The Impact of Cloud Computing on Network Security Risk for Organization Behaviours

Nagham; Nada Mahdi; Sanaa

doi:https://doi.org/10.54216/JCIM.150121

International Journal of Neutrosophic Science | 2025

A Fuzzy Adaptive Control Chart as an Alternative to Neutrosophic Techniques for Handling Imprecise Data

Quality control (QC) charts are essential for ensuring industry process stability, but imprecise data make traditional methods unuseful in such a case. Neutrosophic control charts are available to handle the imprecise data. This article learns fuzzy logic as an approach of handling uncertainty more suitably than neutrosophic approaches. Fuzzy QC charts make use of fuzzy numbers, membership functions and fuzzy control limits and as such are more realistic compared to conventional charts. The study introduces a Fuzzy Adaptive Exponentially Weighted Moving Average (FAEWMA) chart, specifically designed for univariate data in a fuzzy atmosphere. The FAEWMA chart, incorporating α-cuts, is engineered to detect shifts in process means, showcasing its effectiveness through both theoretical development and practical applications. This approach improves decision-making in process control and represents a significant advancement over traditional QC methods.

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Mohammed A. Alshahrani mail -

Imad Khan mail -

Wojciech Sumelka mail

link https://doi.org/10.54216/IJNS.250212

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Internet of Things Assisted Sleep Quality Recognition using Hunger Games Search Optimization with Deep Learning on Smart Healthcare Systems

Rapid urbanization needs major cities that change into smart cities to increase our lifestyle with respect to transportation, people, government, environmental sustainability, and more. In recent times, Internet of Things (IoT) and healthcare wearables have played a vital play in the progress of smart cities by providing enhanced healthcare services and an entire standard of living. Wearables offer real-time health records to individuals and healthcare providers, permitting for proactive management of chronic conditions and early recognition of health problems. While sleep is of major importance for a healthy life, it can be required to forecast sleep quality.  Insufficient sleep affects mental health, physical, and emotional, and is a solution to many illnesses like heart disease, insulin resistance, stress, heart disease, and so on. Recently, deep learning (DL) techniques can be deployed to forecast the quality of sleep dependent upon the wearables data in the awake duration. Therefore, this paper presents an automated sleep quality recognition using hunger games search optimization with deep learning (ASQR-HGSODL) technique in the IoT-assisted smart healthcare system. The ASQR-HGSODL technique allows the IoT devices to perform a data collection process, which collects the data related to sleep activity. For the feature selection process, the ASQR-HGSODL technique applies an arithmetic optimization algorithm (AOA). For the prediction of sleep quality, the ASQR-HGSODL technique implements a convolutional long short-term memory (ConvLSTM) approach. Lastly, the HGSO technique has been applied for the optimum hyper parameter selection of the ConvLSTM approach. To exhibit the effectual prediction results of the ASQR-HGSODL approach, a range of simulation can be carried out. The investigational outputs highlight the improved outcome of the ASQR-HGSODL. technique with other DL methodologies.

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M. B. Sudhan mail -

Deepak Kumar .A mail -

M. S. Minu mail -

Mathan Kumar Mounagurusamy mail -

S. Navaneethan mail -

B. Venkataramanaiah mail

link https://doi.org/10.54216/JISIoT.140110

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Quasi Oppositional Jaya Algorithm with Computer Vision based Deep Learning Model for Emotion Recognition on Autonomous Vehicle Drivers

Facial emotion recognition (FER) technology in autonomous vehicle drivers can considerably strengthen the efficiency and safety of the driving experience. The system can analyze facial expressions in real-time by employing advanced computer vision (CV) techniques, which identify emotions such as stress, fatigue, or distraction. This enables the vehicle to adapt its behavior, triggering interventions or alerts where applicable to alleviate possible threats. Ensuring the emotional well-being of the driver promotes a safer road environment, improving overall road safety and diminishing the possibility of accidents in the era of autonomous vehicles. FER using (Deep Learning) DL is an advanced technique that leverages deep neural network (DNN) to automatically interpret and identify emotions from facial expressions. DL algorithms, especially Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) have attained outstanding results in this field since they allow us to learn temporal dependencies hierarchy and features within the data. This research develops a novel Computer Vision with Optimal DL-based Emotion Recognition (CVODL-ER) model for Autonomous Vehicle Drivers. The CVODL-ER method concentrates on the automated classification of various sorts of emotions of autonomous vehicle drives. To accomplish this, the CVODL-ER technique makes use of the SE-ResNet model for learning intrinsic patterns from the driver's facial images. Besides, the hyper parameter tuning of the SE-ResNet model takes place via a quasi-oppositional Jaya (QO-Jaya) algorithm. For the recognition of driver emotions, the CVODL-ER system executes the deep belief network (DBN) algorithm. The performance analysis of the CVODL-ER technique takes place using a benchmark facial image database. The obtained results underline the improved efficiency of the CVODL-ER technique over other models.

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Rajesh .D mail -

S. Thenappan mail -

Prachi Juyal mail -

Thiyagarajan .V .S mail -

D. M. Kalai Selvi mail -

J. Rajeswari mail -

M. Hema Kumar mail -

V. Saravanan mail

link https://doi.org/10.54216/JISIoT.140111

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Stacked Ensemble Machine Learning based Skin Cancer Detection and Classification Model

Skin cancer is most top three critical kinds of cancer due to damaged DNA, which is cause death. This damaged DNA begins cells for growing uncontrollably and currently it can be obtaining improved quickly. It is several researches on the computerized examination of malignancy from the skin cancer image. But, study of these images are very difficult taking several troublesome issues such as light reflections on the skin surface, differences from the color illumination, sizes of lesions, and distinct shapes. Thus, the outcome, evidential automatic detection of skin cancer are appreciated for developing the accuracy and efficiency of pathologists at the beginning phases. This manuscript develops a Stacked Ensemble Machine Learning based Skin Cancer Detection and Classification (SEML-SKCDC) approach. The presented SEML-SKCDC technique majorly aims to offer ensemble of three ML models for skin cancer classification. In the presented SEML-SKCDC technique, median filtering and contrast enhancement is performed at the pre-processing stage. To generate feature vectors, the honey badger algorithm (HBA) with EfficientNet method has been exploited in this work. At last, an ensemble of k-nearest neighbor (KNN), random forest (RF), and feed forward neural network (FFNN) approaches are applied for skin cancer classification. The simulation evaluation of the SEML-SKCDC system on skin cancer database depicts the developments of the SEML-SKCDC algorithm with recent methods.

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K. Uma Maheswari mail -

C. P. Indumathi mail -

S. Usha mail -

S. Gayathri Priya mail

link https://doi.org/10.54216/JISIoT.140112

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Energy Assessment based Smart Sustainable Production in Wireless Environment Using Internet of Agricultural Things (IoAT)

The attainment of smart sustainable production of energy is the goal, which is being pursued globally. In the field of agricultural system, several challenges are present and as well, it is combined with the climatic crises. In general, the renewable energy resources is the origin of energy production and consumption so that using this energy source it is possible to improve the ecologically and social agriculture. Due to the expansion of renewable energy, the concept of Agrivoltaic System is created which convert the food production to energy generation process. Currently many of the research are developed to increase the crop yield and energy production. In this article, we concentrate on intelligent farming in agrivoltaic system with the help of Internet of Agricultural Things (IoAT). It focuses on newer preliminary methods like fluid dynamic system, improved photovoltaic (PV) module, land equivalent ratio analysis and shading ratio calculation. In IoAT based system, crop field analysis, energy production model, sensor localization process, cost optimization and fault diagnosis processes are concentrated. So that the effective outcomes are attained in the cultivation of crops like melon, bean, millet, and cucumber. The parameters, which are calculated in the results analysis, are shading ratio and temperature, crops-based analysis, and energy-based analysis. With the help of IoAT system both, the crop yield and electricity production is increased.

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Ahmed N. Rashid mail -

Ahmed Mahdi Jubair mail

link https://doi.org/10.54216/JISIoT.140113

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

A Hybrid GA-GWO Method for Cyber Attack Detection Using RF Model

Currently, building a high-performance attack detector for cyber threat should be an essential and challenging task to secure cloud system from malicious activities. Traditional methodologies have become subject to the challenge of overfitting, distributive and intricate system layout, comprehensibility and more extended time particles. Therefore, the proposed contribution can be an efficient solution to design and develop a secure system, which is able to recognize cyber threats from cloud systems. It includes preprocessing and normalization, feature extraction, optimization as well prediction modules. Normalization with the relevant per batch fast Independent Component Analysis (ICA) model. A Genetic Algorithm (GA) - Gray Wolf Optimization (GWO) is then used to select the discriminatory features for training and testing phases. In the end, GAGWO- Random Forest (RF) is employed to classify the flow of data as insider or outsider. The detection system is implemented by taking popular and publicly available datasets like BoT-IoT, KDD Cup’99 etc. The various percentage indicators of feasibility are used as a validation purpose like detection accuracy measuring and comparing with the suggested GAGWO-RF system. Overall Accuracy: The proposed GAGWO-RF system achieved an average accuracy rate at 99.8% on all datasets the used. From the performance study, we have noted that GAGWO-RF security model performs better than other models.

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Abdulrahman Fatikhan Ataala mail -

Khudhair Abed Thamer mail -

Ahmed Hikmat Saeed mail -

Mohammed Yousif mail -

Ahmad Salim mail -

Qusay Hatem Alsultan mail -

Salim Bader mail

link https://doi.org/10.54216/JCIM.150117

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

A Public Key Infrastructure Based on Blockchain for IoT-Based Healthcare Systems

Real-time health monitoring and data collection are possible now due to the introduction of Internet of Things (IoT) in modern healthcare systems. Continuous monitoring enables healthcare providers to find and treat potential health problems early, tailor treatment plans specific to the individual patients, and make better clinical decisions resulting in a higher quality of care. From the benefits of integrating IoT in healthcare to security issues being raised when data is collected or transmitted (as health information becomes a sensitive resource). Patient's health information is very confidential and secrecy, any act that disclosed this data in the wrong way can have more implications than just patient identity thefts and financial fraudulence. In this study, we introduce that in order to solve the security and privacy issues of IoT devices in healthcare systems; we present Block chain-based Security-enhanced Public Key Infrastructure (PKI). The solution integrates the decentralized component of blockchain with its automated and standardized functionality for processing all actions afterwards, which allows such a data access as never before. This is a unique feature of blockchain: once data has been entered onto the ledger, it cannot be changed or deleted - meaning that an irrevocable record exists for each transaction. These provide future IoT devices with medical data that remain compliant keeping your health information sanitary. The other advantage of this decentralized solution is that it allows data to be accessed and stored globally, thus improving the availability and robustness of all components in case anyone fails. The Public Key Infrastructure (PKI) on an already existing blockchain platform, this only makes its security even more solid. Our solution assigns the reliability of safety and encrypted interaction among different section in our healthcare infrastructure through PKI cryptographic keys with digital certificates. Additionally, the proposed blockchain PKI improves security while addressing scalability and interoperability challenges that traditional centralized systems cannot solve, all without relying on an expensive third-party certifying authority.

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Salah N. Mjeat mail -

Mohammed Yousif mail -

Salim Bader mail -

Osama Mohammed mail -

Ahmed Hikmat Saeed mail

link https://doi.org/10.54216/JCIM.150118

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Analysis of Wazuh SIEM's Effectiveness in Cloud Security Monitoring

In today’s rapidly evolving digital landscape and interconnected, organizations are increasingly dependent on cloud -based infrastructure, which introduces significant cybersecurity challenges due to escalating cyber threats and attacks. To effectively manage these threats, a central monitoring system is essential. Security Information and Event Management (SIEM) solution address these issues by providing real-time monitoring and analysis of security events. This research investigates the efficiency of the Wazuh SIEM system in monitoring AWS cloud services, EC2 instance, and File integrity. Wazuh automates the collection, centralization, and analysis of security events. This approach enables the detection of unauthorized activities, monitoring of file integrity, and collection of user activity logs in real-time. This study evaluates Wazuh SIEM's capabilities by executing different types of attacks in an AWS cloud environment. The result was that it generated 1774 security alert within one week. The findings demonstrate that Wazuh SIEM provides comprehensive security monitoring and threat detection, offering significant advantages for organizations security that utilize cloud services.

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Wasan Saad Ahmed mail -

Ziyad Tariq Mustafa AL-Ta’I mail

link https://doi.org/10.54216/JCIM.150119

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Anomaly Detection Improvement in Computer Communication Networks using Machine Learning Techniques

The issue of force misfortune in wireless sensor networks is one of the fundamental points and central defects that should be defeated in building any coordinated computer information trade and communications framework. Where numerous new examinations have given the idea that talk about this point and recommended various techniques and systems of their sorts, proficiency, and intricacy to take care of the issue of energy misfortune in far off sensors in advanced wireless sensor networks. The WSN networks rely upon the sixth-generation innovations by giving a better system than the pace of sending and getting data and giving permitting all over; likewise, the sixth generation crossing points embrace a smart technique for information transmission in WSNs. Sixth generation is the option in contrast to the fifth-generation cellular technique, where 6G frameworks can apply a larger number of frequencies than 5G frameworks and produce a lot higher transmission capacity with lower idleness. In this review, the hardships experienced in terahertz (THz) advances in wireless sensor networks will be demonstrated, including way obstacles that are viewed as the primary test; Additionally, the attention will be on tracking down answers for keep up with the best and least energy misfortune in the WSN networks by proposing machine learning systems that will show exceptional outcomes through effectiveness measures and ideal energy venture.

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Hiba A.Tarish mail

link https://doi.org/10.54216/JCIM.150120

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

The Impact of Cloud Computing on Network Security Risk for Organization Behaviours

Cloud computing presents a new trend for IT and business services which typically involves self-service access over internet. Over these features, cloud computing has the advantages to enhance IT and business ways by offering cost efficiency, dynamically scalable, and flexibility. However, using cloud computing has raised the level of the network security risk due to the services are presented by a third party. In addition, to maintain the service availability and support data collections. Understanding these risks through cloud computing help the management to protect their system from security attacks. In this paper, the most serious and important risks and threats of the cloud computing are discussed. The main vulnerabilities is identifying with the literature related to the cloud-computing environment with possible solutions to overcome these threats and risks.

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Nagham Hamid mail -

Nada Mahdi Kaitan mail -

Sanaa Mohsen mail

link https://doi.org/10.54216/JCIM.150121

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new