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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Ghosh, Sambhram Thenmozhi, M. School of Computing College of Engineering and Technology Department of Networking and Communications SRM Nagar Kattankulathur Tamil Nadu Chengalpattu District 603203 India

ISBN: (纸本)9798350349900

Ayurveda has been an integral part of ancient Indian medicine and it still plays an important role in the creation of Homeopathic medicines. In this paper we have introduced a comprehensive system for detecting, grading, and classifying Ayurveda Herbs by taking images as input and analyzing those images using two Deep Learning Algorithms CNN and MobileNet. The proposed system employs advanced technology to accurately identify different categories and classes of Ayurvedic Herbs, utilizing image processing and machine learning techniques for classification and gradation using Deep Learning Algorithms like CNN and MobileNet. Additionally, it stores a pre-trained uses and benefits data for each and every Ayurvedic Herbs Class and displays the information based on the class of Ayurveda Herbs detected. © 2024 IEEE.

关键词： Adversarial machine learning

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Detecting Brain Cancer Using Explainable AI 7

Detecting Brain Cancer Using Explainable AI

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7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Rahman, Abdul Sohail, Shahab Saquib Alam, Mohammad Sultan Sharma, Anil Mansoor, Wathiq VIT Bhopal University School of Computing Science Engineering and Artificial Intelligence MP Sehore466114 India VIT Bhopal University School of Computing Science and Engineering MP Sehore466114 India Amity School of Engineering & Technology UP Noida201313 India University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates

ISBN: (纸本)9798350368673

The accurate and early diagnosis of brain tumors that is one of the deadly cancers may save many humans. Proposed research focused on primary types of tumors detection (i.e. gliomas, meningiomas, and pituitary tumors). Further, the research is meticulous in fine-tuning the model's parameters - including the loss function, optimizer, learning rate, epochs, and batch size - to optimize performance and achieve high accuracy in tumor detection. While the findings of this work mark a significant advancement in medical imaging and brain tumor detection, it acknowledges the prevailing limitations, such as the constraints imposed by hardware resources and the challenges of model generalization across diverse and more extensive datasets. Recognizing these limitations, the work sets the stage for future research, emphasizing the exploration of larger and more heterogeneous datasets, the adoption of more sophisticated deep learning models, with the aim of enhancing the precision, reliability, and clinical applicability of brain tumor detection methodologies. In essence, this research not only contributes to the field of medical imaging analysis but also represents a transformative step towards the realization of more accurate, timely, and personalized healthcare solutions, powered by the advancements in artificial intelligence and machine learning. © 2024 IEEE.

关键词： Lung cancer

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Recurring Multi-layer Moving Window Approach to Forecast Day-ahead and Week-ahead Load Demand Considering Weather Conditions

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1552-1562页

作者： Dao H.Vu Kashem M.Muttaqi Ashish P.Agalgaonkar Arian Zahedmanesh Abdesselam Bouzerdoum School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongAustralia Information and Computing Technology Division College of Science and EngineeringHamad Bin Khalifa UniversityDohaQatar IEEE

The incorporation of weather variables is crucial in developing an effective demand forecasting model because electricity demand is strongly influenced by weather *** dependence of demand on weather conditions may change with time during a ***,the time stamped weather information is *** this paper,a multi-layer moving window approach is proposed to incorporate the significant weather variables,which are selected using Pearson and Spearman correlation *** multi-layer moving window approach allows the layers to adjust their size to accommodate the weather variables based on their significance,which creates more flexibility and adaptability thereby improving the overall performance of the proposed ***,a recursive model is developed to forecast the demand in multi-step *** electricity demand data for the state of New South Wales,Australia are acquired from the Australian Energy Market Operator and the associated results are reported in the *** results show that the proposed approach with dynamic incorporation of weather variables is promising for day-ahead and week-ahead load demand forecasting.

关键词： Autoregressive(AR)model load forecasting multi-layer moving window Pearson correlation Spearman correlation

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ML assisted enhanced recommendation system for reliable volunteering environment 2

ML assisted enhanced recommendation system for reliable volu...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Prabakaran, N. Meenakshi, K. Kumarnath, J. Rajasekaran, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Mathematics Presidency University Bengaluru560064 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul624005 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu603203 India

The coronavirus pandemic has negatively impacted everyone around the globe. The need to follow social distancing has increased the demand for technology that makes remote volunteering accessible. Alerting, aiding, and educating individuals at risk in a privacy-preserving manner are the need of the hour. Several NGOs have taken up this responsibility but are lacking the resources to reach willing volunteers and donations. This project attempts to make a novel on-demand service application using ID3, CART, and C4.5 decision tree classification algorithms and compare their accuracies. Algorithm C4.5 here we use as a Decision Tree classifier that can be make decisions based on univariate or multivariate predictor. C4.5 Algorithm builds decision trees based on the conception of information gain, with the decision of each classification associated with the target classification. The best way to determine uncertainty is to use entropy that reflects the basis of decision. We aim to classify volunteer data based on predicted volunteer reliability. C4.5 algorithm shows 11.63 higher accuracy when compared to other algorithm and the value of 0.36 Gini index for the C4.5 indicates that there is an adequate equality. It is conferred that the C4.5 algorithm will best solve this problem based on the required dataset that we have created. © 2024 Elsevier B.V.. All rights reserved.

关键词： Decision trees

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Adaptive Attribute-Based Honey Encryption: A Novel Solution for Cloud Data Security

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Computers, Materials & Continua 2025年第2期82卷 2637-2664页

作者： Reshma Siyal Muhammad Asim Long Jun Mohammed Elaffendi Sundas Iftikhar Rana Alnashwan Samia Allaoua Chelloug School of Computer Science and Engineering Central South UniversityChangsha410083China EIAS LAB College of Computer and Information Sciencesand Center of Excellence in Quantum and Intelligent ComputingPrince Sultan UniversityRiyadh11586Saudi Arabia Gianforte School of Computing Montana State UniversityBozemanMT 59717USA Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.

关键词： Cybersecurity data security cloud storage hadoop encryption and decryption privacy protection attribute-based honey encryption

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AyurChat: Empowering Health through AI-Driven Ayurvedic Guidance 15

AyurChat: Empowering Health through AI-Driven Ayurvedic Guid...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Ahmed, Syed Adeel Gahlawat, Gautam Imam, Fahad Saranya, G. College of Engineering and Technology S.R.M Institute of Science and Technology School of computing Department of Networking and Communications Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350370249

AyurChat is a project aiming to transform healthcare discussions by blending intelligence (AI) with the wisdom of Ayurveda. The innovative chatbot system, known as AyurChat acts as a health companion offering personalized advice, for medical queries. The main goal of AyurChat is to empower individuals to manage their health through natural solutions rooted in Ayurvedic principles. By utilizing cutting edge language processing and machine learning techniques, AyurChat engages in conversations with users understanding their health concerns and providing tailored guidance based on Ayurveda. This system aims to improve access to healthcare by bridging the gap between technology and traditional healing practices. With a ranging knowledge base covering Ayurvedic remedies AyurChat ensures thorough and precise responses to user's health inquiries. Through AI driven algorithms evolves over time, enhancing its grasp of user needs and refining its recommendations continuously. AyurChat stands as an endeavor that uses AI to make Ayurvedic healthcare knowledge more accessible. Offering discussions and personalized advice it provides users with treatments from the realm of Ayurveda rather than conventional Western medicine along with web links for alternative medicines, like Siddha and Unani. © 2024 IEEE.

关键词： Medicinal chemistry

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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Computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

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Intelligent Automation & Soft computing 2023年第8期37卷 1499-1512页

作者： Wenlong Ma Youhong Xu Jianwei Zheng Sadaqat ur Rehman School of Information Engineering Quzhou College of TechnologyQuzhou324000China School of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China Department of Natural and Computing Science University of AberdeenScotlandAberdeenAB243FXUK

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is ***-mizing the plethora of cloud services has thus become a top *** ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some *** resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantiﬁcation *** data are assessed by an information correction *** weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service *** manufacturing cloud service optimization algorithm thus provides a quantitative reference for service *** experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

关键词： Cloud manufacturing quality of service optimization algorithm rough set

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Pattern-Based Trading by Continual Learning of Price and Volume Patterns 37th

Pattern-Based Trading by Continual Learning of Price and ...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Liston, Patrick Gretton, Charles Lensky, Artem The Australian National University College of Engineering Computing and Cybernetics Canberra Australia School of Engineering and Technology The University of New South Wales CanberraACT Australia School of Biomedical Engineering Faculty of Engineering The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819603473

Automating trading decisions has been a pursuit of researchers and practitioners alike for decades. We contribute to the literature focusing on "pattern based" strategies. Dynamic time warping is used to group similar patterns into a representative category, while the method of continual learning augmentation is used to maintain the set of patterns used for decision-making. Thus, we implement a novel approach to pattern-based trading, utilising adaptive memory structures to enable adaptability of agent decision making and overall agent performance. Two new online pattern-based trading agents are introduced and tested on two-sets of historical cryptocurrency data, for the BTCUSDT pair over the periods of 2017–2023 and 2023–2024. We compare our newly formulated agents against an established baseline of rule-based agents, thereby comparing the relative profit generating abilities of a wide range of agents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Marketplaces

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Improving Quality of Service Using a Innovative Energy Effective Reinforcement Based Path Finding in Mobile Ad Hoc Network 2

Improving Quality of Service Using a Innovative Energy Effec...

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

作者： Singaravelan, M. Saleem Raja, A. Thangam, S. School of Computing Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai India College of Computing and information Sciences University of Technology and Applied Sciences Shinas Oman Amrita school of computing Department of computer science and engineering Bengaluru India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

RL stands for reinforcement learning, is a component of the broader field of machine learning. It provides a framework its features help to select an efficient action using previously learned information of interaction and its environment. Mobile nodes are grouped to form a network for communicating themselves termed autonomous network with mobile nodes (MANET). This is a dynamic and independent network requiring no administrative control. The mobile nodes in the network are to be part or apart at any time that causes the topology changes. The frequent topology changes make it hard to determine the destination route. The route discovery in the dynamic topology that consumes more node energy and, minimize the network lifetime. The energy usage and network lifetime are a vital part of the MANET. This paper proposes a new global routing protocol using a reinforcement learning algorithm called modified energy and signal strength Q-learning ad hoc on-demand distance vector algorithm (ESQAODV). This process conducts a result comparison to a local routing algorithm known as AODVSARSA. The proposed algorithm proved a better result with reference to average distribution ratio compared to the other, and complete delay metrics © 2024 IEEE.

关键词： Adversarial machine learning

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