مجید محمدپور

ناشر: LAMBERT Academic Publishing

توضیحات: ISBN: 978-613-9-89702-5

ناشر: Journal of Advances in Computer Research

توضیحات: ارائه به نام دانشگاه: باشگاه پژوهشگران و نخبگان دانشگاه آزاد اسلامی واحد یاسوج - نوع مقاله: علمی پژوهشی - لیست نویسندگان به ترتیب از چپ به راست:: Majid Mohammadpour; Hamid Parvin ; Ali Chamkoori

ناشر: inderscience

توضیحات:

In this paper, the droplet optimisation algorithm (DOA) has been proposed. DOA emulates rainfall phenomenon. It employs some special operators to describe the droplet process, including droplet generation, droplet fall, droplet collision, droplet flowing and droplet updating. To compare performance of this algorithm against those of some up-to-date optimisation algorithms, all of the CEC 2005 contest benchmark functions have been employed. The experimental results have proven that DOA is superior to all basic optimisation algorithms and also some up-to-date optimisation algorithms.

ناشر: Journal of Advances in Computer Research

توضیحات: ارائه به نام دانشگاه: باشگاه پژوهشگران و نخبگان دانشگاه آزاد اسلامی واحد یاسوج - نوع مقاله: علمی پژوهشی - لیست نویسندگان به ترتیب از چپ به راست: Majid Mohammadpour – Hamid Parvin

ناشر: Journal of AI and Data Mining

توضیحات: ارائه به نام دانشگاه: باشگاه پژوهشگران و نخبگان دانشگاه آزاد اسلامی واحد یاسوج - نوع مقاله: علمی پژوهشی- Vol 6, No 1, 2018 - لیست نویسندگان به ترتیب از چپ به راست: Majid Mohammadpour – Hamid Parvin – Majid Sina

ناشر: مجله مدلسازی در مهندسی دانشگاه سمنان

توضیحات: نوع مقاله: علمی پژوهشی - لیست نویسندگان به ترتیب: مجید محمدپور، حمید پروین

ناشر: مجله مهندسی برق دانشگاه تبریز

توضیحات: ارائه به نام دانشگاه: باشگاه پژوهشگران و نخبگان دانشگاه آزاد اسلامی واحد یاسوج - نوع مقاله: علمی پژوهشی - لیست نویسندگان به ترتیب: مجید محمدپور، حمید پروین

ناشر: مجله مهندسی برق دانشگاه تبریز

توضیحات: ارائه به نام دانشگاه: باشگاه پژوهشگران و نخبگان دانشگاه آزاد اسلامی واحد یاسوج - نوع مقاله: علمی پژوهشی - یست نویسندگان به ترتیب: حمید پروین، مجید محمدپور، روح اله امیدوار - جلد 47 ، شماره 3، پاییز 1396

ناشر: مجله مهندسی برق دانشگاه تبریز

توضیحات: ارائه به نام دانشگاه: دانشگاه آزاد اسلامی واحد یاسوج - نوع مقاله: علمی پژوهشی- لیست نویسندگان به ترتیب: مجید محمدپور، حمید پروین- جلد، شماره و سال چاپ: جلد 46- شماره 3 -پاییز 1395

ناشر: اولین کنفرانس بین المللی بازیابی تعاملی اطلاعات

توضیحات: نوع مقاله: اولین کنفرانس بین المللی بازیابی تعاملی اطلاعات - لیست نویسندگان به ترتیب: سجاد منطقي - دانشگاه فني و حرفه اي آموزشكده فني پسران واحد ياسوج مجيد محمدپور - باشگاه پژوهشگران و نخبگان، دانشگاه آزاد اسلامي، واحد ياسوج زكيه سادات بزرگواري - معاونت فناوري اطلاعات، وزارت آموزش و پرورش احمد نقي زاده باي - معاونت فناوري اطلاعات وزارت امور اقتصاد و دارايي

ناشر: کنفرانس بین المللی پژوهش هاي نوین در مهندسی برق و کامپیوتر و مهندسی پزشکی

توضیحات: لیست نویسندگان به ترتیب: مجید محمدپور - دانشگاه آزاد اسلامی واحد یاسوج سیدجلال الدین غریبی کریک - دانشگاه فنی و حرفه ای واحد یاسوج سیدعنایت الله غریبی کریک - دانشگاه آزاد اسلامی واحد بوشهر حمید پروین - دانشگاه آزاد اسلامی واحد یاسوج

ناشر: اولین کنفرانس ملی الگوریتم¬های فراابتکاری و کاربردهای آن در علوم و مهندسی

توضیحات: لیست نویسندگان به ترتیب: روح اله امیدوار - مرکز آموزش عالی علمی کاربردی جهاد دانشگاهی یاسوج مجید محمدپور - علوم و تحقیقات کهگیلویه و بویراحمد

ناشر: کنفرانس بین المللی پژوهش هاي نوین در مهندسی برق و کامپیوتر و مهندسی پزشکی

توضیحات: سیدجلال الدین غریبی کریک - دانشگاه فنی و حرفه ای واحد یاسوج سیدعنایت الله غریبی کریک - دانشگاه آزاد اسلامی واحد بوشهر مهرداد موحدپور - دانشگاه فنی و حرفه ای واحد یاسوج مجید محمدپور - دانشگاه آزاد اسلامی واحد یاسوج

ناشر: نشریه پردازش علائم و داده‌ها

توضیحات: نویسندگان مقاله: وحیده رضائی، مجید محمدپور، حمید پروین، صمد نجاتیان

ناشر: fundamenta-informaticae

توضیحات: Journal: Fundamenta Informaticae, vol. 176, no. 1, pp. 79-102, 2020 Received January 2018 | March 2020 | Published: 31 October 2020

ناشر: Volume 10, Issue 38 - Serial Number 38 Winter 2021

ناشر: Fundamenta Informaticae

توضیحات: نویسندگان Hamidd Zhao, Chenyuea | Alizadeh, Hosein | Minaei, Behrouz | Mohamadpoor, Majid | Parvin, M * | Mahmoudi

ناشر: The International Journal of Research on Intelligent Systems for Real Life Complex Problems

ناشر: NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN

توضیحات: Special conditions of wireless sensor networks, such as energy limitation, make it essential to accelerate the convergence of algorithms in this field, especially in the distributed compressive sensing (DCS) scenarios, which have a complex reconstruction phase. This paper presents a DCS reconstruction algorithm that provides a higher convergence rate. The proposed algorithm is a distributed primal-dual algorithm in a bidirectional incremental cooperation mode where the parameters change with time. The parameters are changed systematically in the convex optimization problems in which the constraint and cooperation functions are strongly convex. The proposed method is supported by simulations, which show the higher performance of the proposed algorithm in terms of convergence rate, even in stricter conditions such as the small number of measurements or the lower degree of sparsity.

ناشر: Ieee explorer

ناشر: NASHRIYYAH-I MUHANDISI-I BARQ VA MUHANDISI-I KAMPYUTAR-I IRAN, B-MUHANDISI-I KAMPYUTAR

ناشر: IEEE

توضیحات: Abstract: A fundamental challenge in virology research lies in effectively detecting malicious code. N-gram analysis has become a cornerstone technique, but selecting the most informative features, especially for longer n-grams, remains crucial for efficient detection. This paper addresses this challenge by introducing a novel feature extraction method that leverages both adjacent and non-adjacent bi-grams, providing a richer set of information for malicious code identification. Additionally, we propose a computationally efficient feature selection approach that utilizes a genetic algorithm combined with Boosting principles. Our experimental results show that this detection system significantly outperforms existing methods in virus detection accuracy.

ناشر: Springer London

توضیحات: The Particle Swarm Optimization (PSO) algorithm faces several inherent challenges when applied to dynamic and large-scale optimization problems. These challenges encompass the issues of outdated particle memory, inadequate scalability in high-dimensional search spaces, the incapability to detect environmental changes, a continual trade-off between exploration and exploitation, and the potential loss of population diversity within the problem space. To address these challenges, we propose a novel hybrid PSO algorithm, denoted as Parent–Child Multi-Swarm Clustered Memory (PCSCM).

ناشر: Cluster computing journal

توضیحات:

This paper proposed a novel approach integrating dual linear programming and a greedy algorithm for resource allocation in decentralized federated learning within Internet of Things (IoT) environments. The approach, (Privacy-Preserving Federated Learning with Adaptive Resource Allocation), seeks to optimize computational resource utilization among IoT devices engaged in federated learning while ensuring the preservation of privacy. The dual linear programming technique serves to model the resource allocation problem, addressing the heterogeneous computational capabilities and energy constraints inherent to IoT devices. A greedy algorithm is then applied to facilitate efficient, decentralized resource allocation, aimed at maximizing both convergence speed and model accuracy. The contributions of this research are twofold: it enhances the efficiency of 

ناشر: Neural computing and applications

توضیحات:

The Particle Swarm Optimization (PSO) algorithm faces several inherent challenges when applied to dynamic and large-scale optimization problems. These challenges encompass the issues of outdated particle memory, inadequate scalability in high-dimensional search spaces, the incapability to detect environmental changes, a continual trade-off between exploration and exploitation, and the potential loss of population diversity within the problem space. To address these challenges, we propose a novel hybrid PSO algorithm, denoted as Parent–Child Multi-Swarm Clustered Memory (PCSCM). PCSCM is explicitly designed to leverage an enhanced memory system, capable of mitigating the issue of outdated particle memory after convergence, and efficiently adapting to changing environmental conditions. This innovative memory system retains and retrieves promising solutions from the past when environmental alterations occur. Additionally, PCSCM introduces clustering mechanisms for particles within each swarm, aimed at augmenting diversity within the problem space. This clustering strategy substantially bolsters the algorithm’s performance in tracking evolving optimal solutions and positively contributes to its scalability. Crucially, the clustering approach is implemented not only for the main population but also for stored solutions in memory, which collectively strike a balance between exploration and exploitation. 

ناشر: IEEE ACCESS

توضیحات:

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Abstract:

A fundamental challenge in virology research lies in effectively detecting malicious code. N-gram analysis has become a cornerstone technique, but selecting the most informative features, especially for longer n-grams, remains crucial for efficient detection. This paper addresses this challenge by introducing a novel feature extraction method that leverages both adjacent and non-adjacent bi-grams, providing a richer set of information for malicious code identification. Additionally, we propose a computationally efficient feature selection approach that utilizes a genetic algorithm combined with Boosting principles. Our experimental results show that this detection system significantly outperforms existing methods in virus detection accuracy. The system improves detection accuracy by 15% and reduces false positives by 20% compared to traditional n-gram techniques. Additionally, it cuts computational overhead by about 30%, making 

ناشر: IEEE

توضیحات:

Federated learning (FL) in real-world deployments is fundamentally challenged by heterogeneous environments, where clients differ in both model architectures and computational capacities (system heterogeneity), as well as in local data distributions (statistical heterogeneity due to non-IID data). Existing FL methods typically address these challenges in isolation, which limits their robustness and convergence stability under realistic conditions. To systematically tackle both forms of heterogeneity within a unified framework, we propose the Enhanced Dual-Alignment Federated Learning (EDAF) framework. To address system heterogeneity, EDAF introduces Dynamic Parameter Feature Space Alignment (DPFSA), which aligns heterogeneous client models through sparsity-aware parameter selection, adaptive layer-wise matching, and learnable parameter expansion for resource-constrained clients. To mitigate statistical heterogeneity, EDAF further incorporates a Decentralized Output Space Alignment (DOSA) mechanism that dynamically constructs and updates a shared output embedding space across clients without relying on external pre-trained models. In addition, EDAF employs Federated Aggregation and Splitting (FAS) to enable communication-efficient aggregation while generating personalized global models tailored to individual client characteristics. Extensive experiments on CIFAR-10, CIFAR-100, MNIST, and IoT-23 datasets demonstrate that EDAF consistently achieves faster convergence, higher accuracy, and improved precision, recall, and F1-scores compared to state-of-the-art federated learning methods, while significantly reducing communication overhead and maintaining robustness under severe non-IID data distributions and heterogeneous client settings.

ناشر: IEEE

توضیحات:

Decentralized federated learning (DFL) is recognized as a key paradigm for training models in resource-constrained, privacy-sensitive Internet of Things (IoT) environments. However, its real-world deployment is hindered by device heterogeneity, limited resources, and unpredictable node trustworthiness. To address these challenges, an innovative framework, namely reinforcement learning-driven utility-based decentralized hierarchical federated learning (RL-UDHFL), is proposed, in which reinforcement learning (RL) is leveraged for adaptive optimization across three tiers: edge, coordination, and global aggregation. At the edge, participants are selected through an RL-based adaptive utility-driven participant selection (RL-AUDPS) mechanism, based on a utility function that accounts for computational resources, energy, data quality, and reputation. At the coordination level, self-tuning adaptive clustering is applied, and a trust-aware gossip protocol (TAGS) is employed to enable robust intercluster communication. At the global level, the reputation-based weighting is utilized, and on-the-fly anomaly detection is performed to ensure model integrity. Through extensive simulations, it is demonstrated that an RL-UDHFL achieves a model accuracy of 98%, surpassing hierarchical benchmarks, such as HAFedRL (93.5%) and T-FedHA (92%). This superior performance is attributed to the framework’s capability to balance high accuracy, efficient resource utilization, and system reliability, thereby providing a scalable and robust blueprint for deploying sustainable and trustworthy learning systems in complex IoT applications.