Predictive-based Models for Efficient Energy Management in Smart Buildings

Osama; Marwa; Abdalrahman Fatikhan

doi:https://doi.org/10.54216/JCIM.150202

Journal of Intelligent Systems and Internet of Things | 2025

New Adaptive-Clustered Routing Protocol for Indoor Fire Emergencies Using Hybrid CNN-BiLSTM Model: Development and Validation

This study presents a new adaptive routing protocol for fire emergencies, leveraging a newly created dataset and a hybrid deep learning approach to optimize decision-making and data routing strategies. The developed protocol integrates a hybrid of Convolutional Neural Networks (CNNs) with Bi-Directional Long Short-Term Memory (BiLSTMs) deep learning models to predict fires at early stages, effectively managing the dynamic and unpredictable nature of fire emergencies to prevent data loss and ensure packet delivery to the base station. Exhaustive validation was conducted utilizing the standard protocol to ensure the reliability and effectiveness of the proposed approach. Experimental results demonstrate the superior performance of the proposed hybrid-deep learning model and the significant enhancements in routing efficiency and monitored data preservation for the developed protocol compared to the standard protocol. The findings are useful in providing a reliable solution for adaptive routing during emergencies.

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Ola Khudhair Abbas mail -

Fairuz Abdullah mail -

Nurul Asyikin Mohamed Radzi mail -

Aymen Dawood Salman mail

link https://doi.org/10.54216/JISIoT.140202

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Intrusion Detection System in Wireless Sensor Networks Using Machine Learning

Current industrial control systems are increasingly integrating with corporate Internet technology networks in order to fully utilize the abundant resources available on the Internet. The growing connection between industrial control systems and the internet has made them a desirable choice. Industrial control systems are in need of significant protection due to being a common target for a range of cyber-attacks. The use of the Internet of Things is currently increasing across industries due to its efficiency, and the Internet of Things is facing a security challenge. This document gives an overview of the intrusion detection system and the methods of the intrusion detection system. The purpose of this document is to examine intrusion detection methods and present the best method based on studies. Experimental results show that this system uses a combination of machine learning methods for high performance.

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Zainab S. Idan mail -

Ahmed Al-Fatlawi mail -

Hussein Akeel Hussein Alaasam mail -

Sajjad H. Hasan mail -

Ahmed Ali Talib Al Khazaali mail

link https://doi.org/10.54216/JISIoT.140203

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Reduce Energy Consumption and Increase Lifetime via Genetic Algorithm over Wireless Communication Networks

Wireless sensor networks have been identified as one of the most important technologies. A vast amount of research and development has been devoted to this area in the past decade. Nowadays, they have been applied in various fields including environment monitoring, smart building, medical care, and etc. With the advances in electronics, wireless communications, and sensor technology, more and more new opportunities have been created for the research in wireless sensor networks. However, the successful implementation of WSN faces many challenges, such as limited power, limited memory, and limited computing capability. Among them, limited power is the most critical restriction because it is usually impossible for the battery-powered sensor nodes to be recharged. Therefore, one of the main areas of interest for wireless sensor network research is how to reduce power consumption. The proposed system classifies sensor nodes into two operational modes, optimizes node deployment, and finds optimal node placements using a genetic algorithm (GA) to minimize the energy consumption of the WSN. The system's successful testing on a simulated WSN meant for radiation site identification revealed its potential for practical real-world applications.

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Mohammed Arif Nadhom Obaid Al-agar mail -

Zaynab Saeed Hameed mail -

Israa Ali Al-Neami mail -

Sergey Drominko mail -

Erina Kovachiskaya mail

link https://doi.org/10.54216/JISIoT.140204

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Smart Home Cloud Monitoring Design and Investigation Using Artificial Intelligence Strategies

Artificial intelligence (computer-based intelligence) is advancing significantly in all areas and applications of life at a high speed. The use of modern technologies has become a necessity in daily life, and smart systems have entered daily life, especially in the design of smart homes. Smart homes linked to man-made intelligence mimic the way residents live and facilitate many activities and services. Although some studies have shown how smart homes use computer-based intelligence, few applications have been reported for integrating smart technologies into installation and use of the Internet of Things. In this research, the basic problems in adaptive smart home systems, such as the development of the smart home and its synchronization with the Internet of Things, and “what is the relationship between analysis and adaptation in smart homes with simulation of intelligence algorithms” were addressed to be the focal point of this paper. Moreover, this study aims to depict the capabilities and elements of artificial intelligence in improving the performance of smart homes. In order to understand how to use artificial intelligence to build smart homes, the precise situation of applying artificial intelligence in smart home elements and the way applications are used in homes was determined. We simulated a multi-service smart home environment by designing an efficient, multi-purpose artificial intelligence algorithm to improve the control level and enhance the performance of smart home services.

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Hiba A.Tarish mail

link https://doi.org/10.54216/JISIoT.140205

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Security Inspection for Data Computing Networks Using Deep Learning Techniques

Deep learning offers practical answers for neural network models when applied to cloud registering security. Via robotization Distinguish dangers, decrease manual checking, and further develop in general security adequacy. Deep learning network models assume a pivotal part in security errands like interruption discovery, malware identification, anomaly recognition, and log examination. requires Deep Learning mix in cloud security cautiously assesses existing frameworks, characterizes goals, chooses dataset with arrangement, model tuning and last changes for consistence. Moreover, applying deep learning methods in cloud security requires thought of variables, for example, computational assets, information assortment, arrangement costs, model turn of events, mix endeavors, and continuous observing and support. This study proposes an artificial neural network (ANN) model portrayal in the cloud to track down cloud security parts and recreate security techniques and researches the essential moves toward coordinate these models in the cloud. Regarding that the adequacy of the ANN scheme relies upon cloud parameters like the nature of the preparation information and the network architecture Also, weight change calculations. The review emploies a dataset from Kaggle.com to approve the recreation and blueprints the means Partake in preparing and assessment of the ANN structure.

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Alaa Q. Raheema mail

link https://doi.org/10.54216/JISIoT.140206

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things |

Prediction and Classification of Fatty Liver Disease Using Probabilistic Neural Networks

Fatty liver disease, encompassing conditions like NAFLD (Non-Alcoholic Fatty Liver Disease) and NASH (Non-Alcoholic Steatohepatitis), is a significant global health issue linked to metabolic syndrome and increasing incidences of liver-related complications. Accurate and early detection of fatty liver illness is critical for effective intervention and management. This paper proposes a novel method for the prediction and arrangement of fatty liver disease using Probabilistic Neural Networks (PNNs), leveraging advanced machine learning techniques to enhance diagnostic accuracy and reliability. We developed a PNN-based model to classify liver conditions from a dataset comprising clinical and imaging features, including liver fat content, texture metrics, and demographic information. The PNN was chosen for its capability to handle complex, high-dimensional data and provide probabilistic outputs, which are crucial for assessing the likelihood of different disease stages and improving interpretability. The proposed methodology includes preprocessing steps to normalize and augment the data, followed by feature extraction using advanced techniques to capture relevant patterns. The PNN architecture was designed with multiple layers to process features and deliver class probabilities. The method's concert was estimated utilizing average system of measurement such as accuracy, precision, recall, and F1-score, demonstrating its efficacy in distinguishing between different stages of fatty liver disease. Experimental results indicate that the PNN model achieves high classification accuracy and outperforms traditional machine learning methods in detecting fatty liver illness. This study highlights the potential of PNNs in enhancing diagnostic processes and providing a robust tool for clinicians. Future work will concentrate on expanding the dataset, refining the model, and integrating it into clinical workflows to support better patient outcomes in liver disease management

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Appanaboyina Sindhuja mail -

Seetharam Khetavath mail

link https://doi.org/10.54216/JISIoT.140207

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Automated Detection and Classification of Pneumonia using Deep Learning and Convolutional Neural Networks

Lung disease is considerable deprivation from health standpoint. These include chronic obstructive pulmonary illnesses, asthma, lung fibrosis, lung parenchyma illnesses, and tuberculosis among others. It is highly critical in the early phase of lung illnesses when they are the most treatable. Many of these were made for the purpose of applying machine learning and image processing. Many types of DL methods including CNN, VNN, VGG networks, capsule networks are used during lung illness prediction process. Following the release of the book on Pandemic Covid-19, many projects have been carried out at international level intending to study the feasibility of such work for prediction of future events. Pneumonia is a lung infection that starts earlier in the disease course and is closely associated with the virus (pneumonia condition), which was responsible for considerable chest infection in some covid-positive individuals. While doctors are no strangers to lung diseases and their complicated nature, many will find it difficult in some of them to make distinctions between common pneumonia and the Covid-19. X-ray imaging of the chest provides the highest degree of accuracy in suffem lung diseases. In this work, a novel approach for the calculation of lung illnesses such as pneumonia and COVID-19 is proposed. The data source for this method is Chest X-ray pictures taken from patients. The system includes characteristics such as the extraction of features, the prediction of illnesses, and the precise and adaptive evaluation of ROI, the collecting of datasets, and the enhancement of image quality. In future, this research can be extended with IOT devices for the recognition of COVID-19 and pneumonia.

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Gurijala Anita mail -

Sunil Singarapu mail

link https://doi.org/10.54216/JISIoT.140208

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Research on Big Data efficient hybrid cloud storage model and algorithm based on 5G network

Due to the large demand for big data storage capacity, the storage intensity index is not calculated in the current big data cloud storage process, resulting in a high storage space usage. This paper proposes a big data efficient hybrid cloud storage model and algorithm under 5G network. The model is based on the 5G network performance framework and consists of three parts: users, private cloud and public cloud storage service providers. The purpose of efficient hybrid cloud storage of big data is achieved by using consistent hash algorithm. The simulation results show that the above algorithm occupies less storage memory space, the device load variance is smaller, the overall system load is more stable and balanced, and the average response is fast, which provides a favorable basis for the efficient hybrid cloud storage algorithm of big data.

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Lei Hu mail -

Yangxia Shu mail

link https://doi.org/10.54216/JISIoT.140209

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Adversarial Machine Learning Challenges in Modern Network Security Systems

Hostile machine learning has network security issues that reduce prediction model accuracy. A full defence against these assaults entails establishing hostile scenarios, strengthening models via strategy training, and applying powerful defences. Small adjustments introduce antagonistic inputs into the research. These teach the model to recognize and withstand deception attempts. The proposed solution competed with Trust Shield, Secure Guard, Defend, and Adversary Block in rigorous performance testing. The recommended strategy has a 95.0% success rate for discovering assaults and a much lower 5.0% false positive rate. This is much superior to conventional approaches. Due to its modest accuracy loss and rapid response, it's effective at fighting assaults. This comprehensive overview demonstrates the wide-scale application of the strategy with minimal resources. Finally, this research emphasizes the need for robust and adaptable AI security. This will assist in creating secure and trustworthy AI solutions to protect sensitive data and ensure prediction model accuracy in an increasingly hostile future.

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Lissett Margarita Arévalo Gamboa mail -

Alberto León-Batallas mail -

Jhonny Ortiz-Mata mail -

Denis Mendoza-Cabrera mail

link https://doi.org/10.54216/JCIM.150201

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Predictive-based Models for Efficient Energy Management in Smart Buildings

The integration of sensing technologies with residential buildings raises the concept of a smart home, which has facilitated the life of the habitant nowadays. This technology helps us to track and understand the behavior of the client in the house to give him maximum comfort. A neighborhood area is an interconnected set of houses that exist in the same geographical region and share the same energy resources. The most important component in the process of decision-making is the energy usage in the smart building. The energy optimization problem in the smart building created a challenge for enterprises and the government for a long time. A lot of research were made to solve this energy optimization problem. One of these problems is the organization of energy usage within a neighborhood area network. The main challenges are to maintain the user comfort in each house and to not exceed the total energy offered to the network. For this, we proposed a technique that predicts, based on historical data of each house, its future behavior and created for each one a weekly schedule with hourly annotated field with: high, normal, or low, where each one represents the amount of energy user is able to use at this time. At the end, an incentive-based program is created to give the client an incentive on his bill if he used the daily high energy consumption in the annotated high in his schedule. To create the schedules, we extracted some features from the data, then we used the genetic algorithm to create schedules, then we did an improvement to the technique using dynamic programming that stores the features of a house with created schedule, later when we meet a similar house we can directly give a schedule that fits the need.

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Osama Mohammed mail -

Marwa Ibrahim mail -

Abdalrahman Fatikhan Ataalla mail

link https://doi.org/10.54216/JCIM.150202

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new