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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prakash Jayant, Khel Kumar Singh, Abhishek Thapliyal, Nitin Sharma, Prashant Goyal, Sunita Raj Kumar Goel Institute of Technology Computer Sciences & Engineering UP Ghaziabad India Shiva Institute of Management Studies Department of Computer Science UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department of Civil Engineering UP Mathura India Abes Engineering College Department of Applied Science & Humanities UP Ghaziabad India

ISBN: (纸本)9798350371055

Contemporary networks are complex structures that require superior algorithms to maintain their security. This paper explores the usage of superior algorithms to cozy networks, focusing on the improvement and deployment of efficient and comfortable algorithms for networked infrastructure. It examines current algorithms used to cozy networks, together with authentication and encryption algorithms, as well as a spread of rising technologies, which include artificial intelligence (AI) and gadget learning (ML). it'll study techniques to optimize the safety of the network through the improvement of smart protection architectures, as well as the demanding situations and opportunities posed by real-international implementations paper will offer a evaluation of various safety algorithms utilized in networks, and speak strategies for efficient deployment and integration in current networks. It'll define demanding situations related to the deployment of advanced algorithms on networks and provide a top level view of the modern nation of safety solutions and their implications for networked infrastructure. Sooner or later, it's going to offer guidelines for destiny studies to further optimize safety for allotted infrastructure. Networked Infrastructure security is a fundamental part of any contemporary gadget. To make certain that such systems are thoroughly covered from malicious actors, superior algorithms are frequently employed to optimize the overall safety of the community. These algorithms are used to identify and detect malicious activities, as well as offer anomaly detection competencies. Moreover, they frequently leverage synthetic intelligence techniques along with device gaining knowledge of and herbal language processing to carry out extra effective and accurate protection analysis. The usage of those algorithms can ensure complete security coverage, as well as improve reaction time to threats and vulnerabilities. Furthermore, it is able to provide an extra degree

关键词： algorithms deployment maximize networked protection

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Agent Architecture for Evacuation Pedestrian's Crowded Public Places: Combination of Individual and Collaborative Reasoning 1

Agent Architecture for Evacuation Pedestrian's Crowded Publi...

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1st IEEE International Conference on Advances in Data-Driven Analytics and Intelligent Systems, ADACIS 2023

作者： Abdallah, Wejden Kanzari, Dalel Madani, Kurosh Laria Laboratory University of Manouba National School of Computer Sciences Tunisia University of Sousse Higher Institute of Applied Sciences and Technology Tunisia Lissi Ea 3956 Laboratory Senart Institute of Technology Lieusaint France

ISBN: (纸本)9798350344158

A major problem in a crowd evacuation is rescuing frightened and trapped people. This paper presents a decision-making process based on a combination of individual and social collaborative reasoning to evacuate people during a crowd evacuation. Specifically, we propose to evacuate crowds by modeling the social behaviors of multi-agent systems. In this context, collaborative reasoning contributes to finding data on emergency exits. Collaborative reasoning allows agents to exchange information and reach an agreement to exit quickly and safely. Experimental results showed that collaborative reasoning combined with individual reasoning has a great impact on the final decision and can lead to a successful evacuation with a percentage of 90. © 2023 IEEE.

关键词： Multi agent systems

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Optimization of Resource Allocation for Detection of Software Architectural Defects 18

Optimization of Resource Allocation for Detection of Softwar...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Tandon, Stuti Kumar, Vijay Singh, V.B. Amity University Amity Institute of Information Technology Uttar Pradesh Noida India Amity University Amity Institute of Applied Sciences Department of Mathematics Uttar Pradesh Noida India Jawaharlal Nehru University School of Computer and Systems Sciences New Delhi India

ISBN: (纸本)9789380544519

Software defects which bring about depreciation in quality of software without alteration in the working of the software are known as 'Code smells'. The serious outcomes of these flaws motivated researchers to study code smells with the objective of optimization of the resources. We aim to conduct this study on architectural category of the code smells. This category is claimed to have the potential of critically hampering the quality of the code. The aim of the study is to investigate the efforts made by the development team in resolving the architectural issues of the software. Here, we propose a mathematical model in order to help the software managers, to optimize their resource allocation. Since architectural category of code smell is claimed by many researchers to be the most crucial one, hence implementation of this study is considered to increase the profit margins by increasing the efficiency of the software. The proposed model is driven by the real time dataset targeting the architectural category of smells. This study may benefit corporate firms which many a time struggle to optimize the resource allocation on several projects. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Architectural Code smells Architectural Smells Mathematical Modelling Optimization Resource Allocation Static Code Analysis

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Microsnoop: A generalist tool for microscopy image representation

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The Innovation 2024年第1期5卷 109-117页

作者： Dejin Xun Rui Wang Xingcai Zhang Yi Wang Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China State Key Lab of Computer-Aided Design&Computer Graphics Zhejiang UniversityHangzhou 310058China John A.Paulson School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02138USA Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University Hangzhou 310018China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314100China

Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological ***,we present Microsnoop,a novel deep learning–based representation tool trained on large-scale microscopy images using masked self-supervised *** can process various complex and heterogeneous images,and we classified images into three categories:single-cell,full-field,and batch-experiment *** benchmark study on 10 high-quality evaluation datasets,containing over 2,230,000 images,demonstrated Microsnoop’s robust and state-ofthe-art microscopy image representation ability,surpassing existing generalist and even several custom *** can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal ***,Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing *** will regularly retrain and reevaluate the model using communitycontributed data to consistently improve Microsnoop.

关键词： hardware image representation

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Custom Hidden Markov Models for Effective Part-of-Speech Tagging 18

Custom Hidden Markov Models for Effective Part-of-Speech Tag...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Khalid, Hassan Bin Siddique, Abu Bakar Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain. © 2023 IEEE.

关键词： Hidden Markov models

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Efficient Parameter Mining and Freezing for Continual Object Detection

arXiv

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arXiv 2024年

作者： Menezes, Angelo G. Peterlevitz, Augusto J. Chinelatto, Mateus A. de d Carvalho, André C.P.L.F. Institute of Mathematics and Computer Sciences University of São Paulo São Carlos Brazil Computer Vision Department Eldorado Research Institute Campinas Brazil

Continual Object Detection is essential for enabling intelligent agents to interact proactively with humans in real-world settings. While parameter-isolation strategies have been extensively explored in the context of continual learning for classification, they have yet to be fully harnessed for incremental object detection scenarios. Drawing inspiration from prior research that focused on mining individual neuron responses and integrating insights from recent developments in neural pruning, we proposed efficient ways to identify which layers are the most important for a network to maintain the performance of a detector across sequential updates. The presented findings highlight the substantial advantages of layer-level parameter isolation in facilitating incremental learning within object detection models, offering promising avenues for future research and application in real-world scenarios. © 2024, CC BY.

关键词： Object detection

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Empirical Analysis of Machine Learning Classifiers for Architectural Smells Using Critic-Topsis Approach 3

Empirical Analysis of Machine Learning Classifiers for Archi...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Tandon, Stuti Kumar, Vijay Singh, V.B. Amity Institute of Information Technology Amity University Uttar Pradesh Noida India Amity Institute of Applied Sciences Amity University Department of Mathematics Uttar Pradesh Noida India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (纸本)9798331518592

Code Smells are resultants of poor coding techniques adopted by programmers which not only depreciates the quality of the software but also makes it tedious to maintain. We report best Machine learning classifier for the detection of the most severe smell of the Architectural category. The architectural category needs further investigation thereby this category of smell is considered. The most severe architectural smell is analyzed to rank eight Machine Learning (ML) classifiers. Eight performance measures of the ML classifiers are used during the study. Criteria Importance Through Inter Criteria Correlation and Technique for Order Preference by Similarity to an Ideal Solution (CRITICTOPSIS) method is used to analyze the performance measures of the classifiers for the severe architectural smell which are then ranked. J48 classifier is reported to be the best classifier for the analysis of the Feature Concentration smell of the architectural category. © 2024 IEEE.

关键词： Contrastive Learning

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Wordle as a Teaching Tool

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PRIMUS 2023年第8期33卷 901-915页

作者： Ritchey, Katherine Patterson, Sarah Patterson, Blain Ritchey, Nathan University of Mount Union Mathematics and Computer Science Department United States Virginia Military Institute in the Applied Mathematics Department United States Applied Mathematics at Virginia Military Institute United States Kent State University in the Department of Mathematical Sciences United States

Wordle, a game in which a player has six chances to find a five-letter word, has become quite popular. In this paper, we present four activities that are based on Wordle and are designed to help students advance their knowledge and skill of a particular concept. Some of the activities could be used for beginning students and some activities could be used for more advanced students. The activities are designed to promote mathematical engagement and interest and can be used individually, or collectively, to fit the specific needs of the intended audience. The first two activities enhance students' knowledge of combinatorics and probability when letter words are chosen at random. In another activity, individual and pairs of letter counts from the list of possible Wordle words are presented in graphical format. More advanced students can replicate these graphs using a computer program, while other students can simply use the graphs that are included. In the fourth activity, students have the opportunity to play Wordle and develop strategies to minimize the number of steps required to solve a particular puzzle. Classroom experience is included and student responses to the activities are presented. © 2023 Taylor & Francis Group, LLC.

关键词： combinatorics decision making mathematical modeling probability statistics Wordle

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Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App 18

Revolutionizing Campus Exploration with GikiLenS: A Deep Lea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mueed, Iqra Arshad, Usama Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing 'GikiLenS', an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure. © 2023 IEEE.

关键词： Object detection

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Optimal Logistic Map with DNA Computing Model for Image Encryption 2

Optimal Logistic Map with DNA Computing Model for Image Encr...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Shiny, K.V. Wilfred Blessing, N.R. Jino, R. Palarimath, Suresh Mathumitha, V. Sutherlin Subitha, G. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Oman Department of Civil Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil nadu Kanyakumari District India

ISBN: (纸本)9798331542573

The logistic map with optimization with a novel Deoxyribonucleic Acid (DNA) sequence operation-based novel image encryption scheme is generated. The best mask is obtained by enhancing the excellence of DNA which is the significant advantage of this approach. According to the image decryption and encryption process, correct errors and the similarity of DNA sequences are decreased by the DNA cryptogram becoming an input pace for DNA calculation. The maximum PSNR of encrypted images is obtained by the logistic map function to improve the performance of image security Ant Lion optimization (ALO). The image encryption and decryption process generate optima point DNA sequence rules. DNA coding: utilizing the complementary rule, each nucleotide is then changed into its base pair for a random time or times;the times are produced by Chebyshev maps. The experimental investigations provided better UACI, Number of Pixel Exchange Rate (NPCR), and Peak Signal Noise Ratio (PSNR). This strategy is significant because it increases entropy, which is the fundamental property of randomness, withstands multiple statistical and differential attacks, and produces positive experimental outcomes. © 2024 IEEE.

关键词： Signal to noise ratio

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