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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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A Detailed Investigation into the Brain Cancer Detection Capabilities of CNN

A Detailed Investigation into the Brain Cancer Detection Cap...

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International Health Informatics Conference, IHIC 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International University Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819771899

Brain tumour detection is an accepted testing task in the beginning phases of life. Yet, presently it has progressed with different AI calculations. Along these lines, to identify the brain tumour of a patient we consider the data of patients like MRI pictures of a patient’s brain. Here our concern is to spot if the tumour is available in persisting’s brain. It’s crucial to identify the tumour at the beginning level for a sound lifetime of a patient. During this task, we gauge the seriousness utilizing Convolutional Neural Network calculationwhich gives us precise outcomes. Tumour division is a pivotal and burdensome assignment inside the territory of clinical picture preparation as a human-helped manual characterization may bring about erroneous expectations and findings. We suggested using convolutional neural organization to distinguish tumours from 2D appealing reverberation brain images (MRI). The exploratory analysis was performed on a dataset that included tumours of various sizes, regions, forms, and picture forces. We used convolutional neural networks, which are implemented using Keras and Tensorflow because they respect a much better display than standard ones. CNN achieved an accuracy of 83.54% in our research. The recently developed CNN design may be used as a productive decisionaid device for radiologists in clinical diagnostics because of its high speculation capacity and quickness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neuroimaging

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Resource Allocation in Cloud Computing Using Quasi-Opposition Learning Based Aquila Optimizer

Resource Allocation in Cloud Computing Using Quasi-Oppositio...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Kumari, K. Aruna Muvva, Vijaya Bhaskar Reddy Kumar, K. Sudheer Rai, B. Karunakara Shakir, Aboothar Mahmood Department of CSE SRKR Engineering College Bhimavaram Chinaamiram India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences -Shinas Shinas Oman School of Computer Science and Artificial Intelligence SR University Warangal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq

ISBN: (纸本)9789819778751

The resource allocation is focus on enhancing the service provider proceedings and obtaining customer satisfaction by Service Level Agreements (SLA). Cloud technology has significantly transformed the organizations to manage and allocate their resources. The cloud users are choosing sources of application distribution exclusive of cooperating Quality of Service (QoS) necessities. To overcome this issue, this paper proposed a Quasi-Opposition Learning based Aquila Optimizer (QOL-AO) for resource allocation in cloud. The virtualization supports to implement task using resource availability and reduces the response time. The QOL-AO employs the hunting approach for obtain the effective and efficient resource allocation in cloud. The Performance of QOL-AO is estimate through execution time, response time and energy consumption for various number of Virtual Machines (VMs). The QOL-AO attained less execution time of 18.568 ms and response time of 0.095 s than existing algorithms like Emperor Penguin Optimization (EPO) and Particle Swarm Optimization (PSO). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Associations of occupation categories with cardiovascular diseases and all-cause mortality: an analysis of NHANES 2005-2014

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BMC Public Health 2025年第1期25卷 1-10页

作者： Chi, Haiyang Zhou, Jia Li, Chao Lu, Yuhuan Xie, Can He, Baoyu Ke, Wei School of Applied Sciences Macao Polytechnic University 999078 Macao Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao Polytechnic University 999078 Macao Chongqing Yubei Maternal and Child Health Hospital Chongqing 40120 China Guang Ming District Center for Disease Control and Prevention Guangdong Province Shenzhen 518106 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau 999078 Macao

Background: Evidence on the effects of occupation categories on cardiovascular disease (CVD) progression and longevity has mostly come from cross-sectional studies, which limits our understanding of the pathogenesis of CVD. This study aimed to evaluate the associations of occupation categories with CVD and all-cause mortality in the American population. Methods: We analyzed data from the 2005–2014 U.S. National Health and Nutrition Examination Survey (NHANES), a large-scale public health survey representative of the U.S. population, linked to mortality data obtained in 2019. To evaluate the association between occupation categories and the risks of cardiovascular disease (CVD) and all-cause mortality, we estimated Cox proportional hazard ratios (HRs) with 95% confidence intervals (CIs). Results: Among 11,102 participants (aged 20–85 years, 53.99% male), 132 died from CVD among the total 478 deaths during a median follow-up of 9.9 years. In multivariable-adjusted models, the occupation categories were significantly associated with CVD mortality, with the hazard ratios (HRs) and 95% confidence intervals (95% CIs) of 3.95(1.94–8.04) for construction and clean-up industry, 2.51(1.15–5.52) for sales and service industry, 2.49(1.04–5.95) for business and management, and 2.98(1.56,5.71) for others. For all-cause mortality, only construction and clean-up industry and sales and service industry were positively associated with all-cause mortality, and HRs (95% CIs) were 2.05(1.33–3.16) and 1.64(1.12–2.41). Both working hours in the previous week and the number of months worked exhibited varying degrees of dose-response relationships with CVD and all-cause mortality. Conclusions: Occupation categories were found to be significantly associated with the risk of CVD and all-cause mortality. Future research could incorporate different work properties into specific prevention strategies for these outcomes. © The Author(s) 2025.

关键词： All-cause mortality Cardiovascular mortality Cohort study Occupation categories

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Design of a Dual Attention Mechanism for Small Object Detection

Design of a Dual Attention Mechanism for Small Object Detect...

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International Symposium on Computational and Business intelligence (ISCBI)

作者： Cheng Qian Simon Fong Hongxu Yin Chao Gao Huafeng Qin João Alexandre Lobo Marques School of Artificial Intelligence Chongqing Technology and Business University ChongQing China Department of Computer and Information Science University of Macau Laboratory of Applied Neurosciences - University of Saint Joseph Macau SAR China

ISBN: (数字)9798331533786

ISBN: (纸本)9798331533793

Detecting small objects remains a challenging task in object detection, with even notable models like YOLO struggling to achieve high accuracy in complex backgrounds. A dual attention (DA) mechanism specifically designed for small object detection is proposed in this paper, integrating two attention modules: the Global Grouped Coordinate Attention (GGCA) module, which enhances global information retention in feature maps for improved contextual integration, and the Median Pooling-Enhanced Multi-Scale Attention (MPSA) module, which increases noise resilience and refines multi-scale features. Experimental evaluations on datasets such as SHWD and Hardhat and Safety Vest demonstrate that incorporating the DA mechanism into YOLOv7 yields accuracy improvements of 0.75% and 0.59%, respectively, as well as gains of 0.57% and 0.17% on the Traffic Vehicles Dataset and J-EDI dataset. These results confirm that the proposed DA mechanism significantly enhances small object detection performance in complex scenes. Furthermore, this approach shows potential for application in medical imaging, potentially advancing precision in medical image analysis.

关键词： YOLO Attention mechanisms Accuracy Noise Feature extraction Safety Business intelligence Biomedical imaging Resilience

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Meta-Learning-Driven QoS Estimation for Self-Optimizing 6G Networks in Stochastic α-η-µ Fading Channels with Multi-Layer Selection Combining and Cross-Layer Interference Adaptation

Meta-Learning-Driven QoS Estimation for Self-Optimizing 6G N...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Keshav Kaushik Priyanka Gaur Barga Mohammed Mujahid Mudassir Khan Elfatih Elmubarak Mustafa Amrita Singh Sharda School of Computer Science & Engineering Sharda University Greater Noida India Department of Computer Science Applied College Khamish Mushaiyt King Khalid University Saudi Arabia Department of Computer Science College of Computer Science Applied College Tanumah King Khalid University Abha Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and Information Technology University of Bisha Saudi Arabia Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

The authors proposed to use a meta-learningbased approach for QoS optimization in future 6G networks that are self-optimizing sections, focusing on stochastic α − η − µ fading channels. The proposed model utilizes information learned in the past so that it can rapidly adapt to these changes as they arrive, providing much better area adaptation, energy efficiency and interference mitigation than traditional learning approaches. You push the best and QoS, network throughput, latency abased simulation results are provided under high mobility in addition to severe fading conditions. The metalearning model showed accelerated convergence, adaptive resource allocation and improved interference mitigation which naturally led to QoS improvement up to 80% as well energy efficiency increased up to 65−90%, network reliability were enhanced. This step helps build green and robust communication mechanisms in the next-generation networks.

关键词： Fading channels 6G mobile communication Metalearning Adaptation models Prevention and mitigation Stochastic processes Quality of service Interference Energy efficiency Numerical models

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Design an Ensemble Pretrained Deep Learning Model for Classification of Melanoma Skin Cancer Images

Design an Ensemble Pretrained Deep Learning Model for Classi...

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

作者： Dheeb Albashish Nedaa Almansour Azizi Abdullah Hossam M. J. Mustafa Manar Rizik AlSayyed Oraib Alrashdan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan Center for Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Center For Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Malaysia Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Pathology Department University of Jordan Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The recent global outbreak of melanoma skin cancer has prompted increased attention toward computer-aided diagnosis systems (CAD). The primary application of CAD in melanoma is to discriminate between benign and malignant skin medical images. Additionally, the survival rate improves with early cancer detection. This study presents a CAD diagnosis method for melanoma skin diagnosis, which utilizes deep learning and medical image processing. To elaborate, the proposed model comprises two stages: Firstly, fine-tune three powerful pretrained models, namely VGG16, NASNetMobile, and ResNet50V2, to address the current task. Secondly, we leverage their decisions through ensemble learning and hard voting techniques. The evaluation of the proposed ensemble model on a skin cancer dataset yielded a notable accuracy of 91.5% on a test portion of the dataset. This method reveals the potential for diagnosing melanoma while also illustrating the efficacy of the ensemble model in augmenting diagnostic performance.

关键词： Deep learning Solid modeling Design automation Computational modeling Melanoma Skin computer aided diagnosis Ensemble learning Medical diagnostic imaging Computational intelligence

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Action Recognition in Real-World Ambient Assisted Living Environment

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Big Data Mining and Analytics 2025年第4期8卷 914-932页

作者： Zakka, Vincent Gbouna Dai, Zhuangzhuang Manso, Luis J. School of Computer Science and Digital Technologies Aston University Birmingham B4 7ET United Kingdom Aston University Applied Artificial Intelligence and Robotics Department Birmingham B4 7ET United Kingdom

The growing ageing population and their preference to maintain independence by living in their own homes require proactive strategies to ensure safety and support. Ambient Assisted Living (AAL) technologies have emerged to facilitate ageing in place by offering continuous monitoring and assistance within the home. Within AAL technologies, action recognition plays a crucial role in interpreting human activities and detecting incidents like falls, mobility decline, or unusual behaviours that may signal worsening health conditions. However, action recognition in practical AAL applications presents challenges, including occlusions, noisy data, and the need for real-time performance. While advancements have been made in accuracy, robustness to noise, and computation efficiency, achieving a balance among them all remains a challenge. To address this challenge, this paper introduces the Robust and Efficient Temporal Convolution network (RE-TCN), which comprises three main elements: Adaptive Temporal Weighting (ATW), Depthwise Separable Convolutions (DSC), and data augmentation techniques. These elements aim to enhance the model's accuracy, robustness against noise and occlusion, and computational efficiency within real-world AAL contexts. RE-TCN outperforms existing models in terms of accuracy, noise and occlusion robustness, and has been validated on four benchmark datasets: NTU RGB+D 60, Northwestern-UCLA, SHREC'17, and DHG-14/28. The code is publicly available at: https://***/Gbouna/RE-TCN. © 2018 Tsinghua University Press.

关键词： action recognition Ambient Assisted Living (AAL) computational efficiency noise robust occlusion robust

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SEDNet: Synergistic Learning Network with Embedded Encoder and Dense Atrous Convolution for Vehicle Re-identification

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Alexandria Engineering Journal 2025年 128卷 297-305页

作者： Mingfu Xiong Tanghao Gui Zhihong Sun Saeed Anwar Aziz Alotaibi Khan Muhammad School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan 430200 China Department of Information Security Naval University of Engineering Wuhan 430030 China The University of Western Australia (UWA) Crawley 6009 Australia Department of Computer Science College of Computing and Information Technology Taif University Taif 21974 Saudi Arabia Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab) Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea

To address the issue of information redundancy (such as color and vehicle model) caused by excessive emphasis on local features in vehicle re-identification, this paper proposes a Synergistic Learning Network with Embedded Encoder and Dense Atrous Convolution (SEDNet). The proposed SEDNet framework consists of three unique branches: a global embedded multi-head encoder (GEME), local dual-dense atrous convolution (LDAC), and auxiliary attribute embedding (AAM). The GEME branch integrates the global appearance features of the vehicle to enhance consistency in descriptions from different perspectives. To suppress redundant information such as color and vehicle model information, and refine local features, the LDAC branch employs an attention mechanism to capture multiscale features using convolutional kernels with varying dilation rates. In addition, the AAM branch uses vehicle metadata, such as direction and camera perspectives, to enhance feature robustness. Our proposed SEDNet method has been rigorously tested on the mainstream benchmark vehicle re-identification datasets, including VeRi-776, VehicleID, and VeRi-Wild. The results show that our method enhances the mAP by 2.2%, 2.2%, and 0.2%, respectively, when compared to the latest methods, all evaluated on a regular scale. Additional experiments conducted on the Market-1501 and DukeMTMC-reID datasets further verify our method’s generalization capability.

关键词： Vehicle re-identification Embedded encoder Dense atrous convolution Intelligent networks Synergistic learning

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Effective Gas Level Prediction Based on Sensor Array Using Deep Learning in Mixed Gases: A Comparative Analysis of CNN-1D, LSTM, and GRU Models

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Addae, Sampson Kim, Jungyoon Kang, Misun Kent State University Smart Communities and IoT Laboratory Department of Computer Science KentOH44242 United States Hansung University Department of Applied Artificial Intelligence Seoul02876 Korea Republic of

Estimating the concentration levels of gas components in a mixture is vital for taking preventive actions and ensuring public safety in various environments, including industrial settings, urban areas, and confined spaces where gas leaks or accumulation can pose significant health and safety risks. This is because general gas sensors for target pollutants are easily affected by other gases. In this study, we assessed the effectiveness of combining data segmentation, feature extraction, and normalization on the performance of three deep learning (DL) models: 1-D convolutional neural network (CNN-1D), long short-term memory (LSTM), and gated recurrent unit (GRU) network for predicting gas levels in two combinations: ethylene (Ety)-methane (Met) and Ety-carbon monoxide (CO). We evaluated performance using mean absolute error (MAE), root mean squared error (RMSE), and R-squared (R2). Among the models evaluated, the GRU network outperformed the CNN-1D and LSTM in terms of averaged MAE (0.1578), RMSE (0.0481), and R2 (0.96375). Comparative analysis with existing studies confirmed that our proposed GRU model outperformed others so that we propose its use in gas concentration estimation tasks. © 1963-2012 IEEE.

关键词： Long short-term memory

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