Strategies for Managing and Analyzing Large-Scale Neurological Datasets: A Review of Advanced Computational Methods

Abdelaziz

doi:https://doi.org/10.54216/MOR.040103

Fusion: Practice and Applications | 2025

Hybrid Multi-Descriptor and Deep Belief Network Model for Acute Lymphoblastic Leukaemia Diagnosis

The nature of images can differ in texture, contrast, illumination, noise levels, and structural patterns. The descriptor suitable for one image may not be optimal for another. Therefore, this paper proposes a new hybrid diagnostic model that combines multi-descriptor feature extraction with a Deep Belief Network. It is used to classify Acute Lymphoblastic Leukaemia. The proposed model consists of two phases: feature extraction and classification. Three descriptors, Histogram of Oriented Gradients, Scale-Invariant Feature Transform, and Convolutional Neural Network are employed in the feature extraction phase. Each descriptor captures different aspects of the image using distinct computational techniques. The Deep Belief Network was trained on each group of features individually. Three trained Deep Belief Network were produced with each data extract by descriptors. The membership function between the training set and the test data determines which DBN will be selected. The model was tested and evaluated on the 10,661 Leukaemia images of the C-NMC_Leukaemia dataset. It consists of two classes of images: 7272 images of Leukaemia cancer and 3389 of the Benign. Experimental results showed that the proposed model achieved an accuracy outperforming several recent methods. The accuracy of the proposed model reaches 96.87%, while the best accuracy of the recent works is 94.91%.

groups

Saif Ali Abd Alradha Alsaidi mail -

Ali Hakem Alsaeedi mail -

Hussein Al-Khamees mail -

Riyadh Rahef Nuiaa Al Ogaili mail -

Zaid Abdi Alkareem Alyasseri mail -

Mazin Abed Mohammed mail

link https://doi.org/10.54216/FPA.200111

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Optimizing Random Forest for Handwritten Digit Recognition Through Hyper-parameter Tuning

The significant increase in the volume of recently released records and multimedia news that is available presents fresh issues for pattern-recognition and machine-learning, particularly in addressing the longstanding issue of recognizing handwritten digits. Handwriting-recognition is a captivating area of research due to the uniqueness of each individual's handwriting style. It involves a computer's ability that automatically identify and comprehend handwritten (digit or character). Hyper parameters play a crucial role in the performance of machine learning algorithms, directly influencing the training process and significantly affecting the resulting model's performance. This work introduce a general automated hyper parameter tuning mechanics were used to optimize the random forest parameters, which are: grid- random search and Bayesian optimization applying on MNIST digit database (images) that have already been pre-processed. These proposed methods successfully identify optimal hyper parameters across a wide variety of ML models, taking into consideration the time cost of the search. This work shows the effectiveness and efficiency of used techniques, crucial for real-world applications. The results of this study show an accuracy rate of 99.3% for the Grid Search model, 98.8% for the Random Search model, and 96.0% for Bayesian Optimization on random forest algorithm.

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Yaqeen Saad Ali mail -

Rihab Hazim Qasim mail -

Sura Mahroos Searan mail -

Othman Mohammed Jasim mail -

Ibaa Sadoon Jabbar Alzubaydı mail

link https://doi.org/10.54216/FPA.200112

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Anomaly Detection in Satellite Imagery Using Deep Autoencoders

This study affords a deep autoencoder-primarily based framework for anomaly detection in multispectral satellite tv for pc imagery, addressing vital challenges in environmental monitoring and disaster response. Utilizing datasets from Sentinel-2, Landsat-eight, and MODIS, the version employs a hybrid loss function (MSE+MS-SSIM) and spatial attention mechanisms to discover and localize anomalies consisting of wildfires, floods, and urban encroachment. Experimental outcomes display superior overall performance (F1-Score: 0.84, AUC-ROC: 0.93) compared to PCA and Isolation Forest baselines, with precise anomaly localization demonstrated thru errors heatmaps and IoU metrics. The framework’s integration with early warning structures highlights its capability for actual-time applications, although boundaries in managing seasonal versions and occasional-decision information underscore the want for future paintings in multi-modal fusion and semi-supervised studying. This study advances scalable solutions for sustainable land control and emergency response, leveraging open-supply satellite data for global accessibility.

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Ayat Jasim Mohammed mail -

Ali Raheem Khraibet mail -

Huda Lafta Majeed mail -

Oday Ali Hassen mail

link https://doi.org/10.54216/FPA.200113

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

New Learning Approach for High-Load Traffic Optimization SDN

Due to the Internet's growing importance in our lives, Software-Defined Networking (SDN) networks have experienced high load traffic issues. Thus, network load has increased, lowering quality of service (Qos) performance. Modern networked systems depend on communication channels to transmit data between sources and destinations.  High traffic loads exacerbate packet distribution inefficiencies, causing network congestion in specific channels, compromising these communication channels. Congestion delays packet delivery and generates significant packet loss, reducing network dependability and efficiency. Communication channels' improper packet allocation along accessible paths is the fundamental issue. Some paths are overcrowded during peak traffic, while others are underused.   Bottlenecks slow packet transit and increase packet loss due to this imbalance. Current packet distribution techniques don't adapt effectively to dynamic traffic, resulting in poor network performance. Current traffic management solutions often rely on load balancing algorithms, but these methods may not adequately account for the dynamic and unpredictable nature of high-load traffic. This paper introduces Adaptive Load Balancing using Reinforcement Learning (ALBRL), which uses Q-learning and deep reinforcement learning to distribute traffic in real time in SDNs with high traffic loads. This model uses more network-specific indicators including packet loss ratio, latency, Jitter, and traffic pattern history to improve decision-making. ALBRL outperformed static routing and Q-learning with 15.34(ms) average delay, 2.11(ms) jitter, and 7.89% packet loss ratio.

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Mohammad Khalid mail -

Hassan Mohamed Muhi-Aldeen mail -

Basma Rashid Mahdi Alhamdani mail

link https://doi.org/10.54216/JISIoT.170118

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

IoT Optimum Planning for Human Facilities Enhancement in Smart Cities

More environmentally friendly standards may be implemented as work environments, lifestyles, and our conception of a fulfilling life evolve. The COVID-19 pandemic highlighted the need for adaptable systems and revealed the flaws in our routines. Because smart cities are more flexible than traditional urban areas, they are becoming more and more important. While supporting citizens is the main goal of these networked smart city components, they also unintentionally enhance urban environments. This paper uses a methodical approach to investigate smart cities, breaking down and analyzing each component to clarify their beneficial interactions. This paper provides a direction for future research through its discussion of problems, challenges, and barriers related to the urban environment that affect the development of smart cities. Real-time monitoring is made possible by connecting these devices to the internet. The spacing between lighting poles significantly influences the overall uniformity and illuminance. This paper describes the architecture of Internet of Things (IoT)-based smart public smart utility system using forecasting techniques that interconnected with the sensors using IoT stack. Sensors are made to gather the timely data from different utility applications such as lighting, CCTV cameras, water usage, wastage volume, etc. The paper demonstrates the potential synergies between IoT and artificial intelligent for supporting smart cities. We deployed three convolutional neural networks namely: AquaNet, PredWasting and LightSage for forecasting the water requirements, wasting volume and light consumption in smart cities. Results shown that PredWasting is outperformed with 99.21% of accuracy over the other models.

groups

Salah Ayad Jassim mail -

Abdalrahman Fatikhan Ataalla mail -

Mohammed Kareem Mohammed mail

link https://doi.org/10.54216/JISIoT.170119

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

DeepBalance: A Deep Reinforcement Learning Framework for Dynamic Load Balancing in Software-Defined Networks

Software-Defined Networks (SDNs) offer unparalleled network control flexibility, yet efficient load balancing is still challenging in dynamic environments. DeepBalance is a novel framework presented in this paper, which deploys dynamic load balancing in SDNs using Deep Reinforcement Learning (DRL). Our solution employs a Deep Q-Network (DQN) agent, which learns the optimal routing policies by monitoring network states and being rewarded based on load distribution. DeepBalance continuously tracks link utilization and intelligently reshifts traffic to alleviate congestion and achieve maximal throughput. We employ a comprehensive simulation environment, which emulates actual network conditions and traffic patterns. Experimental results demonstrate that DeepBalance significantly outperforms traditional load balancing techniques, lowering link utilisation variance by 37% and total throughput by 28% over shortest-path routing. The infrastructure adapts with changing traffic patterns automatically without the necessity of manual reconfiguration, thus naturally circumventing hotspots by making forward-looking path decisions. Additionally, our visualizations illustrate how the DRL agent learns over time to distribute network load more evenly across alternative paths. DeepBalance is a strong candidate for autonomous network optimization in future SDN deployments.

groups

Ali Abdullah Ali mail -

Ghaith Ali Hussein mail -

Bushra Majeed Muter mail -

Oday Ali Hassen mail

link https://doi.org/10.54216/JISIoT.170120

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Optimizing Hybrid Renewable Energy Systems for Electric Vehicle Charging Stations: A Feasibility Study in Iraqi Cities

The transition from conventional vehicles to electric vehicles (EV) represents an important development in the field of sustainable transportation. To prevent concerns about battery drain, the use of EVs requires the establishment of sufficient charging stations (CS) to recharge vehicle batteries. In Iraq, the infrastructure of electric vehicle charging stations (EVCS) is still limited, which reduces the reliance and reliability of EVs. This study assessed the economic efficiency and feasibility of optimizing hybrid renewable energy systems (HRES) for EVCS in three cities of Iraq addressing the growing demand for renewable energy due to concerns regarding fossil fuel depletion, environmental sustainability, and escalating conventional energy expenses. Hybrid Optimization Model for Multiple Energy Resources (HOMER) program was used considering weather data, load profiles, and equipment specifications. The results indicated that the system with a capacity of 300 kW of photovoltaic (PV), 100 kW of generator (GEN), and 78 units of batteries is found to be the optimal system in all three cities, with the lowest cost of energy (COE) around 0.025 $/kw. The renewable energy fractions of the optimal system in Mosul, Baghdad, and Basrah are 53%, 52.7%, and 52.7%, respectively. This setup achieves annual energy production of 704351 kWh from PV and 509681 kWh from GEN. This arrangement keeps the battery storage at a high state of charge (SoC), guaranteeing system stability and prolonging the battery's life. The system's capacity to reliably fulfil load requirements with less dependence on the DG. These results provide valuable insights into the deployment of HRES to achieve a more sustainable environment.

groups

Othman J. alhayali mail -

Abdalrahman Fatikhan Ataalla mail -

Qusay Hatem Alsultan mail -

Abdullah Fawzi Shafeeq mail -

Sameh aljanabi mail -

Mustafa Abd jalil mail

link https://doi.org/10.54216/JISIoT.170121

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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Metaheuristic Optimization Review | 2025

AI-Driven Decentralized Energy Systems: A Review of Peer-to-Peer Renewable Energy Networks

This work examines the transformational potential of AI-based decentralized energy systems: P2P renewable energy networks interconnect AI, blockchain technology, and multi-agent systems, thus circumventing the barriers of traditional centralized grids. This paper will trace how their latest trends in real-time energy optimization, secure smart contracts, and autonomous coordination of distributed resources can enhance grid resilience, minimize transmission losses, and democratize energy markets. However, it becomes evident that to enable mass adoption; significant challenges must be addressed regarding renewable energy intermittency, scalability limitations, regulatory loopholes, and cybersecurity threats. Through synthesizing current research and the analytical case of Brooklyn Microgrid, this paper discusses some of the barriers and potential future directions that must be emphasized, such as hybrid optimization models, standardized frameworks, and inclusive design for accelerating transitions towards sustainable and equitable energy systems.

groups

M. El-Said mail -

Marwa M. Eid mail

link https://doi.org/10.54216/MOR.040101

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2025

A Review of Adversarial Deep Learning Models in Neuroscience Research and Clinical Practice

Adversarial deep learning has, therefore, been tabled as one of the key research focus areas in neurosciences, and both the opportunities and drawbacks for the operation of deep learning models on neuroimaging and diagnostic jobs have been unveiled. This review examines these models' weaknesses from adversarial attacks, which can severely affect diagnosis and patient care. For example, it has been shown that slight disturbances in the level of EEG signals can confuse more profound learning algorithms employed for the identification of epilepsy, which can lead to severe diagnostic mistakes. In addition, GANs have the dual role of generating realistic neuroimaging data that can improve diagnostic processes while at the same time using adversarial images that expose the deficits of current models. This duality highlights the need to securely defend models against such risks and employ adversarial training and bio-mimic-based resilient neural network techniques. The consequence of these discoveries should not be underestimated because they reveal the necessity of showing further safety in using deep learning techniques in clinical practices. In addressing these weaknesses, the principle goal of this research is not only to help improve the diagnostic systems but also to expand the knowledge on how adversarial deep learning might affect the health, well-being and safety of patients in neuroscience.

groups

Khaled Sh. Gaber mail -

Ehsan khodadadi mail

link https://doi.org/10.54216/MOR.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2025

Strategies for Managing and Analyzing Large-Scale Neurological Datasets: A Review of Advanced Computational Methods

The progress of neuroimaging and the availability of big neuro data have brought both opportunities and difficulties in the fast-developing scientific area of computational neuroscience. As this review will show, new ways of managing and analyzing these large and layered datasets are emerging, highlighting the importance of various computational approaches to achieve valuable insights. We assess various methods for performing such analyses, among which we focus on machine learning algorithms like deep learning capable of addressing high-dimensional data characteristics for neuroimaging studies. The proposed method of analyzing multiple structural and functional MRI data in conjunction with electrophysiological and genetic data should help model neurological disorders more accurately. We also describe the preprocessing methods for dealing with data noise and variability, combined with statistical analysis that depends on existing databases to identify previously unknown patterns concerning brain functions and disorders. We also discussed the importance of open-source teamwork spaces and applications, which allow datasets and results to be shared and replicated. This review, therefore, aimed at reviewing the most effective strategies and filling the gaps within the current methodologies that may help enhance the strength and reliability of vast neurological datasets, hence diminishing diagnostic errors and helping formulate the right therapeutic intercessions in neurological disorders. This synthesis emphasizes the choice of a multidisciplinary approach when studying the neural tissues since the issue appears complex.

groups

Abdelaziz Rabehi mail

link https://doi.org/10.54216/MOR.040103

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new