检索结果-内蒙古大学图书馆

59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Kaushik, Padmanabh Yu, Yih-Choung Gabel, Lisa Lafayette College Department of Electrical and Computer Engineering EastonPA18042 United States Lafayette College Department of Psychology and Neuroscience Program EastonPA18042 United States

ISBN: (纸本)9798331513269

Dyslexia is a learning disability that negatively impacts an individual's ability to read, write, spell, and sometimes speak. It results in difficulties in recognizing and decoding words and patterns, despite normal level of education and intelligence. Studies have shown that early detection of dyslexia is vital for improving learning abilities in young children. Many virtual platforms exist for diagnosing and rehabilitating dyslexia;however, most require tests that measure reading skills. Since developing reading capabilities can delay the detection of dyslexia, a gaming platform based on Hebb-Williams mazes has been developed. This platform does not rely on reading skills and can diagnose dyslexia in young children. This paper presents a machine learning driven approach using two algorithms - Random Forest and Linear SVM - to classify reading abilities based on data from participants performing virtual maze tasks, which are indicative of symptoms of dyslexia. Results from this study indicate that it is possible to predict dyslexia with up to 95% accuracy based on a participant's performance in virtual gaming environments. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Skin Cancer Detection Using A Multi-Scale AI Deep Learning Approach 5

Skin Cancer Detection Using A Multi-Scale AI Deep Learning A...

引用

5th IEEE International Conference on Advances in electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Appavu, Narenthirakumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350356236

Skin cancer occurrences are increasing, and melanoma is the deadliest type of the disease. Preventing the illness from spreading to other regions of the body requires early identification. In this work, an automated technique for identifying skin cancer without the need for physician assistance is presented. The suggested method for detecting melanoma lesions makes use of transfer learning. To reduce lighting effects and noise, the acquired photos are first pre-processed. By creating more training examples, image augmentation - a crucial deep learning technique - is then used to improve the dataset and raise the training process' efficacy. Then, using transfer learning, a Convolutional Neural Network (CNN) is built using pre-learnt weights from the ImageNet dataset. The network is then adjusted to prioritise the detection of melanoma and differentiate it from benign tumours. Each of the three main phases in the method's processing of image data focusses on the central area of the picture where the suspicious lesion is located. The results from these processes are aggregated and used to train a fully connected neural network. The suggested solution outperformed other cutting-edge techniques, as seen by the 90.09% accuracy attained in testing on a collection of photos. Through concentrating on the questionable skin region at different phases, the method offers a more precise and trustworthy diagnosis. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

MalaNet: A Small World Inspired Neural Network for Automated Malaria Diagnosis

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Dwivedi, Shubham Pandey, Kartikeya Shubham, Kumar Pandey, Om Jee Tripathi, Achyut Mani Sandhan, Tushar Hegde, Rajesh M. Indian Institute of Technology Kanpur Department of Electrical Engineering India Varanasi Department of Electrical Engineering India Varanasi Department of Electronics Engineering India Indian Institute of Technology Dharwad Department of Computer Science and Engineering India

In this work, a novel neural network architecture called MalaNet is proposed for the detection and diagnosis of malaria, an infectious disease that poses a major global health challenge. The proposed neural network architecture is inspired by small-world network principles, which generally involve the introduction of new links. A small-world neural network is realized by establishing new connections, thereby reducing the average path length and increasing clustering coefficient. These characteristics are known to enhance interconnectivity and improve feature propagation within the network. In the context of malaria diagnosis, these characteristics of MalaNet can enhance detection accuracy and enable better generalization in scenarios with limited data availability. Broadly, two variants of MalaNet are proposed in this work. First, a small-world-inspired Feed-Forward Neural Network (FNN) is developed for symptom and categorical feature-based diagnosis, providing an accessible solution when blood smear images are unavailable. Subsequently, a small-world-inspired Convolutional Neural Network (CNN) is developed for precise and automated diagnosis when blood smear images are available. Both variants of MalaNet are rigorously validated using the National Institute of Health Malaria dataset, a clinical dataset from Federal Polytechnic Ilaro Medical Centre, Nigeria, and the APTOS dataset. Comparative results against several state-of-the-art neural network models in the literature demonstrate MalaNet's superior performance, generalization capability, and computational efficiency. The small-world neural network architecture proposed in this work enhances feature learning, diagnostic accuracy, and adaptability in limited-data and resource-constrained settings, motivating its application in disease diagnosis where timely and accurate results are critical. © 2020 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Detection of Medicinal Plants Using Machine Learning 12th

Detection of Medicinal Plants Using Machine Learning

引用

12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

Marine Predators Algorithm for Optimal Path Planning in Robotic Navigation

Marine Predators Algorithm for Optimal Path Planning in Robo...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Najem Sirhan Manel Martinez-Ramon Computer Science Department University of Petra Amman Jordan Electrical and Computer Engineering Department University of New Mexico Albuquerque New Mexico USA

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

In this paper, we present a novel approach to robotic path planning in grid-based environments using the Marine Predators Algorithm (MPA). The MPA, a recently developed nature-inspired optimization method, emulates the foraging strategies of marine predators to efficiently explore and exploit complex search spaces. Our proposed method aims to find collision-free, optimal or near-optimal paths from a given start location to a target goal by strategically selecting a series of way- points. The primary objective is to minimize path length while avoiding obstacles, demonstrated through a two-dimensional grid world scenario. We evaluated our approach across multiple runs to validate its robustness and consistency, showing that MPA effectively converges to high-quality solutions compared to traditional metaheuristics. The results suggest that the MPA-based path planner can be a promising tool for navigation tasks in both static and potentially dynamic environments, paving the way for future integration into real-world autonomous robotic systems.

关键词： Navigation Heuristic algorithms Metaheuristics Optimization methods Path planning Robustness Collision avoidance Robots Computational intelligence

来源：评论

学校读者我要写书评

暂无评论

A Unified Modulated Model Predictive Control for a Two-Stage AC-DC Converter Interfacing a Vanadium Redox Flow Battery

引用

IEEE Transactions on Industry Applications 2025年

作者： Volpini, Andrea Salvo, Salvatore R. Di Tresca, Giulia Leuzzi, Riccardo Zanchetta, Pericle Electrical Computer and Biomedical Engineering Dept. Pavia Italy Department of Electrical and Information Engineering Bari Italy

With the increasing integration of renewable energy sources into electrical grids, energy storage systems have become crucial for stability and regulation. Thus, dedicated two-stage AC-DC converters are essential for integrating batteries into the grid infrastructure. These converters typically employ either single-phase or three-phase inverters paired with a Dual Active Bridge (DAB) in a back-to-back configuration, ensuring high efficiency and galvanic isolation. The conventional control scheme for such converters involves two separate PI-based control loops, one for each stage. This paper proposes a novel control strategy based on a Modulated Model Predictive Control (MMPC) algorithm for a two-stage AC-DC converter. The proposed approach utilizes a single cost-function optimization routine to simultaneously control both the grid-side and the battery-side stages, simplifying the control architecture and enhancing performance. Finally, the proposed MMPC is implemented on a DSP and FPGA-based board and the experimental results show that the proposed solution achieves faster dynamics than conventional control with no overshoots or undershoots. © 1972-2012 IEEE.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

Permissioned Blockchain Security Pqfabric from Quantum and Classical Attacks 1

Permissioned Blockchain Security Pqfabric from Quantum and C...

引用

1st International Conference on Advances in computer science, electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Tamilarasi, C. Bhavadrarani, K. Kittappa, Thiagarajan Subashini, N. Anitha, V. Vimalraj, E. B.S.Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Karur India Rajalakshmi Institute of Technology Department of Mathematics Chennai600124 India Sathyabama Institute of Science and Technology Department of Mathematics Chennai600119 India Panimalar Engineering College Department of CSE Poonamallee Chennai600123 India School of Computing Mohan Babu University Andhra Pradesh Tirupati India

ISBN: (纸本)9798331518578

Old style cryptographic techniques are genuinely compromised by the development of quantum computing, which requires the formation of safety ideal models that are impervious to quantum mistakes. A new permissioned blockchain security system called PqFabric is intended to oppose both traditional and quantum attacks. PqFabric ensures the validness, mystery, and respectability of exchanges in a quantum-empowered future by including post-quantum cryptographic techniques like hash-based and grid based marks. As opposed to ordinary blockchains, PqFabric utilizes a half breed agreement process that gets information trades and members characters by melding Byzantine Fault Tolerance (BFT) with quantum-safe encryption. The system gives protection against calculation based attacks by Shor and Grover, tending to huge imperfections in existing blockchain structures. Besides, when quantum-safe guidelines advance, its secluded plan empowers smooth enhancements. With this procedure, PqFabric is situated as a ground breaking answer for organizations and areas wishing to safeguard their blockchain networks from new cryptographic risks. © 2025 IEEE.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

A High-Speed Compression and Secure Authentication Framework for Data Sharing Cloud-Enabled Big Data Environments

A High-Speed Compression and Secure Authentication Framework...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Abirami I Selvi S Lalitha R Anitha Julian Department of Computer Science and Engineering RMK Engineering College Chennai INDIA Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai INDIA Department of Computer Science and Engineering Saveetha Engineering College Chennai INDIA

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With rapidly expanding cloud-enabled big data environments, there is an imperative need for efficient data-sharing mechanisms that are multidimensional and balance both speed and security. In this connection, high-speed compression and secure authentication come forth playing a very crucial role. The two significant challenges addressed in this framework are data compression for efficient storage and transmission and robust authentication for secure access and integrity of shared data. This way, the framework compresses data size significantly without losing its quality, leading to efficient, faster data transfers and reduced considerably storage costs on cloud infrastructures. At the same time, secure authentication mechanisms [1] such as cryptography techniques and multi-factor authentication ensure that only authorized users access sensitive data, thus protected from potential unauthorized access or cyber-attacks. Integration of both capabilities speed compression and secure authentication-functionalizes big data environments to their peak performance and security. This framework is highly suitable for high-scale data-oriented industries like healthcare, finance, and IoT, which necessitate high-speed, secure, and reliable data sharing. Ultimately, this approach improves scalability and security in cloud-enabled big data systems by enabling organizations to keep pace with growing needs for highly efficient and secure data sharing in a highly dynamic environment.

关键词： Industries Multi-factor authentication Scalability Finance Organizations Medical services Big Data Cryptography Reliability Indexes

来源：评论

学校读者我要写书评

暂无评论

Optimizing Heart-Kidney Interaction for Cancer Detection Through Physiological Process Simulation as a Decision Support System

引用

IEEE Access 2025年 13卷 79169-79179页

作者： Jaddi, Najmeh Sadat Abdullah, Salwani Othman, Zalinda Yahya, Yazrina Zakree Ahmad Nazri, Mohd Leng Goh, Say Alvankarian, Fatemeh Alvankarian, Jafar Iranian eUniversity Faculty of Computer Engineering Tehran96351-14578 Iran Universiti Kebangsaan Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Bangi43600 Malaysia Universiti Kebangsaan Malaysia Center for Software Technology and Management Faculty of Information Science and Technology Bangi43600 Malaysia Universiti Malaysia Sabah Kampus Antarabangsa Labuan Optimisation and Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States RayanMiniFab Tehran University Science and Technology Park Tehran*** Iran

A decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization by analyzing the available data. The heart-kidney (HK) model proposed in this paper, as a DSS, simulates the physiological processes of the heart and kidneys in the human body to support cancer detection. The first part of the HK algorithm is inspired by the functions of the heart, where blood molecules (solutions) are sent to the heart, simulating the processes of oxygenation and the pumping of oxygenated blood to the kidneys as a local search. The solutions generated by the heart are then fed into the kidney-inspired algorithm (KA), which performs filtration, reabsorption, secretion, and excretion. The heart component enhances the KA’s local search capability, guiding the algorithm toward optimal solutions. The HK model is evaluated on eight test functions, with results compared to those of the heart algorithm and KA alone. Additionally, the HK algorithm’s effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare. © 2013 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Hybrid FLO-MPA and its Application in Three Truss Optimization Problem

Hybrid FLO-MPA and its Application in Three Truss Optimizati...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Najem Sirhan Manel Martinez–Ramon Computer Science Department University of Petra Amman Jordan Electrical and Computer Engineering Department University of New Mexico Albuquerque New Mexico USA

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Metaheuristic algorithms have become indispensable tools for tackling complex global optimization problems across various scientific and engineering domains. However, no single method universally excels due to the diverse nature of real-world landscapes and constraints. This paper proposes a novel hybrid algorithm, FLO-MPA, that integrates the Frilled Lizard Optimization (FLO) and the Marine Predators Algorithm (MPA) to leverage their complementary strengths. FLO simulates a natural leaf growth process to balance exploration and exploitation through two distinct phases: hunting for promising areas and moving up the tree to intensify the search around identified regions. MPA models predator-prey interactions in marine environments, employing adaptive memory, random Brownian and Lévy steps, and dynamic phase transitions to maintain diversity and avoid premature convergence. By combining these paradigms, FLO-MPA offers structured early-stage exploration and controlled exploitation enhanced by MPA's adaptive search strategies, memory handling, and sophisticated perturbation mechanisms. Experimental evaluations on CEC 2022 benchmark functions show that FLO-MPA significantly outperforms several state-of-the-art metaheuristics, with improvements in solution accuracy reaching over 95 % relative to standalone FLO on certain functions. The results underscore the potential of hybridization in delivering robust, high-performance optimizers that effectively balance global diversification with local refinement.

关键词： Accuracy Systematics Heuristic algorithms Metaheuristics Diversity reception Stochastic processes Benchmark testing Search problems Convergence Bars

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：