method has achieved better accuracy on the Yoga-82 pose recognition dataset with a significant margin, e.g., 11.6% gain using ResNet-50 base CNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Fused Transformers: Fused Information of Arabic long Article for Summarization 6

Fused Transformers: Fused Information of Arabic long Article...

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6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Ezzat, Zeyad Khalfallah, Ayman Khoriba, Ghadad Center for Informatics Science Information Technology and Computer Science School Nile University Giza Egypt Computer and Systems Department Faculty of Engineering Alexandria University Alexandria Egypt Applied Innovation Centre Egypt Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt

This paper introduces a novel approach for extending the applicability of pre-trained models to accommodate longer texts. Addressing the inherent limitation of quadratic performance in attention models within transformers, we propose the concept of fused transformers. By integrating new adaptor techniques, we enhance the encoding of lengthy text segments by breaking them into shorter spans. Subsequently, these segments are fused to increase the model's effectiveness in processing extended texts. This fusion mechanism serves to fortify the model's capacity for fine-tuning. Furthermore, we implement a length-based curriculum for expedited training Our experiments yielded 16 Rouge-2 points, representing a doubling of the score achieved by the vanilla fine-tuning method on the newly introduced "Mukhtasar" dataset for summarization. This highlights the effectiveness of managing complex relations among text segments and confirms that our method can outperform conventional training approaches. © 2024 Elsevier B.V.. All rights reserved.

关键词： Distribution transformers

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CervixNet: A Reward-Based Weighted Ensemble Framework for Cervical Cancer Classification 4th

CervixNet: A Reward-Based Weighted Ensemble Framework for Ce...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Roy, Kaushiki Bhattacharjee, Deboparna Banik, Debapriya Krejcar, Ondrej Sarkar, Ram Kolkata700091 India University of Engineering and Management Kolkata700160 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Computer Science and Engineering RCC Institute of Information Technology Kolkata700015 India Department of Computer Science and Engineering ICFAI University Tripura Agartala799210 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

ISBN: (纸本)9789819726103

Cervical cancer is one of the most common causes of death among women worldwide. However, this fatal disease can be treated, and the mortality rate can be decreased if detected at an early stage. The Papanicolaou test is the gold standard for screening cervical cancer patients. However, the process of manual inspection is tedious and subject to manual errors. Thus, computer-based automatic screening is considered a viable alternative. To this end, in the present work, we have developed a novel system for classifying cervical cancer images as either normal or malignant. We have used the Herlev dataset in this study, which is the standard Pap smear image benchmark dataset. The proposed framework uses multiple base deep learning frameworks, namely Vision Transformer, Xception, VGG-19, VGG-16, and ResNet-101, for classification. Further, we have introduced a novel reward-based weighing technique to decide the weights of individual classifiers, which are, in turn, used to make decisions about the final class label of an input image using the weighted average technique. The proposed framework achieves an overall accuracy, precision, recall, F1-score, and AUC of 96%, 94%, 92%, 93%, and 94%, respectively, for the binary classification task that is normal vs. malignant. An elaborate study of the performance achieved by our proposed framework on the Herlev dataset shows it to be both robust and effective for the Pap smear image classification task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

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computer Systems Science & engineering 2023年第1期44卷 235-247页

作者： R.Bhaskaran S.Saravanan M.Kavitha C.Jeyalakshmi Seifedine Kadry Hafiz Tayyab Rauf Reem Alkhammash Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science Faculty of Engineering&InformaticsUniversity of BradfordBradfordUnited Kingdom English Department University CollegeTarabaTaif UniversityTaif21944Saudi Arabia

Sentiment Analysis(SA)is one of the subfields in Natural Language Processing(NLP)which focuses on identification and extraction of opinions that exist in the text provided across reviews,social media,blogs,news,and so *** has the ability to handle the drastically-increasing unstructured text by transform-ing them into structured data with the help of NLP and open source *** current research work designs a novel Modified Red Deer Algorithm(MRDA)Extreme Learning Machine Sparse Autoencoder(ELMSAE)model for SA and *** proposed MRDA-ELMSAE technique initially performs pre-processing to transform the data into a compatible ***,TF-IDF vec-torizer is employed in the extraction of features while ELMSAE model is applied in the classification of ***,optimal parameter tuning is done for ELMSAE model using MRDA technique.A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced effi-ciency of MRDA-ELMSAE technique against other recent techniques.

关键词： Sentiment analysis data classification machine learning red deer algorithm extreme learning machine natural language processing

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Multi-Criteria Decision-Making Approach for Laptop Selection: A Case Study 3

Multi-Criteria Decision-Making Approach for Laptop Selection...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Channi, Harpreet Kaur Sandhu, Ramandeep Faiz, Mohammad Islam, Sardar M. N. Chandigarh University Gharuan Department of Electrical Engineering Punjab Mohali India School of Computer Science and Engineering Lovely Professional University Punjab Jalandhar India Victoria University Applied Informatics Research Melbourne Australia

ISBN: (纸本)9798350302288

This article uses the well-known multi-criteria decision-making (MCDM) theory to determine which laptop is the best among those that are currently on the market. To illustrate the importance of decision-making in a class of 25 students, data on laptop features was gathered for this case study. The following eight factors were considered while choosing an option: price, weight, RAM, camera megapixels, screen size, clock speed, operating system, and laptop appearance. SAW and WPM determine the optimal laptop alternative. To rank laptop qualities, identical weights are applied. The suggested work displays laptop rankings derived by SAW and WPM methods. Future engineers and management may benefit significantly from this straightforward decision-making process. More important attributes/criteria and alternatives may be taken into account, and there are a variety of weighting techniques from which to choose. The study emphasizes the importance of incorporating user preferences and real-time market data to ensure personalized and up-to-date laptop recommendations. Through this approach, users can make well-informed decisions that align with their specific needs and environmental considerations. © 2023 IEEE.

关键词： Decision making

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Communicative robot signals: Presenting a new typology for human-robot interaction 23

Communicative robot signals: Presenting a new typology for h...

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18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023

作者： Holthaus, Patrick Schulz, Trenton Lakatos, Gabriella Soma, Rebekka University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom Norwegian Computing Center Department of Applied Research in Ict Oslo Norway University of Oslo Department of Informatics Oslo Norway

ISBN: (纸本)9781450399647

We present a new typology for classifying signals from robots when they communicate with humans. For inspiration, we use ethology, the study of animal behaviour and previous efforts from literature as guides in defining the typology. The typology is based on communicative signals that consist of five properties: the origin where the signal comes from, the deliberateness of the signal, the signal's reference, the genuineness of the signal, and its clarity (i.e., how implicit or explicit it is). Using the accompanying worksheet, the typology is straightforward to use to examine communicative signals from previous human-robot interactions and provides guidance for designers to use the typology when designing new robot behaviours. © 2023 Copyright held by the owner/author(s).

关键词： Human robot interaction

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RMSD Calculations for Comparing Protein Three-Dimensional Structures 10th

RMSD Calculations for Comparing Protein Three-Dimensional S...

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10th International Conference on Numerical Methods and Applications, NMA 2022

作者： Sapundzhi, Fatima Popstoilov, Metodi Lazarova, Meglena Department of Communication and Computer Engineering Faculty of Engineering South-West University "Neofit Rilski" Blagoevgrad2700 Bulgaria Department of Mathematical Modelling and Numerical Methods Faculty of Applied Mathematics and Informatics Technical University of Sofia Sofia1000 Bulgaria

ISBN: (纸本)9783031324116

The root Mean Square Deviation (RMSD) is a popular measure of structural similarity between protein structures in the field of bioinformatics. The RMSD calculations involve alignment and optimal superposition between matched pairs of atoms, searching for the lowest RMSD result for both structures. Among the popular methods for calculating the optimal rotation matrix that minimizes the RMSD are the Kabsch algorithm and the Quaternion algorithm. The aim of this research is to present a simple tool for calculation of the RMSD between pairs of aligned three dimensional structures by applying the methods mentioned above. As an implementation language the object-oriented C# programming language has been chosen. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Proteins

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Adaptive AI-Based Personalized Learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient Learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (Science and Information Organization). All rights reserved.

关键词： Students

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The Role of Soft Skills in engineering Education 53

The Role of Soft Skills in Engineering Education

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Fojcik, Marcin Fojcik, Martyna Katarzyna Ziebinski, Adam Pollen, Bjarte Kampen, Anne-Lena Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Førde Norway Volda University Collage Department of Humanities and Education Volda Norway Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350336429

The rapid but uneven education development worldwide is creating many challenges. Changing conditions and rules in engineering fields such as programming, automation, and electronics are not always followed by changes in education. Employers expect potential candidates to be current with the latest domain competencies and soft skills. The idea is to hire candidates with both knowledge (theoretical and practical) and experience in reflection and collaboration skills. Universities can not always keep up with the development of technology and the adaptation of curricula because change happens so quickly. Therefore, upgrades are needed in engineering education. Focusing education solely on measurable knowledge and results is a simple but often insufficient approach to presenting the entire curriculum within the time frame of a course. Our study of about 150 engineering students in Poland and Norway included the role of students' experiences in hands-on activities, especially labs. The research aimed to determine what students expect, what influences their results, and what complications may arise. The study results show that students often struggle with time management, prioritizing, and understanding practical tasks. In addition, there is a difference between students with and without practical work experience. Our research showed that both groups of students did not achieve all their intended educational goals for various reasons. Therefore, adapting the current teaching system to the student's needs and requirements is important. © 2023 IEEE.

关键词： Bolognia process culture model engineering education laboratory teaching soft skills

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