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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pujari, Jambukeshwar Wasim, Javed Tripathi, Aprna Mangalayatan University Department of Computer Engineering and Applications Aligarh20221385 India Mangalayatan University Department of Computer Engineering and Applications Aligarh India Manipal University Department of Data Science Engineering Jaipur India

ISBN: (纸本)9798331512965

In recent years, Geographic Information Systems (GIS) have garnered a significant deal of interest for their ability to detect changes in metropolitan areas. One of the uses of change detection in satellite photographs is in the field of urban planning. Other applications include environmental monitoring and remote surveillance. A number of different approaches have been suggested in recent times for the purpose of automatically identifying alterations in the land cover based on GIS photographs. However, due to poor texture, cluttered context, and considerable object scale disparities, the convolutional neural network (CNN) method is only partially successful in capturing the context from geographic information systems (GISs). This is despite the fact that it remains an effective method for detecting change in urban environments. The article presents the construction of a CNN-based change detection model using the utilization of Dung Beetle Optimization (DBO). The DBO algorithm is used to adjust the loss parameter in order to increase the overall performance. The approach that was proposed makes use of the Onera satellite data set and displays an overall accuracy of 97.50% percent. In light of this, it appears that the strategy that is proposed is both very efficient and effective. © 2025 IEEE.

关键词： Convolutional neural networks

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Precise Multi-Class Classification of Brain Tumor via Optimization Based Relevance Vector Machine

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Intelligent Automation & Soft Computing 2023年第4期36卷 1173-1188页

作者： S.Keerthi P.Santhi Department of Computer Science Engineering Dayananda Sagar College of EngineeringBangalore560078India Department of Computer Science Engineering M.Kumarasamy College of EngineeringKarurTamil Nadu639113India

The objective of this research is to examine the use of feature selection and classiﬁcation methods for distinguishing different types of brain *** brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or *** tumors are misdiagnosed due to the variabil-ity and complexity of lesions,which reduces the survival rate in ***-sis of brain tumors via computer vision algorithms is a challenging *** and classiﬁcation of brain tumors are currently one of the most essential surgical and pharmaceutical *** brain tumor identi-ﬁcation techniques require manual segmentation or handcrafted feature extraction that is error-prone and *** the proposed research work is mainly focused on medical image processing,which takes Magnetic Resonance Imaging(MRI)images as input and performs preprocessing,segmentation,fea-ture extraction,feature selection,similarity measurement,and classiﬁcation steps for identifying brain ***,the medianﬁlter is practically applied to the input image to reduce the *** graph-cut segmentation technique is used to segment the tumor *** texture feature is extracted from the output of the segmented *** extracted feature is selected by using the Ant Colony Opti-mization(ACO)algorithm to improve the performance of the classiﬁ*** prob-abilistic approach is used to solve computing *** Euclidean distance is used to calculate the degree of similarity for each extracted *** selected feature value is given to the Relevance Vector Machine(RVM)which is a multi-class classiﬁcation ***,the tumor is classiﬁed as abnormal or *** experimental result reveals that the proposed RVM technique gives a better accuracy range of 98.87%when compared to the traditional Support Vector Machine(SVM)technique.

关键词： Brain tumor segmentation classiﬁcation relevance vector machine(RVM) ant colony optimization(ACO)

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IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

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Intelligent Automation & Soft Computing 2023年第1期35卷 567-580页

作者： M.P.Karthikeyan E.A.Mary Anita Department of Computer Science and Engineering R.M.K.Engineering CollegeChennai601206India Department of Computer Science and Engineering School of Engineering and TechnologyChrist UniversityBengaluru560029India

In recent years,there has been a significant increase in the number of people suffering from eye illnesses,which should be treated as soon as possible in order to avoid *** Fundus images are employed for this purpose,as well as for analysing eye abnormalities and diagnosing eye *** can be recognised as bright lesions in fundus pictures,which can be thefirst indicator of diabetic *** that in mind,the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis(IM-EDRD)with multi-level *** model uses Support Vector Machine(SVM)-based classification to separate normal and abnormal fundus images at thefirst *** input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix(GLCM).Furthermore,the presence of Exudate and Diabetic Retinopathy(DR)in fundus images is detected using the Adaptive Neuro Fuzzy Inference System(ANFIS)classifier at the second level of *** detection,blood vessel extraction,and Optic Disc(OD)detection are all processed to achieve suitable ***,the second level processing comprises Morphological Component Analysis(MCA)based image enhancement and object segmentation processes,as well as feature extraction for training the ANFIS classifier,to reliably diagnose ***,thefindings reveal that the proposed model surpasses existing models in terms of accuracy,time efficiency,and precision rate with the lowest possible error rate.

关键词： Retinal fundus images exudate diabetic retinopathy SVM ANFIS morphological component analysis

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Features enhancement of micro-service based cab booking system using block-chain 4th

Features enhancement of micro-service based cab booking syst...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Rohit Singh, Neelaksh Shukla, Riya Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (纸本)9781032911571

Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, March 18, 2021). It has a micro-service based architecture (Li et al. 2021). Load balancing (Al-Jaroodi et al. 2012) and stream processing (Besnard et al. 2013) are used to handle a large number of requests. The workload is divided among servers using Dispatch Optimization (Abid et al. 2022). It ensures fast responses, easy connection minimal downtime, and efficient use of resources. However, the use of servers increases the cost of service. Rider and driver depend on the server to connect. They don’t know if the system is charging them fairly. It reduces transparency and trust. The paper proposes a blockchain-based cab booking application to solve some of the problems. Trust, transparency, and decentralization can be ensured using blockchain. Transparent and immutable ledgers of blockchain ensure trust and reduce reliance on intermediaries. Smart contracts govern ride management, optimizing efficiency. Blockchain capabilities are used to ensure fair rides, direct interaction of passengers with drivers, and automated payments. The proposed system aims at combining existing technology with blockchain solution to ensure security and transparency along with scalability and quick response time. © 2025 the Author(s).

关键词： Reservation systems

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ROUGE-SciQFS: A ROUGE-based Method to Automatically Create datasets for Scientific Query-Focused Summarization 31

ROUGE-SciQFS: A ROUGE-based Method to Automatically Create D...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Ramirez-Orta, Juan Milios, Evangelos Maguitman, Ana Soto, Axel J. Faculty of Computer Science Dalhousie University Canada Institute for Computer Science and Engineering UNS - CONICET Department of Computer Science and Engineering Universidad Nacional del Sur Argentina

ISBN: (纸本)9798891761964

So far, the task of Scientific Query-Focused Summarization (Sci-QFS) has lagged in development when compared to other areas of Scientific Natural Language Processing because of the lack of data. In this work, we propose a methodology to take advantage of existing collections of academic papers to obtain large-scale datasets for this task automatically. After applying it to the papers from our reading group, we introduce a novel dataset for Sci-QFS composed of 8,695 examples, each one with a query, the sentences of the full text from a paper and the relevance labels for each. After testing several classical and state-of-the-art embedding models on this data, we found that the task of Sci-QFS is far from being solved, although it is relatively straightforward for humans. Surprisingly, we found that classical methods outperformed modern pre-trained Deep Language Models (sometimes by a large margin), showing the need for large datasets to better fine-tune the latter. We share our experiments, data and models at https://***/jarobyte91/rouge_sciqfs. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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An intrusion detection system using ranked feature bagging

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1213-1219页

作者： Azhagiri, M. Rajesh, A. Karthik, S. Raja, K. Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Jain University Bangalore India Department of IT College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

An Intrusion Detection System monitors the network for any malicious attacks. It is an ideal tool for protecting extensive business networks from any kind of attack. In this paper, an Intrusion Detection System using Bagging and ranked features has been proposed. The sample datasets for training the ensemble of classifiers in the bagging approach are obtained by generating subsets of the features ranked according to their importance in the classification process. OneR algorithm has been used to rank the features. The decision tree has been used as the base classifier in the bagging approach. The proposed system was able to predict attacks with an accuracy of 99.71% with a minimal number of features and outperformed other similar approaches discussed in the literature. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bagging CIC-IDS 2017 dataset Decision tree Ensemble Feature selection Intrusion detection system OneR feature selection

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

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Light(science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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Navigating the Waves of Precision: Optimization Techniques for Sailfish Segmentation in the Pacific 3

Navigating the Waves of Precision: Optimization Techniques f...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Deepa, M. Praveenkumar, G.D. Vikram, N.R. Magesh Kumar, B. Pavai Arts and Science College for Women Department of Computer Science Namakkal India Vet Institute of Arts and Science College Department of Computer Science Namakkal India Paavai College of Engineering Department of Computer Science and Engineering Namakkal India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350396157

A well-known and mysterious species that lives in the vast and varied seas of the Pacific Ocean is the Indo-Pacific sailfish. The sailfish has piqued the interest of marine scientists, ecologists, and fans alike because of its unusual sail-like dorsal fin and amazing speed. Its existence as a pelagic predator, searching the ocean floor, provides a special insight into the mystifying workings of the marine ecosystem. In addition to being magnificent in their natural habitat, sailfish are crucial to comprehending the dynamics of the ecosystems of the Pacific Ocean. In essence, 'Navigating the Waves of Precision' makes a substantial contribution to the study of marine biology and the analysis of underwater images. This study extends the instruments available for the conservation and scientific study of Indo-Pacific sailfish, enabling a greater comprehension of these extraordinary creatures and their crucial role in the Pacific environment. The present work offers useful insights into choosing the best optimization approach based on the particular needs of the sailfish segmentation problem through thorough experimentation and reviews. It also increases the precision and efficacy of sailfish segmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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3GPP Edge–Fog federation: Transparent 3rd-party authentication and application mobility

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computer Communications 2024年 222卷 220-229页

作者： Ali, Asad Islam, Minhajul Mallick, Tushin Islam, Mohammad Sakibul Sakib, Sadman Shohrab Hossain, Md. Lin, Ying-Dar National Institute of Cyber Security Ministry of Digital Affairs Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh

3GPP edge and fog computing paradigms provide computational services to users at low latency. These paradigms alone are not enough to fulfill the users’ requirements completely. Therefore, a federation among these computing paradigms is necessary. To realize such federation, there is a need of an authentication mechanism where subscribers of a fog, can access 3GPP edge's services, or vice versa, without buying new subscription, and an application mobility mechanism for continuous service during handover from 3GPP edge to fog, or vice versa, without re-authentication. In this work, we propose: (1) a proxy-based state transfer and third-party authentication (PS3A), that uses a transparent proxy to transfer the authentication and application state information between 3GPP edge and fog, and (2) a token-based state transfer and proxy-based third-party authentication (TSP3A), that uses the proxy to transfer the authentication information and tokens to transfer application state information between 3GPP edge and fog. The proxy plays different roles, via virtual counterparts of entities involved in these protocols, to provide transparency. When the 3GPP edge, using EPS-AKA, receives an authentication request, the proxy relays and behaves as a virtual Home Subscriber Server (vHSS) for the 3GPP edge, and behaves as a virtual user for the fog, which is using OIDC. We applied PS3A and TSP3A to three federation scenarios among 3GPP edge and fog. Experimental results show that PS3A and TSP3A provide authentication within 0.345–2.858 s for a 0–100 Mbps proxy load. The results further show that TSP3A provides application mobility, while taking 40%–52% less time than PS3A, using state tokens. TSP3A and PS3A also reduce the service interruption latency by 82.4% and 84.6%, compared to the cloud-based service, via tokens and prefetching. © 2024 Elsevier B.V.

关键词： Authentication

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