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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

computer Modeling in engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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Non-Destructive Detection of Fillet Fish Quality Using MQ135 Gas Sensor and Neutrosophic Logic-Enhanced System

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Neutrosophic Sets and Systems 2025年 80卷 540-563页

作者： Shams, M.Y. Darwesh, M.R. Bhatnagar, Roheet Al-Sattary, N.S.A. Salama, A.A. Ghoname, M.S. Deptartment of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheihk33516 Egypt Department of Agriculture Engineering Faculty of Agriculture Tanta University Tanta31527 Egypt Department of Food Processing Faculty of Industry and Energy Samannoud Technological University Egypt Department of Computer Science and Engineering Manipal University Rajasthan Jaipur India Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt

This paper demonstrates the feasibility of using an electronic nose to assess fish quality by analyzing air quality and examining volatile organic compounds (VOCs) alongside physical variables, with pH, protein content, and VOCs serving as chemical reference points. Artificial intelligence algorithms were employed to predict quality and calculate regression coefficients. Using the reference neural network algorithm based on chemical and physical compounds, regression coefficients (R values) achieved were 0.99, 0.98, and 0.97, respectively. Additionally, ANFIS (Adaptive Neuro-Fuzzy Inference System) produced R values of 0.99, 0.85, and 0.98. Both fuzzy logic and ANFIS proved effective for predicting fish quality. Image processing techniques, including histogram analysis, color mapping, and edge detection, were also applied to assess fish quality. To enhance the inference process, Neutrosophic Logic-Enhanced Fuzzy Logic Systems were utilized, addressing uncertainty and imprecision in fish quality assessment. Neutrosophic logic combines fuzzy logic's partial truth with indeterminacy, represented by three membership functions: truth, indeterminacy, and falsity. Neutrosophic fuzzy inference integrates steps like neutrosophication, rule evaluation, aggregation, and defuzzification, ensuring improved expressiveness and fidelity. For instance, neutrosophic fuzzy rules evaluated fish freshness and appearance to determine quality ratings such as poor, good, or excellent. This integration enhances decision-making by accurately modeling complex real-world uncertainties. These methods, combining electronic nose technology, artificial intelligence, and neutrosophic inference, provide a robust, non-destructive, and cost-effective approach to detecting spoilage in fillet fish. © 2025, University of New Mexico. All rights reserved.

关键词： Volatile organic compounds

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Unveiling network intrusions: Leveraging large language models for anomaly detection in cybersecurity

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AIP Conference Proceedings 2025年第1期3255卷

作者： Vraj Patel Bhargavi Savani Bela Shah Amit Thakkar Deptartment of Computer Science and Engineering CSPIT Charotar University of Science and Technology Anand India

Large Language Models (LLMs) are recognized for their flexibility which has benefited a variety of domains. One such area of study is log analysis for cybersecurity where LLMs can be a great asset. This research delves deep into utilization of Large Language Models (LLMs), namely RoBERTa and MegatronBERT, to analyze network vulnerabilities, with a specific focus on Hadoop Distributed File System (HDFS) logs. Through training these Large Language Models (LLMs) on this dataset, the work aims to compare the accuracy of identifying outliers. The approach includes preprocessing the data, extracting relevant features, and the utilization of progressive LLM architectures for pattern recognition. By performing a number of experiments, the study demonstrates the effectiveness of LLMs in enhancing network security, offering a promising avenue for proactive defense strategies in modern IT environments.

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Optimising Energy Harvesting and Throughput for UAV-Assisted IRS Systems With Adaptive Energy Harvesting

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IET Communications 2025年第1期19卷

作者： Sheu, Jeng-Shin Ho, Chun-Yu Deptartment of Computer Science and Information Engineering National Yunlin University of Science and Technology Douliu Taiwan

Integrating intelligent reflecting surfaces (IRS) with unmanned aerial vehicles (UAVs) presents a promising approach for future energy-efficient wireless communications. This paper proposes an adaptive framework that dynamically balances energy harvesting (EH) efficiency and system throughput by adjusting the required EH efficiency based on the UAV's power levels and communication needs. Utilising real-coded genetic algorithm (RCGA), the framework effectively tackles challenges posed by multi-user interference (MUI) and imperfect channel estimation (CE). Our results demonstrate that the RCGA-based approach outperforms deep reinforcement learning (DRL) methods, delivering superior energy harvesting and throughput in realistic conditions. The adaptive EH strategy not only optimises throughput performance but also ensures efficient UAV energy management, particularly in dynamic and energy-constrained environments, making it a robust solution for sustained UAV operations in dynamic and energy-constrained environments. © 2025 The Author(s). IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Micro air vehicle (MAV)

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Magnetic nanoparticle detection methods in the context of complex fluids

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International Journal of Coal science & Technology 2024年第6期11卷 173-184页

作者： Alirza Orujov Jon M.Pikal TeYu Chien Jefferson A.Carter Saman A.Aryana Deptartment of Chemical and Biomedical Engineering University of WyomingLaramieWYUSA Deptartment of Electrical Engineering and Computer Science University of WyomingLaramieWYUSA Deptartment of Physics and Astronomy University of WyomingLaramieWYUSA

Foams improve mobility control in injection operations within geological ***,such as iron-oxide,have been shown to enhance the stability of foams when combined with *** this research,we leverage the magnetic properties of these nanoparticles to detect their presence as a surrogate for monitoring the geologic extent of injected fluids in the *** feasibility of using these nanoparticles for monitoring purposes stems from their detectability at low concentrations in subsurface *** developed two distinct methods to detect the presence of magnetite nanoparticles in complex *** simulate complex subsurface fluids in a laboratory setting,we included various ions and surfactants and investigated their effects on the detection of *** this end,we designed an experimental setup and tested two magnetic detection methods:Induction Heating(IH)and Oscillator Frequency Shift(OFS).The IH method involves applying a high-frequency alternating magnetic field to a solution containing small amounts of magnetic nanoparticles and measuring the temperature *** built an experimental setup to generate this magnetic field for different samples,with temperature changes recorded by an infrared *** results indicate that nanoparticle concentrations linearly affect the solution's temperature ***,the presence of ions and surfactants also influences the temperature *** OFS method measures shifts in the resonance frequency of a circuit caused by changes in magnetic permeability inside a *** coil is part of a transistor oscillator circuit that produces a sinusoidal voltage waveform,with the oscillation frequency depending on the coil’s *** presence of nanoparticles causes a shift in resonance frequency,which were precisely measured for various *** drop in resonance frequency is a linear function of nanoparticle concentration,and both methods detect concentrations as low as 15

关键词： CO_(2) storage Monitoring Nanoparticle-stabilized foams Sequestration Subsurface Tracer nanoparticles

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An Explainable AutoML-Driven Meta-Learning Scheme for Intrusion Prevention in Zero-Touch Networks Within Carbon Intelligent IIoT

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IEEE Internet of Things Journal 2025年

作者： Islam, Anik Karimipour, Hadis Gadekallu, Thippa Reddy University of Calgary Schulich School of Engineering Deptartment of Electrical and Software Engneering CalgaryT2N1N4 Canada Zhejiang A and F University The College of Mathematics and Computer Science Hangzhou311300 China Lovely Professional University Division of Research and Development Phagwara India Chitkara University Center of Research Impact and Outcome Punjab Rajpura140401 India

Carbon Intelligent Industrial Internet of Things (IIoT) systems are critical for achieving sustainable industrial automation but face challenges such as scalability, operational complexity, and security vulnerabilities. Zero-Touch Networks (ZTN), with their autonomous management capabilities, offer solutions to operational challenges but remain vulnerable to sophisticated cyber intrusions due to their high level of autonomy and interconnectedness. While Artificial Intelligence (AI), especially Deep Learning (DL), shows potential in intrusion detection, current approaches often encounter obstacles such as insufficient datasets, challenges in automated data preprocessing, and a lack of transparency. This paper introduces an AutoML-enabled Meta Learning-based Intrusion Prevention Scheme designed specifically for ZTN within Carbon Intelligent IIoT. The proposed framework integrates AutoML and meta-learning to streamline data preprocessing and improve model adaptability in dynamic and evolving threat environments. To ensure transparency, an Integrated Gradient-based Explainable AI (XAI) mechanism is employed, offering insights into the impact of individual features on model predictions, thereby addressing concerns related to trust and accountability in industrial applications. Experimental evaluations demonstrate the framework’s effectiveness in enhancing intrusion prevention, bolstering security, and improving transparency for ZTN in carbon intelligent IIoT, providing a comprehensive solution to prevailing challenges. © 2014 IEEE.

关键词： Network intrusion

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RSA Threshold signature based node eviction in vehicular ad hoc network

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Information Technology Journal 2012年第8期11卷 980-988页

作者： Harit, S.K. Saini, S.K. Tyagi, N. Mishra, K.K. Motilal Nehru National Institute of Technology Deptartment of Computer Science and Engineering Allahabad-211004 Uttar Pradesh India

Vehicular Ad hoc network is a self-organizing net-work between the vehicles that helps in enhancing transportation safety and efficiency. Vehicular Ad hoc network has a wide range of applications that needs a high level of security, as a single vehicle sending out false warnings can disrupt the traffic of whole highway. Eviction of such misbehaving vehicle is a critical issue of vehicular ad hoc network. Almost every approach, proposed for security of vehicular ad hoc network communication is based on public key infrastructure in which only certification authority can revoke the certificate of misbehaving vehicle. In this paper, we propose a node eviction protocol based on RSA Threshold signature which can evict the misbehaving node from vehicular ad hoc network by maintaining the properties of public key infrastructure architecture. © 2012 Asian Network for Scientific Information.

关键词： Vehicular ad hoc networks

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Combining rough set theory and instance selection in ontology mapping

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High Technology Letters 2008年第3期14卷 258-265页

作者： Qian Pengfei Wang Yinglin Zhang Shensheng Deptartment of Computer Science and Engineering Shanghai Jiaotong UniversityShanghai 200240P.R.China

This paper presents a novel ontology mapping approach based on rough set theory and instance *** this appoach the construction approach of a rough set-based inference instance base in which the instance selection(involving similarity distance,clustering set and redundancy degree)and discernibility matrix-based feature reduction are introduced respectively;and an ontology mapping approach based on multi-dimensional attribute value joint distribution is *** core of this mapping aI overlapping of the inference instance *** valuable instances and important attributes can be selected into the ontology mapping based on the multi-dimensional attribute value joint distribution,so the sequently mapping efficiency is *** time complexity of the discernibility matrix-based method and the accuracy of the mapping approach are evaluated by an application example and a series of analyses and comparisons.

关键词： ontology mapping instance selection rough set feature reduction

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Machine Learning Methods for Dysgraphia Screening with Online Handwriting Features 4

Machine Learning Methods for Dysgraphia Screening with Onlin...

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4th International Conference on computer and Applications, ICCA 2022

作者： Kunhoth, Jayakanth Al Maadeed, Somaya Saleh, Moutaz Akbari, Younus Qatar University Deptartment Computer Science and Engineering Doha Qatar

ISBN: (纸本)9781665452946

Dysgraphia, a major learning disorder that primarily interferes with writing skills can hinder the academic track of children unless recognized in the early stage. The diversity in the symptoms, as well as the emergence in different ages, makes the diagnosis quite an intricate task. This work proposes automated methods that can be used for the diagnosis of dysgraphia by analyzing handwriting. Particularly this work examined the effectiveness of kinematics and dynamics of handwriting for discriminating abnormal writing. Furthermore, this work focused on developing methods that utilize fewer features for classifying dysgraphic and non-dysgraphic subjects. The proposed methods are evaluated in a publicly available online handwritten dataset. Obtained results indicate that the proposed method can diagnose the existence of dysgraphia with an accuracy of 77% with a limited number of features. © 2022 IEEE.

关键词： E-learning

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