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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Jain, Garima Shukla, Amita Bairwa, Nitesh Kumar Chaudhary, Anamika Patel, Ashish Jain, Ankush Noida Institute of Engineering and Technology Department of Computer Science and Buisness Systems Greater Noida India Noida Institute of Engineering and Technology Department of Artificial Intelligence Greater Noida India Noida Institute of Engineering and Technology Department of Data Science Greater Noida India Dronacharya Group of Institutions Department of Artificial Intelligence and Machine Learning Greater Noida India Netaji Subhash University of Technology Department of Computer Science and Enginnering Delhi India

ISBN: (纸本)9798331540364

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services. © 2024 IEEE.

关键词： Speech recognition

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Prediction of Alzheimer’s Disease Using Adaptive Fine-Tuned Deep Resnet-50 with Attention Mechanism

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SN Computer science 2024年第4期5卷 392页

作者： Venkatesh, R. Anantharajan, Shenbagarajan Gunasekaran, Shenbagalakshmi Yogaraja, C.A. Gethzi Ahila Poornima, I. Computer Science and Engineering Ramco Institute of Technology Rajapalayam Tamil Nadu Virudhunagar 626117 India Artificial Intelligence and Data Science Mepco Schlenk Engineering College Sivakasi Tamilnadu Virudhunagar 626005 India Computer Science and Engineering Mepco Schlenk Engineering College Sivakasi Tamilnadu Virudhunagar 626005 India

Alzheimer’s disease (AD) sufferers can benefit from early detection and diagnosis so that protective treatments can be taken before permanent brain impairment develops. Magnetic resonance imaging (MRI)-detected lesion sites in AD patients exhibit significant heterogeneity and these characteristics are spread throughout the imaging space, according to cross-sectional imaging studies of Alzheimer's disease. Because conventional Deep Learning (DL) methods can’t generate enough long-distance correlations in the characteristic space, adaptive fine-tuned deep resnet-50 with attention mechanism (AFDR-AM) is introduced in this research for the early identification of AD. The input MRI data is de-noised using a wavelet thresholding approach, and the contrast is enhanced using Improved Color Histogram Equalization (ICHE). Image recognition and classification characteristics are extracted at the feature extraction phase using a Gabor Filter Bank (GFB). To narrow down a big list of traits, the artificial Bee Colony Optimization (ABCO) technique can be used to pick the most useful ones and get rid of the rest. Measures of accuracy, precision, recall, f1-score, specificity, and MSE are used to evaluate the effectiveness of the suggested strategy. Comprehensive experiments reveal that our approach achieved the best performance compared to other approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： AFDR-AM Alzheimer’s disease (AD) artificial Bee colony optimization (ABCO) Deep learning (DL) Gabor filter bank (GFB) Improved color histogram equalization (ICHE) Magnetic resonance imaging (MRI)

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Question Categorizer: An Automated Approach For Organizing Queries 2

Question Categorizer: An Automated Approach For Organizing Q...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Roy, Naina Upadhyay, Khushi Doshi, Payal Nagarhalli, Tatwadarshi P. Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Mumbai India Prime SofTech Solutions Pvt. Ltd. Pune India

ISBN: (纸本)9798350365269

Question categorization is an essential task in natural language processing (NLP) and information retrieval which is the process of automatically assigning a category or class label to a given question based on its content. Categorizing questions plays a pivotal role in enhancing comprehension and facilitating targeted responses. This paper delves into the development and implementation of an advanced question categorization system which classifies questions into eight distinct categories: Analytical, definition, Theoretical, Inferential, Formulation, Differential, Mathematical and Statistical thereby encompassing a wide spectrum of question types. The foundation of this system lies in the integration of a sophisticated BERT architecture, natural language processing techniques, and domain-specific expertise. The system autonomously processes input queries, identifying the underlying characteristics that define each category and assigns the category to the input data. The model is trained on a curated set of data and the proposed methodology achieved promising results with an accuracy score of 0.98 for the 8 categories multiclass classification. © 2024 IEEE.

关键词： Classification (of information)

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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Penetration Testing: A Cost-Benefit Analysis of Best Practices Implementation for Software Startups 6

Penetration Testing: A Cost-Benefit Analysis of Best Practic...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Gaafar, Ahmed Ali Fouad, Khaled M. Sadek, Mohamed A. Nile University Faculty of Information Technology and Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Egypt New Mansoura University Faculty of Computer Science and Engineering Egypt Nile University Egypt

ISBN: (纸本)9798350378511

Despite software startups often not handlingsensitive data, the implementation of robust security measures is crucial to mitigate significant financial and reputational risks. This study investigates the cost-benefit analysis of implementing best practices in penetration testing (Pentest) versus notimplementing them, using Roboost as a case study. It emphasizes that proactive security investments not only protect current assets but also prepare organizations for future growthThe research employs a mixed-methods approach, combining quantitative analysis of financial data with qualitative insights from stakeholder interviews. The findings indicate that while the initial costs of hiring penetration testers and implementing security protocols are substantial, the benefits far outweigh these expenses. Securing new clients and retaining existing ones, which constitute a significant portion of Roboost' s revenue, underscores the financial prudence of these investments. Our analysis demonstrates that the costs of not implementing security practices, such as potential losses from security breaches, are substantially higher than the investments in proactive measures. This underscores the critical importance of embedding security best practices within organizational frameworks to ensure long-term sustainability and success. © 2024 IEEE.

关键词： Decentralized finance

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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Computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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Enhancing Early Detection of Pancreatic Cancer: A Machine Learning Approach with Explainable AI Insights

Enhancing Early Detection of Pancreatic Cancer: A Machine Le...

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2024 IEEE International Conference on Computer Vision and Machine intelligence, CVMI 2024

作者： Pattanashetti, Neeraj V Mayur, R. Prakash, Adarsh Adiga, Tryambaka B Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Information Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350376876

Among the most deadly and complex illnesses in the world, pancreatic cancer is typified by its early asymptomatic stage and subsequent discovery of the disease when it has progressed to an advanced stage. Getting the patients in a correct treatment path at the earliest point is important for suitable intervention and patient outcomes in the long run. We utilized advanced algorithms and model prediction techniques includes, feature extraction, hyperparameter tuning and explainability methods which aims to enhance both the comprehension and predictive accuracy of models employed. The goal is to assess patient health status and diagnose pancreatic diseases (cancer vs. non-cancer) by developing a learning algorithm. We obtained a dataset containing demographic information and urine biomarkers to start the investigation. First stage of our analysis employs a single classifier and later we apply bagging and boosting algorithms to achieve ensemble models. Hyperparameter fine-tuning has got a positive outcome, which has Gradient Boosting and Cat Boosting as its top performing models. The ensemble model produced excellent results (92.02 %), demonstrating the usefulness of the suggested approach. LIME and SHAP techniques play the key role in model outcome interpretability providing an insight of algorithmic decision-making process. Such a wide range of combinations of biomarkers have a potential of pancreatic cancer detection in early stages and thus will help shape diagnostics strategies in near future. © 2024 IEEE.

关键词： Diseases

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BAnDIT: Business Process Anomaly Detection in Transactions 29th

BAnDIT: Business Process Anomaly Detection in Transactions

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29th International Conference on Cooperative Information Systems, CoopIS 2023

作者： Rudolf, Nico Böhmer, Kristof Leitner, Maria Faculty of Computer Science Research Group Workflow Systems and Technology University of Vienna Währinger Straße 29 Vienna Austria Faculty of Informatics and Data Science Chair of Artificial Intelligence in IT Security University of Regensburg Regensburg Germany

ISBN: (纸本)9783031468452

Business process anomaly detection enables the prevention of misuse and failures. Existing approaches focus on detecting anomalies in control, temporal, and resource behavior of individual instances, neglecting the communication of multiple instances in choreographies. Consequently, anomaly detection capabilities are limited. This study presents a novel neural network-based approach to detect anomalies in distributed business processes. Unlike existing methods, our solution considers message data exchanged during process transactions. Allowing the generation of detection profiles incorporating the relationship between multiple instances, related services, and exchanged data to detect point and contextual anomalies during process runtime. To validate the proposed solution, it is demonstrated with a prototype implementation and validated with a use case from the ecommerce domain. Future work aims to further improve the deep learning approach, to enhance detection performance. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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Multi-trial Vector-based Whale Optimization Algorithm

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Journal of Bionic engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： Swarm intelligence algorithms Metaheuristic algorithms Optimization engineering design problems Whale optimization algorithm

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An IoT Intrusion Detection Approach Based on Salp Swarm and artificial Neural Network

An IoT Intrusion Detection Approach Based on Salp Swarm and ...

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作者： Alzubi, Omar A. Alzubi, Jafar A. Qiqieh, Issa Al-Zoubi, Ala' M. Faculty of Artificial Intelligence Al-Balqa Applied University Salt Jordan Faculty of Engineering Al-Balqa Applied University Salt Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

The Internet of Things has emerged as a significant and influential technology in modern times. IoT presents solutions to reduce the need for human intervention and emphasizes task automation. According to a Cisco report, there were over 14.7 billion IoT devices in 2023. However, as the number of devices and users utilizing this technology grows, so does the potential for security breaches and intrusions. For instance, insecure IoT devices, such as smart home appliances or industrial sensors, can be vulnerable to hacking attempts. Hackers might exploit these vulnerabilities to gain unauthorized access to sensitive data or even control the devices remotely. To address and prevent this issue, this work proposes integrating intrusion detection systems (IDSs) with an artificial neural network (ANN) and a salp swarm algorithm (SSA) to enhance intrusion detection in an IoT environment. The SSA functions as an optimization algorithm that selects optimal networks for the multilayer perceptron (MLP). The proposed approach has been evaluated using three novel benchmarks: Edge-IIoTset, WUSTL-IIOT-2021, and IoTID20. Additionally, various experiments have been conducted to assess the effectiveness of the proposed approach. Additionally, a comparison is made between the proposed approach and several approaches from the literature, particularly SVM combined with various metaheuristic algorithms. Then, identify the most crucial features for each dataset to improve detection performance. The SSA-MLP outperforms the other algorithms with 88.241%, 93.610%, and 97.698% for Edge-IIoTset, IoTID20, and WUSTL, respectively. © 2024 John Wiley & Sons Ltd.

关键词： Network intrusion

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