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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Behera, Ajit Kumar Dehuri, Satchidananda Ghosh, Ashish Silicon Institute of Technology Department of Computer Science & Engineering Bhubaneswar India Fakir Mohan University Department of Computer Science Balasore India Indian Statistical Institute Machine Intelligence Unit 203 B.T. Road Kolkata700108 India

ISBN: (纸本)9781665475174

This paper presents a new surrogate-assisted multi-objective genetic fuzzy associative classification model by learning multiple granularities. The specific method is the hybridization of multi-objective genetic algorithms (MOGAs), radial basis function neural networks (RBFNs), and rough set. We show that our approach requires only a few numbers of fitness evaluations compared to the methods proposed in [34] without compromising to maintain an average classification ability in almost all the datasets considered in this work for evaluation of the model. © 2023 IEEE.

关键词： Genetic algorithms

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DATR: Domain Agnostic Text Recognizer 27th

DATR: Domain Agnostic Text Recognizer

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Purkayastha, Kunal Sarkar, Shashwat Palaiahnakote, Shivakumara Pal, Umapada Ghosal, Palash Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India School of Science Engineering and Environment University of Salford Manchester United Kingdom Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Gangtok India

ISBN: (纸本)9783031784460

Recognizing text extracted from multiple domains is complex and challenging because complexities vary from one domain to another. Most existing methods focus either on natural scene text or specific text type but not text of multiple domains, namely, scene, underwater, and drone texts. In addition, the state-of-the-art models ignore the vital cues that exist in multiple instances of the text. This paper presents a new method called the Student-Teacher-Assistant (STA) network, which involves dual CLIP models to exploit cues in multiple text instances. The model that uses ResNet50 in its image encoder is called helper CLIP, while the model that uses ViT in its image encoder is called primary CLIP. The proposed work processes both models simultaneously to extract visual and textual features through image and text encoders. Our work uses cosine similarity for the randomly chosen input image to detect instances similar to the input image. The input and similar instances are supplied to primary and helper CLIPs for visual and textual feature extraction. The outputs of dual CLIPs are fused in a different way through the alignment step for recognizing text accurately, irrespective of domains. To demonstrate the proposed model’s significance, experiments are conducted on a set of standard natural scene text datasets (regular and irregular), underwater images, and drone images. The results on three different domains show that the proposed model outperforms the state-of-the-art recognition models. The datasets and code for public use in training and testing shall be made available on GitHub. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Underwater imaging

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Quantum Quest: Resource Requirement of RC4 Cryptanalysis under Grover's Lens

Quantum Quest: Resource Requirement of RC4 Cryptanalysis und...

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Sen, Sarbani Bajpayee, Soumen Mukherjee, Imon Paul, Goutam Singh, Bhupendra Indian Institute of Information Technology Computer Science & Engineering Kalyani India Indian Statistical Institute Cryptology & Security Research Unit Kolkata India Centre for Artificial Intelligence and Robotics DRDO Bengaluru India

ISBN: (纸本)9798331516666

The RC4 cipher is a widely used software stream cipher which has long stood as a cornerstone in cryptographic applications due to its design simplicity, speed and efficiency. In this paper, we present an approach to decrypt RC4-encrypted data by leveraging Grover's quantum search algorithm. To the best of our knowledge, this is the first documented attempt to employ Grover's algorithm in breaking RC4 encryption. We enumerate the quantum resources required to perform exhaustive key search attack on RC4. Our approach demonstrates the potential of quantum computing to revolutionize cryptanalysis. © 2024 IEEE.

关键词： Encryption algorithms

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Physicians' Experiences of EHR Technical Quality: Results from Four Large Cross-Sectional Surveys in 2010-2021

Physicians' Experiences of EHR Technical Quality: Results fr...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Lääveri, Tinja Viitanen, Johanna Reponen, Jarmo Renko, Jari Aalto University Department of Computer Science Finland Inflammation Center Helsinki University Hospital Finland Research Unit of Health Sciences and Technology University of Oulu Finland Oy Apotti Ab Helsinki Finland

ISBN: (纸本)9781643685335

Unexpected downtime and long response times of electronic health record (EHR) systems not only impact user satisfaction and clinicians' work efficiency but also bring about potential harm for patients. Despite improvements in the performance of EHR systems' architecture, hardware, and networks, technical challenges continue to cause problems. We explored the end-user experiences of EHR technical functionality and quality from four large national cross-sectional surveys conducted among Finnish physicians in 2010-21. The results were analyzed by healthcare sector/specialty groups. In most groups, the experiences of stability and reaction speed became worse in 2010-17, which is readily explained by the implementation of the national patient data repository services, but improvements were seen in 2021, suggesting that EHR vendors have solved at least some of the slowness problems. The proportion of physicians reporting having experienced faulty system function with potential or actualized harm for the patient had decreased in operative and medical specialties and in the private sector but remained stable in other groups. Our findings underline the importance of continuing to develop technical qualities - including the implementations of national integrations. © 2024 The Authors.

关键词： Electronic health record

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IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

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computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： Saeed Masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.Alzahrani Mohammed Maray Sana Alazwari Mohamed A.Shamseldin Mesfer Al Duhayyim Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)*** presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RSO algorithm is exploited for effective selection of subset of ***,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of ***,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

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Optimizing ASD Diagnosis: Enhanced Autism Spectrum Disorder Prediction Using Machine Learning Classifiers 2

Optimizing ASD Diagnosis: Enhanced Autism Spectrum Disorder ...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Al Tawil, Arar Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Marzouk, AbdulRahman S. Hamad, Adam L. Zakaria, Khaled Y. Danial, Moshir A. Nassief, Mounir N. Abdelminaam, Diaa Salama Applied Science Private University Faculty of Information Technology Amman11931 Jordan Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Jadara University Jadara Research Center Irbid21110 Jordan Misr International University Faculty of Computers Egypt Middle East University MEU Research Unit Amman11831 Jordan

ISBN: (纸本)9798350362633

Autism Spectrum Disorder is a multidimensional neurological developmental disorder that includes a wide plethora of mental disorders targeting one's social and behavioral abilities. Gaining a perceptive understanding of a topic like ASD is essential in one's lifespan, whether the symptoms displayed are mild or severely debilitating. Due to the populous size of clinical terms under the disorder's spectrum and the drastic gap between each symptom's severity, the diagnosis of ASD became a long and tribulating task for experts and professionals alike. The purpose of this paper is to present and compare different Machine Learning Classifiers Categorical Boosting, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Naïve Bayes and use their Training Algorithms Train-Test Splitting, K-Folds Cross-Validation to develop a quick and accurate Labeler ASD: 'YES' ? 'NO' for afflicted individuals based on multiple Datasets Generic, Adolescent, Children, Young, Adult, ASD aiming for an early diagnosis and an overall better understanding of the disorder. For each dataset, four Performance Metrics are extracted after applying the 7 ML models and the two training algorithms Accuracy, Precision, Recall, F1-Score. The resulting metrics are then displayed and compared to conclude the most accurate trained model for each dataset and with each of the training algorithms. © 2024 IEEE.

关键词： Diseases

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Migratable AI Systems for Tailoring User Experience through Multimodal Affective-Cognitive State Analysis

Migratable AI Systems for Tailoring User Experience through ...

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2023 International Conference of the Italian Association for Artificial Intelligence Doctoral Consortium, AIxIA-DC 2023

作者： La Monica, Ludovica Institute of Cognitive Science and Technology National Research Council Rome00185 Italy Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome00128 Italy

In the era of digital technologies permeating numerous aspects of society, the development of intelligent artificial agents endowed with autonomy, social capabilities, reactivity, and proactivity has become a pivotal innovation. Traditionally, these agents were confined to singular forms, known as embodiments, which dictated their capabilities and the environments in which they operated. To transcend these limitations and enable agents to expand their capabilities, the concept of agent migration has emerged, allowing them to seamlessly transition between different embodiments. My research program delves into the realm of agent migration, uncovering its challenges and potential opportunities. The central objective is to leverage this technology to deepen our understanding of human affective and cognitive states across various contexts. Specifically, my aim is to develop a migratory agent architecture capable of recognizing and characterizing individuals’ emotional experiences using different sets of capabilities tied to distinct embodiments. In summary, the intent of this research is to harness migratable AI to promote the technical advantages brought about by agent migration and gain insights into how to utilize the acquisition of additional capabilities resulting from migration to craft a more comprehensive and potentially enduring architecture, to be applied in the realm of affective computing. © 2022 Copyright for this paper by its authors.

关键词： Autonomous agents

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Home healthcare: particle swarm optimization for human resource planning under uncertainty

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Multimedia Tools and Applications 2024年 1-34页

作者： Zarrouk, Rim Mahmoudi, Ramzi Bedoui, Mouhamed Hedi Hu, Yu-Chen Medical Imaging Technology Laboratory Faculty of Medicine LTIM-LR12ES06 University of Monastir Monastir5019 Tunisia Gaspard-Monge Computer-science Laboratory Paris-Est University Mixed Unit CNRS-UMLV-ESIEE UMR8049 ESIEE Noisy Le Grand Paris City Descartes93162 France Department of Computer Science Tunghai University Taichung City Taiwan

The home healthcare (HHC) staff scheduling problem involves allocating care tasks to healthcare staff at a minimal cost, subject to healthcare service requirements, labor law, organizational requirements, staff preferences, and other restrictions. Healthcare service providers strive to meet the time window restrictions specified by patients to improve their service quality. Nevertheless, many of those models are deterministic and neglect to take into account the system’s dynamic and uncertain characteristics, as well as the unpredictability of particular patient data. This study proposes a Two-Level particle swarm optimization variant (2LPSO) for solving the HHC staff’s scheduling problem with Staff Workload Equalization and Uncertainty management while avoiding overtime work. This paper provides a solution for good planning within reasonable computation times with the treatment of two cases: patient cancellation and new patient requests. The computational results obtained in this study demonstrate the efficiency and effectiveness of our approach. Overall, the proposed optimization structure achieves robust scheduling performance so that staff work schedules are balanced to meet the time window restrictions specified by patients to improve their quality of service by taking into account a new patient request or an incoming cancellation request. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Particle swarm optimization (PSO)

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Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

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Journal of Bionic Engineering 2023年第3期20卷 1263-1295页

作者： Jeffrey O.Agushaka Absalom E.Ezugwu Oyelade N.Olaide Olatunji Akinola Raed Abu Zitar Laith Abualigah School of Mathematics Statisticsand Computer ScienceUniversity of KwaZulu-NatalKing Edward AvenuePietermaritzburg CampusPietermaritzburg3201KwaZulu-NatalSouth Africa Department of Computer Science Federal University of LafiaLafia950101Nigeria Unit for Data Science and Computing North-West University11 Hoffman StreetPotchefstroom2520South Africa Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044Abu DhabiUnited Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia

This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

关键词： Improved dwarf mongoose Nature-inspired algorithms Constrained optimization Unconstrained optimization Engineering design problems

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Harnessing Deep Learning for Effective Extractive Text Summarization: A Comparative Study 2

Harnessing Deep Learning for Effective Extractive Text Summa...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Alsekait, Deema Mohammed Almanaseer, Waref Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Baligh, Salma Alaa, Hana Hegazy, Jasmine Magdy, Julia Eldmrat, Alaa Tariq Abdelminaam, Diaa Salama Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information Technology Saudi Arabia Applied Science Private University Faculty of Information Technology Amman11931 Jordan Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Midocean University Faculty of Human Sciences United Arab Emirates Jadara University Jadara Research Center Irbid21110 Jordan Misr International University Egypt MEU Research Unit Middle East University Faculty of Computers Amman11831 Jordan

ISBN: (纸本)9798350362633

Text Summarization might seem like a trivial problem, but in reality, is essential for data acquisition and understanding by compressing large amounts of text into specific a nd to-the-point summaries. This research is conducted to help compare the proposed methods' applications and make a summary system accessible to applications such as information retrieval, legal documents summarization and much more. from this, we intend to start by acquiring the dataset, preprocessing and training, and testing the models. In this paper will explore the methods that can be applied on summarization. These methods are Machine learning models like the random forest, naive Bayes, SVM, logistic regression, Deep learning like a neural networks, Transformers-based models like T5, and performance metrics like ROUGE. By trying all these various methods and models on a news article dataset we are going to compare the results from each of them and get the best performance. Finally, the results would be a comparison between all outputs and found that Natural Language Processor. In conclusion, Text Summarization is best done using NLP, and challenges in this project would be due to the diversity of text and context. © 2024 IEEE.

关键词： Contrastive Learning

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