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3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Yue, Lei Wu, Tao Shen, Yang Zhang, Jin School of Computer and Communication Engineering Changsha University Of Science and Technology Changsha China College of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350365443

'Memory hot pages' usually refer to memory pages that are frequently accessed within a period of time. Using faster memory pages for these hot pages can optimize application performance. Using 'fast memory' for hot pages involves data placement and migration issues on heterogeneous memory systems, and the synthesis and splitting of huge pages in ordinary systems. The premise for implementing these operations is to detect hot pages. This article briefly describes several advanced memory hot page detection methods and the principles of the corresponding memory management mechanism, and discusses multiple aspects such as hot page detection accuracy, overhead, and performance improvement of the corresponding memory management mechanism. © 2024 IEEE.

关键词： Memory management

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A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Class Breast Cancer Classification 2

A Fine-Tuned DenseNet-Based Diagnosis Approach for Multi-Cla...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Kansal, Kanika Kansal, Kajal Department of Computer Science and Engineering Data Science ABES Engineering College Uttar Pradesh Ghaziabad India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530389

Breast cancer is a prevalent tumor across women and is associated with a high mortality rate. Prompt diagnosis is one of the biggest challenges that needs to be addressed globally, as it can considerably improve survival rates. An automatic diagnosis system based on deep learning models is critical for improving detection, prediction accuracy, and survivability. This study used various DenseNet-based architectures for multi-class classification using the BUSI dataset. The experiments indicate that the fine-tuned DenseNet201 model outperforms with an accuracy of 98.28%. The results demonstrate the promising capability of the fine-tuned DenseNet201 model to have a high potential for further implementation in practice for achieving early diagnosis and highly individualized approaches for better treatment. The further development of extensive and diverse data sets will be essential for developing new diagnostic methods and improving the existing ones. © 2025 IEEE.

关键词： Lung cancer

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Jointly Estimation of Microstructure Maps Across Acquisition Protocols

Jointly Estimation of Microstructure Maps Across Acquisition...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Tenglong Yu, Jiahao Pan, Yiang He, Jianzhong He, Yifei Wu, Ye Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Zhejiang University of Technology College of Information Engineering Hangzhou China

ISBN: (纸本)9798350386226

Diffusion MRI (dMRI) allows for examining microarchitecture profiles and tissue changes using specific microstructure modeling but depends on advanced acquisition protocol and well-established biological model assumptions. It is also limited in clinical applications due to lengthy acquisition time and dense q-space sampling requirements. In this study, we present a method that aims to estimate multiple functional microstructural parameters robustly, thereby enhancing clinical applications with different acquisition protocols. Instead of learning the mapping between dMRI and microstructure maps directly, our approach learns a set of explainable over-completed tissue-compartment kernels between parameterized spherical mean signal and widely-used microstructure maps and apply into the joint characterization of multiple biological models. We validate the effectiveness of our method using an in vivo dataset across different health conditions collected by a clinical scanner. This work sets the foundation for future techniques that integrate clinical data acquisition protocols and enhance sensitivity to microstructure measures. The code is provided at https://***/1nlandempire/JEMAP. © 2024 IEEE.

关键词： Tissue

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Detection of Distributed Denial of Service in the Application Layer of Iot Using Machine Learning 2

Detection of Distributed Denial of Service in the Applicatio...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Nanthiya, D. Manjula Devi, R. Gopal, S.B. Madhumitha, C. Ramya Sree, N. Kowsika, D.V. Kongu Engineering College Department of Computer Technology Perundurai India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

Cybersecurity prevents harmful assaults on computers, networks, and mobile devices. Included in the category of cyber-attacks are threats like as malware, distributed denial of service assaults, phishing, spoofing, code injection, and supply chain attacks. Massive denial-of-service assaults are destructive. As part of a Distributed Denial-of-Service (DDoS) assault, hackers bypass botnets and go straight for the server. In order to temporarily block or suspend a website, these botnets send a flood of requests to the server. The server typically takes requests and returns responses. There will be a brief outage if the server gets more requests than it can handle. While this attack makes the service inaccessible, it prevents data theft. A machine-learning approach is used to detect server-side DDoS assaults. The term "machine learning"refers to software that can learn new tasks and solve old ones without any input from a human. Learning Vector Quantization, Support Vector Machine, Naive Bayes, K-Nearest Neighbour, Random Forest, and Decision Tree are the six ML classification models used since these ML procedures enhance accuracy. The main goal of this research is to use the CIDAD dataset to find ways to protect the application layer of the Internet of Things (IoT) against Distributed Denial of Service (DDoS) attacks. The lower layers of the IoT network architecture are disregarded by this dataset, which comprises information at the COAP level. © 2024 IEEE.

关键词： Denial-of-service attack

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BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

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Frontiers of computer science 2023年第5期17卷 103-118页

作者： Jinwei LUO Mingkai HE Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China

Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e

关键词： session-based recommendation graph neural network heterogeneous behaviors

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Design and Analysis of a Multipath Routing Protocol to Enhance QoS in MANET 1

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Saraswat, Devdas Khan, Nikhat Raza Department of Computer Science and Engineering IES University Bhopal India Department of Computer Science and Engineering IES College of Technology Madhya Pradesh Bhopal India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

A combination of their ability to adapt to a broad spectrum of applications and their ability for self-organization, mobile ad hoc networks, or MANETs, have attracted much interest. However, the fundamental characteristics of MANETs—such as limited resources and node mobility—present challenges for ensuring reliable communication and maintaining Quality of Service (QoS). The study presents the Multipath Routing Protocol (MRP) designed for enhancing QoS in MANETs with the goal of getting around these issues. The recommended MRP produces multiple inconsistent routes among source and destination nodes using the dynamic topology of MANETs the goals of this multiplepath’s strategy are to enhance throughput, reduce packet loss, improve network reliability, and minimize the effects of node movement and link failure. For determining the most suitable routes for the transfer of data, the protocol utilizes a dynamic routing method that takes into account multiple factors such as node energy levels, bandwidth at hand, and link quality. For the purpose of determining the MRP’s efficiency, thorough simulations have been carried out utilizing an established network simulation environment. There are many QoS measurements that are important, such as packets transferred, received, the proportion of delivered packets of data, and the usual routing load. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mobile ad hoc networks

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Ensuring purity, precision and consumption in every drop of water with IoT technology 4

Ensuring purity, precision and consumption in every drop of ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Gomathi, S. Devi, D. Srinidhi, A. Shanthini, P. Sri Sairam Engineering College Department of Information Technology Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

In a world where access to clean and pure water is paramount, ensuring its quality and fair distribution is of utmost importance. Traditional water purification methods often fall short in providing real-time insights into water purity, leading to potential health risks. Moreover, the equitable distribution of water resources in shared residential spaces remains a challenge, often resulting in unfair billing practices. To address these issues, we present an innovative solution leveraging IoT technology. Our system comprises sensors measuring critical parameters such as pH, turbidity, ultrasonic, and dissolved oxygen, which are connected to an Arduino Mega board. This setup continuously monitors the quality of water stored in reservoirs managed by water cooperatives. Upon detecting any deviation from predefined purity standards, the system triggers alerts via SMS using a SIM 900 A module, ensuring prompt action by relevant authorities to maintain water quality. Furthermore, we propose the installation of individual water meters in each household within residential complexes. These meters accurately track daily water consumption, empowering residents to pay only for the water they use, eliminating unfair billing practices. Additionally, this initiative aims to raise awareness about water conservation by enabling users to visualize their consumption patterns and encouraging responsible usage. By implementing cost estimation algorithms based on water consumption data, our system facilitates transparent billing processes while enabling authorities to predict future water demands more accurately. Ultimately, this project not only ensures the purity and precision of every drop of water but also promotes equitable distribution and conservation of this precious resource, fostering a sustainable future for all. © 2024 IEEE.

关键词： Health risks

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Improved Alzheimer's Disease Early Prediction using Machine Learning Method : A Comprehensive Diagnostic Approach with Random Forest Classifier 4

Improved Alzheimer's Disease Early Prediction using Machine ...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Munawar, Madiha Monika Singh, T. Kishor Kumar Reddy, C. Hanafiah, Marlia Mohd Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Hyderabad India Universiti Kebangsaan Malaysia Faculty of Science and Technology Malaysia

ISBN: (纸本)9798331515720

Alzheimer's Disease (AD) often affects the elder persons and is the prevalent kind of Dementia. AD has a huge expense, especially when it comes to the treatment. AD is a main reason the older generations die whereas when Alzheimer Disease is detected early there will be minimal deaths and accurate diagnosing likely occurs in most cases. Using anonymized Oasis longitudinal MRI data, the study trains several machine learning algorithms to identify AD patients. Analyzing the test result includes utilizing different learning strategies or algorithms e.g., Ensemble Random Forest (RF), Hierarchical Decision Tree (DT), Logistic Regression (LR), Convolutional Neural Network (CNN), Deep Neural Network binary classifier (DNN), soft voting classifier (SVC), transfer learning approaches (TLA), Support Vector Machine (SVM) and boosting AdaBoost in reference of their Accuracy, Error Rate, AUC (area under the curve), Time Taken and Recall for each method. The obtained results concluded that the proposed Random Forest classifier achieves higher accuracy (92.84%), Error Rate (7.16%), AUC (95%) (Area Under Curve) and Recall (89%). The Random Forest classifier also achieves higher Time Taken that is 483.654 sec. © 2024 IEEE.

关键词： Alzheimer's Disease Decision Trees Deep Learning Dementia Learning Approaches Machine Learning Random Forest

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Skyline Frequent-utility Patterns Mining: A Survey

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第1期8卷 1-16页

作者： Wu, Jimmy Ming-Tai Zhou, Huiying Pirouz, Matin Tayeb, Shahab College of Computer Science and Engineering Shandong University of Science and Technology Qindao China Department of Computer Science California State University Fresno California United States Department of Electrical and Computer Engineering California State University Fresno California United States

High Utility Itemset Mining (HUIM) and Frequent Itemset Mining (FIM) are two important branches in the data mining area, where Frequent Itemset Mining considers itemsets that occur in large numbers in the transaction database, while High Utility Itemset Mining considers the number and unit utility value of itemsets. However, in practical applications, both mining approaches have their own limitations, for example, itemsets with a high number of occurrences may contribute less to the ultimate goal of the merchant, and although these itemsets are strongly related to each other, they do not have great reference value for the final decision. The itemsets with high utility values, on the other hand, may have a very low number of occurrences and weak connections between the itemsets. Therefore recommending these itemsets to users may not be very meaningful. In order to better meet the requirements of real life, some scholars took these two mining algorithms into account, hoping to find those high-quality itemsets, that is, those itemsets with more occurrences and high utility values. For this purpose, the skyline frequent-utility pattern mining (SFUPM) algorithm is introduced. This paper describes in detail the classification of skyline frequent-high uyility itemset mining, mining methods and future research directions. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Data mining

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Automated Underwater Fish Species Recognition using Deep Learning-based Techniques 11th

Automated Underwater Fish Species Recognition using Deep Lea...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Anantha Prabha, P. Sachin, S. Srinithish, U. Deva Priya, M. Karthick, S. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819717231

Knowledge about fish species with continuous monitoring play a dominant role in determining short and long-term effects on ecosystems and generating ways to manage the problem through specific treatment. Categorization and identification of fish species from underwater film are critical for maintaining resources of fisheries and environmental balance. It is difficult to get an accurate result from actual underwater films owing to poor resolution and object motion. Deep Learning (DL) technology is effectively used in a variety of sectors including aquaculture. In order to identify and recognize fishes in the presence of marine background noise, this research offers architecture for fish detection system that includes an updated training model and a multi-scale fish detection module using enhanced Convolutional Neural Network (CNN). The training technique entails gradual training of enhanced CNN by initially focusing on learning tough species followed by gradual learning of new species by incrementally employing knowledge distillation loss while maintaining high performance of previously learned species. This detection model employs a DL strategy based on CNN to improve detection performance on small-sized objects. As a consequence, the issues prevalent in identifying objects underwater are reduced, and performance of the framework is improved. In contrast to current identification methods, the proposed techniques offer better accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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