Fault Monitoring in Transmission Lines Using Modular Neural Networks in Simulated Smart Grids

Sánchez-Juárez J. R.; Aldana-Franco R.; Leyva-Retureta, J. G.; Álvarez-.Sánchez E. J.; López-Velázquez A.; Aldana-Franco F.

doi:https://doi.org/10.54216/JISIoT.180209

Journal of Intelligent Systems and Internet of Things | 2026

A New Strategy for Exploration and Area Coverage Using Swarm Robots by Enhancing the Pelican Optimization Algorithm

Area coverage and exploration of unknown environments by swarm robots autonomously is one of the challenges in the robotics domain. This paper proposes a new strategy for area coverage in two parts; firstly, enhancing a Pelican Optimization Algorithm (POA) using swarm robots to explore an unknown area. Secondly, merges many algorithms with the proposed POA, such as Timed Elastic Band (TEB) as a local planner for obstacle avoidance, SLAM (Simultaneous Localization and Mapping), and a training model which is called You Only Look Once version 8 nano (YOLOv8n) for person detection. The proposed POA algorithm successfully monitored a large area and achieved a high exploration ratio with minimal time. In this work, the new strategy is applied to a robot warehouse environment, utilizing a swarm of robots to explore the area and find targets, which are employees suffocated by the effects of chemical pollution. The simulation and real-world tests for a new strategy were done in the Robot Operating System (ROS) using the TurtleBot3 robot. The total time-consuming for exploration and detection time is less by POA, while the coverage ratio is the largest when compared with the original RRT exploration algorithm for empty, small, and large environments, respectively.

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Dena Kadhim Muhsen mail -

Ahmed T. Sadiq mail -

Firas Abdulrazzaq Raheem mail

link https://doi.org/10.54216/JISIoT.180203

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Vol. Volume 18 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2026

Power Consumption Prediction Using a CNN-LSTM-Attention Hybrid Deep Learning Model

Reducing energy losses and increasing power grid efficiency need accurate prediction of power consumption accurate prediction of future energy consumption requires the use of time series data. To overcome the shortcomings of conventional techniques for forecasting energy consumption in India for the period from 2 January, 2019 to 23 May, 2020, we used an attention mechanism, which is still relatively new and not well known. In this paper, we propose a new approach for predicting energy consumption by combining local feature extraction with convolutional neural networks (CNNs), long short-term memory (LSTM) to capture long-term temporal dependencies, and attention mechanisms to deal with the issue of information loss brought on by extremely lengthy input time series data. After high-dimensional features are extracted from the input data using a one-dimensional CNN layer, temporal correlations within historical sequences are captured using an LSTM layer.  In order to optimize the weighting of the LSTM outputs, strengthen the impact of important information, and enhance the prediction model as a whole, an attention mechanism is finally implemented. This integration improves the model's ability to represent complex spatio-temporal patterns. The mean absolute error (MAE) and root mean square error (RMSE) are used to assess the performance of the proposed model. The results demonstrate that the CNN-LSTM-Attention model outperforms conventional hybrid CNN-LSTM and LSTM models, demonstrating superior performance across a range of prediction scenarios. By supporting more reliable grid management, proactive intervention methods, and predictive maintenance, these developments contribute to reducing load imbalances and energy waste in India. The Future developments could see the proposed model extended to other time series prediction domains.

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Nebras Jalel Ibrahim mail -

Samah Faris Kamil mail -

Ghasaq Saad Jameel mail

link https://doi.org/10.54216/JISIoT.180204

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Vol. Volume 18 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2026

Developing a Fast Hybrid Metaheuristic Algorithm to Enhance the Efficiency of Resource-Constrained Applications

The rapid development of intelligent computing has led to Internet of Things (IoT) applications and embedded devices suffering from severe constraints on energy, processing, and memory. This calls for fast and lightweight algorithms that maintain performance accuracy without draining resources or affecting response time. This paper presents a new hybrid metaheuristic algorithm that combines the advantages of four optimization algorithms to achieve efficient results and reduce computational complexity without compromising output quality. Experiments demonstrate significant improvements in performance and execution time compared to traditional algorithms, in addition to the algorithm's ability to scale and handle diverse workloads. The lowest improvement of the proposed algorithm compared to other algorithms was approximately 25.7%. This algorithm opens up prospects for effective applications in smart systems in urban and industrial areas.

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Alaa Abdalqahar Jihad mail -

Ahmed Subhi Abdalkafor mail -

Sameeh Abdulghafour Jassim mail

link https://doi.org/10.54216/JISIoT.180205

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Vol. Volume 18 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2026

Multi-Variable Markov Framework for Predicting Battery Depletion in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) support intelligent data acquisition systems across environmental monitoring, industrial automation, and smart cities. As a fundamental enabler of the Internet of Things (IoT), WSNs rely heavily on battery-powered sensor nodes for sustained operation in dynamic and often remote environments. However, predicting battery lifetime in WSNs remains a critical challenge due to the complex interplay between environmental conditions and operational behaviors. Conventional energy models often fail to consider the simultaneous influence of temperature, humidity, and data traffic intensity on battery depletion rates. This study proposes a battery lifetime prediction model based on a Markov framework integrated with an exponential energy consumption function to address this issue. The model incorporates three primary variables—ambient temperature, relative humidity, and data movement to simulate energy usage dynamically. The framework calculates transition probabilities and energy load based on environmental states, enabling accurate forecasting. Additionally, the model evaluates the impact of different battery chemistries (Ni-MH, LiPo, Li-ion, and Alkaline) on lifespan performance across varying environmental scenarios. Simulation results reveal that temperature and humidity significantly influence energy depletion, while data transmission intensity plays a supporting role in high-traffic cases. LiPo and Li-ion batteries demonstrate superior performance and stability, especially under extreme environmental conditions. This study contributes a novel multi-variable model that bridges physical sensing environments with predictive battery analytics. The findings provide a foundation for strategic energy planning and adaptive deployment of WSNs in sustainability-critical applications.

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Deden Ardiansyah mail -

Moestafid mail -

Teddy Mantoro mail

link https://doi.org/10.54216/JISIoT.180206

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Vol. Volume 18 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2026

Exposing Image Tampering: A Deep Learning Approach to Copy-Move Forgery Detection for Secure Digital Image Forensics

Nowadays, with the proliferation of mobile devices and the internet around the world that are available for everyone, and due to the low prices versus their high capabilities, images are considered one of the most common ways of transmitting information between users, advancement of image processing and editing tools, simplified the process of editing and changing photographs such as in magazines, newspapers, scientific journals, and on social media or on the Internet. As a result, the propagation of manipulated photographs that misrepresent the truth is prevalent, whether deliberate or inadvertent. We propose a method that uses deep learning based convolutional neural network in order to detect instances of the copy-move forgeries in images which can  help to ensure data authenticity in digital forensic investigations. In this case, our method is intended to improve digital evidence integrity by detecting complicated changes quickly and precisely. This work can supports cybersecurity applications like anti-fraud systems, fake news detection, and social media forensics. The findings of the experiment demonstrate that the suggested approach is capable of detecting forgery against multiple copies and post-processing activities. The dataset's images used for both training and testing are MICC-F2000, composed of 2,000 images, 700 tamper and 1,300 originals. The findings indicate a testing accuracy of 98.00% and a training accuracy of 99.17%.

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Nadia Mahmood Ali mail -

Sameer Abdulsttar Lafta mail -

Amaal Ghazi Hamad Rafash mail

link https://doi.org/10.54216/JISIoT.180207

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2026

I_g^-Continues and I_g^-irresoluteness

In this paper, I_g^*- closed sets, and I_g^*- open are used to investigate and define a new class of functions is said to be I_g^*-Continues functions, I_g^*-irresolute functions in ideal topological space topological spaces. Morover, I introduce I_g^*- compact spaces and I_g^*-connected spaces, and maximal I_g^*-closed sets. I obtain their characterizations and study their basic properties.

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Wadei Faris AL-Omeri mail

link https://doi.org/10.54216/IJNS.270212

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Vol. Volume 27 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2026

Group Message Prioritization Using Circular Bipolar Complex Dual Valued Fuzzy Linguistic Sets and Frank Aggregation Operators

This paper introduces a novel extension of the multi-attributive border approximation area comparison (MABAC) method based on circular bipolar complex dual valued fuzzy uncertain linguistic sets (CBCDVFULSs) using Frank power aggregation operators. In order to effectively integrate aspects of fuzzy set theory, bipolarity, complex-valued, and uncertain linguistic information, this paper presents a novel framework based on CBCD- VFULSs. Frank power aggregation operators is used specifically for CBCDVFULSs in order to handle and aggregate such complex data. These operators maintain the circular and bipolar properties of the fuzzy linguistic data by utilizing the adaptability of Frank t-norms pFT N q and t-conorms pFT CN q. In contrast to current approaches, the suggested method’s superior handling of complex uncertain linguistic environments, flexibility, and applicability are demonstrated through a numerical example. A group message prioritization system for WhatsApp that involve deciding on the priority under complex, uncertain, and bipolar linguistic evaluations is used to demonstrate the efficacy of the suggested approach.

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M. Kaviyarasu mail -

J. Angel mail -

Prasanta Kumar Raut mail -

Mana Donganont mail -

Said Broumi mail

link https://doi.org/10.54216/IJNS.270213

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Vol. Volume 27 / Iss. Issue 2

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Journal of Neutrosophic and Fuzzy Systems | 2025

Neutrosophic Boundary and Neutrosophic Semi-boundary on Fuzzy Setting

The aim of this paper is to introduce the concept of fuzzy neutrosophic boundary and fuzzy neutrosophic semi-boundary of a fuzzy neutrosophic topological space. Some characterization are discussed. Several examples and properties are obtained. The aim of this paper is to introduce the concept of fuzzy neutrosophic boundary and fuzzy neutrosophic semi-boundary of a fuzzy neutrosophic topological space. Some characterization are discussed.Several examples and properties are obtained.

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E. Poongothai mail -

E. Kalaivani mail

link https://doi.org/10.54216/JNFS.100103

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Vol. Volume 10 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2026

Criminal Activity Classification in Surveillance Videos Using Deep Learning Models

Detecting and identifying crimes in real time represents a very necessary aspect of public safety. Traditional systems are human based monitoring cameras, video surveillance systems are ineffective, time consuming and prone to mistakes. Automated solutions are much needed. Using convolutional neural networks (CNNs) to efficiently examine surveillance video footage is the main goal. This work presents a crime detection system based on deep learning. the study utilize UCF Crime dataset and four deep learning models: ResNet50, EfficientNetB2, Xception, and custom (CNN) were up-graded, trained, and tested. To guarantee best model performance, the suggested approaches required careful dataset preparation, pre-processing, and strategic data separation. By means of fine-tuning, each model addressed the constraints of conventional techniques and enhanced feature extraction and classification accuracy. With extraordinary performance measures of (99.53%) accuracy, (99.07%) precision, (98.43%) recall, and a (98.69%) F1 score, experimental findings show the superiority of the suggested system. These findings reveal the system’s high dependability in detecting and classifying criminal events, thereby far surpassing other CNN-based approaches. The model runs at an average inference speed of (30 ms per frame on CPU), with a lightweight model size of around (20 MB), These results demonstrate the system’s scalability, efficiency, and strong potential for intelligent surveillance applications. This study shows how scalable and effective deep learning models transform crime detection in surveillance systems to support public safety.

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Raed Majeed mail -

Hiyam Hatem mail

link https://doi.org/10.54216/JISIoT.180208

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2026

Fault Monitoring in Transmission Lines Using Modular Neural Networks in Simulated Smart Grids

The transmission of energy is one of the main tasks of Electrical Engineering. Transmission lines are used for this purpose, which are susceptible to various problems such as short-circuit, overload, open circuit, and complex faults. From the perspective of smart grids, one of the open challenges is to have autonomous systems that allow the detection, classification, and location of faults in transmission lines. On the other hand, Artificial Neural Networks are computational tools used in classification and control tasks to be applied to different plants and systems. There are several ways to solve problems using ANNs; one is modularity. This strategy consists of dividing the problem into components that are easier to classify. In this way, a modular system is proposed that is composed of three ANNs: One for detection, one for classification, and one more for the location of faults in transmission lines. A simulation model of a three-phase electrical power system was built using Simulink MATLAB, employing a data transmission approach typical of smart grids. Supervised learning and WEKA software were used for network training. Databases were created using the potential difference and line current, as well as the ground fault impedance. The database was developed through cases and mathematical models, and the performance of the networks was evaluated in the simulated model. The results show that the proposed model allows the identification of all cases presented in the test stage (100%), which is a better performance than a single neural network (81.25%) that is responsible for detecting, classifying, and locating faults.

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Sánchez-Juárez J. R. mail -

Aldana-Franco R. mail -

Leyva-Retureta, J. G. mail -

Álvarez-.Sánchez E. J. mail -

López-Velázquez A. mail -

Aldana-Franco F. mail

link https://doi.org/10.54216/JISIoT.180209

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

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