International Journal of Intelligent Networks最新文献

{"title":"A systematic review on early prediction of Mild cognitive impairment to alzheimers using machine learning algorithms","authors":"K.P. Muhammed Niyas ,&nbsp;P. Thiyagarajan","doi":"10.1016/j.ijin.2023.03.004","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.03.004","url":null,"abstract":"<div><h3>Background</h3><p>A person consults a doctor when he or she is suspicious of their cognitive abilities. Finding patients who can be converted into Alzheimer's in the future is a difficult task for doctors. A person's dementia can be converted into several types of dementia conditions. Among all dementia, Alzheimer's is considered to be the most dangerous as its rapid progression can even lead to the death of an individual. Consequently, early detection of Alzheimer's would help in better planning for the treatment of the disease. Thereby, it is possible to reduce the progression of the disease. The application of Machine Learning algorithms is useful in accurately identifying Alzheimer's patients. Advanced Machine Learning algorithms are capable of increasing the performance classification of future AD patients. Hence, this study is made on a number of previous works from 2016 onwards on Alzheimer's detection. The aspects such as the country of the participants, modalities of data used and the features involved, feature extraction methods used, how many follow-up data were used, the period of Mild Cognitive Impairment to Alzheimer's Disease converters predicted, and the various machine learning models used in the previous studies of Alzheimer's detection are reviewed in this study. This review helps a new researcher to know the features and Machine Learning models used in the previous studies for the early detection of Alzheimer's. Thus, this study also helps a researcher to critically evaluate the literature on Alzheimer's disease detection very easily as the paper is organized according to the various steps of the Machine Learning process for Alzheimer's detection in a simplified manner.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 74-88"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fairness-driven link scheduling approach for heterogeneous gateways for digital twin enabled industry 4.0","authors":"Suvarna Patil ,&nbsp;Mandeep Kaur ,&nbsp;Katarina Rogulj","doi":"10.1016/j.ijin.2023.06.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.06.001","url":null,"abstract":"<div><p>The advent of Industry 4.0 has brought with it the integration of digital twin technology, which has enabled businesses to develop a virtual replica of their physical assets. This technology allows businesses to optimize their operations and improve their overall efficiency. However, the successful implementation of digital twin technology in Industry 4.0 heavily relies on the effective utilization of gateways. A significant challenge in gateway utilization is the fair allocation of resources, particularly in heterogeneous environments where gateways have different capabilities<strong>.</strong> Digital Twin is helping Industry 4.0 vision by connecting the authorized people to the exact data and processes to protect the data/assets from unauthorized access. It is accomplished by connecting sensing devices using a unique addressing system and transmitting their combined data to the Internet of Things (IoT) cloud. Massive volumes of heterogeneous data have resulted from the rapid growth of IoT applications and services. As a result, evaluation of data which affects the Digital Twin enabled industry is studied in this article which focuses on data traffic generated from different Industry 4.0 applications and protection of data along with the industry assets is looked by Digital Twin technology. IoT gateways are currently used to connect the devices from various technologies to the Digital Twins. In such networks, sudden increase in demand of IoT gateways will increase with the increase in IoT devices and the operational cost will also be increased. In the proposed system, low-cost specific gateways are proposed to minimize cost and maximize network performance for protecting assets of smart city through Digital Twin technology. In order to accomplish effective resource allocation in a Digital Twin based infrastructure, data transmission fairness at every gateway is accomplished in an IIoT network by considering link scheduling issues. To address these issues and provide fairness in heterogeneous networks with enhanced data transfer, two steps solution is implemented. The Long Short-Term Memory (LSTM) technique is used in the initial step of traffic prediction to assess the minimal time of prior traffic conditions before being applied to estimate dynamic traffic. In the second step, effective link scheduling and selection are made for each wireless technology, taking into account predicted load, gateway distance, link capacity, and estimated time. More data is transmitted at maximum capacity as a result of improved data transfer fairness for all gateways and then the data is protected by Digital Twin technology. Simulated results show that our suggested strategy performs better than other approaches by obtaining maximum network throughput in Industry 4.0 to provide protective solutions using Digital Twin technology.</p><p>Index Terms – Internet of Things (IoT), Link Scheduling, Traffic Prediction, Machine Learning (ML), Industry 4.0.</p></di","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 162-170"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic algorithm with self adaptive immigrants for effective virtual machine placement in cloud environment","authors":"P. Karthikeyan","doi":"10.1016/j.ijin.2023.07.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.07.001","url":null,"abstract":"<div><p>In cloud environments, optimization of resource utilizations is one among the predominant challenges. The two sub-research topics are cloud resource prediction and allocation. A few contributions to virtual machine (VM) placement techniques have been identified in the literature. In order to efficiently put up the virtual machine (VM) on the physical machine (PM), a Self Adaptive Immigrants with Genetic Algorithm (SAI-GA) is presented in this study. Based on CPU and memory usage, the proposed technique would forecast the best PM for each VM. The algorithm will adjust itself with the appropriate immigrant based on the history of past VM placement to find the best VM placement. In this paper, the VM live dataset from the CSAP lab at SNU in Korea has been used. For the purpose of demonstrating the significance of the findings, a number of non-parametric tests were used to evaluate how well the proposed SAI-GA performed. The outcomes demonstrate that the suggested approach makes a considerable contribution to the placement of VMs in cloud environments.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 155-161"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Customer churning analysis using machine learning algorithms","authors":"B. Prabadevi,&nbsp;R. Shalini,&nbsp;B.R. Kavitha","doi":"10.1016/j.ijin.2023.05.005","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.05.005","url":null,"abstract":"<div><p>Businesses must compete fiercely to win over new consumers from suppliers. Since it directly affects a company's revenue, client retention is a hot topic for analysis, and early detection of client churn enables businesses to take proactive measures to keep customers. As a result, all firms could practice a variety of approaches to identify their clients early on through client retention initiatives. Consequently, this study aims to advise on the optimum machine-learning strategy for early client churn prediction. The data included in this investigation includes all customer data going back about nine months before the churn. The goal is to predict existing customers' responses to keep them. The study has tested algorithms like stochastic gradient booster, random forest, logistics regression, and k-nearest neighbors methods. The accuracy of the aforementioned algorithms are 83.9%, 82.6%, 82.9% and 78.1% respectively. We have acquired the most effective results by examining these algorithms and discussing the best among the four from different perspectives.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 145-154"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ARTP: Anomaly based real time prevention of Distributed Denial of Service attacks on the web using machine learning approach","authors":"P. Krishna Kishore,&nbsp;S. Ramamoorthy,&nbsp;V.N. Rajavarman","doi":"10.1016/j.ijin.2022.12.001","DOIUrl":"https://doi.org/10.1016/j.ijin.2022.12.001","url":null,"abstract":"<div><p>Distributed Denial of Service (DDoS) attack is one of the most destructive internet network attacks, denying legitimate users access to resources and networks by maliciously blocking available computing resources. Intruders send a large number of packets to the network in order to create a crowding effect. Unlike a Denial of Service (DoS) attack, where a single compromised source generates all of the traffic, a Distributed Denial of Service (DDoS) attack generates traffic from multiple compromised nodes spread across multiple geographies. To address the challenges posed by the Distributed Denial of Service (DDoS) attack, several researchers proposed a variety of solutions for early detection and prevention of the attack. Effective solutions for the prevention and early detection of Distributed Denial of Service (DDoS) attacks, on the other hand, have yet to be developed, and the problem remains a prominent research focus area. This paper tries to present a novel and optimal solution for detecting Distributed Denial of Service (DDoS) attacks on internet networks more quickly and accurately. The proposed model is an anomaly-based real-time prevention model for web networks. The model is based on machine learning principles and can effectively counter new types of Distributed Denial of Service (DDoS) attacks. To demonstrate the efficiency, accuracy, model robustness, and relative of the proposed model, a simulation study was run on an LLDOS session log, and the results indicated that the model performed better than benchmark models found in the literature.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 38-45"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unsupervised machine learning technique for classifying production zones in unconventional reservoirs","authors":"Karrar A. Abbas ,&nbsp;Amir Gharavi ,&nbsp;Noor A. Hindi ,&nbsp;Mohamed Hassan ,&nbsp;Hala Y. Alhosin ,&nbsp;Jebraeel Gholinezhad ,&nbsp;Hesam Ghoochaninejad ,&nbsp;Hossein Barati ,&nbsp;James Buick ,&nbsp;Paria Yousefi ,&nbsp;Reham Alasmar ,&nbsp;Salam Al-Saegh","doi":"10.1016/j.ijin.2022.11.007","DOIUrl":"https://doi.org/10.1016/j.ijin.2022.11.007","url":null,"abstract":"<div><p>Significant amounts of information are rapidly increasing in bulk as a consequence of the rapid development of unconventional tight reservoirs. The geomechanical and petrophysical characteristics of the wellbore rocks influence the sweet and non-sweet areas of tight unconventional reservoirs. Using standard approaches, such as data from cores and commercial software, it is difficult and costly to locate productive zones. Furthermore, it is difficult to apply these techniques to wells that do not have cores. This study presents a less costly way for the systematic and objective detection of productive and non-productive zones via well-log data using clustering unsupervised and supervised machine learning algorithms. The method of cluster analysis has been used in order to classify the productive and non-productive reservoir rock groups in the tight reservoir. This was accomplished by assessing the variability of the reservoir characteristics data that are forecasted by looking at the dimensions of the well logs. The Support vector machine as a supervised machine learning algorithm is then used to evaluate the classification accuracy of the unsupervised algorithms based on the clustering labels. The application made use of approximately ten different variables of rock characteristics including zonal depth, effective porosity, permeability, shale volume, water saturation, total organic carbon, young's modulus, Poisson's ratio, brittleness index, and pore size. The findings show that both clustering techniques identified the sweet areas with high accuracy and were less time-consuming.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 29-37"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing vulnerability scoring for information security in intelligent computers","authors":"Qingkun Zhu","doi":"10.1016/j.ijin.2023.09.002","DOIUrl":"https://doi.org/10.1016/j.ijin.2023.09.002","url":null,"abstract":"<div><p>Rapid computer and internet technology advancements have catalyzed continuous breakthroughs and innovations in related fields, significantly impacting social production and individual lifestyles. A critical challenge addressed here is the inadequacy of existing methods for assessing computer vulnerabilities. The vulnerability scoring model for intelligent vehicles (VSMIV) is proposed to solve the issue in intelligent computers. A key aspect involves optimizing the attack vector and attack complexity of common vulnerabilities and exposures (CVEs) to align with the specific behaviour of intelligent computers. The model's effectiveness is enhanced by integrating four distinct indicators: property security, privacy security, functional security, and life security. The diversity ratings indicate that the VSMIV scoring system has the highest diversity of system distribution at 95%, followed by CVSS_IoT at 88%, and CVSS with a slightly lower diversity score of 85%. The proposed methodology holds promise in establishing a new paradigm for strengthening the security of computer systems, thereby stimulating their flexibility in the face of emerging threats.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"4 ","pages":"Pages 253-260"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50194835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Effective Lightweight Homomorphic Cryptographic Algorithm for data security in cloud computing","authors":"Fursan Thabit ,&nbsp;Ozgu Can ,&nbsp;Sharaf Alhomdy ,&nbsp;Ghaleb H. Al-Gaphari ,&nbsp;Sudhir Jagtap","doi":"10.1016/j.ijin.2022.04.001","DOIUrl":"10.1016/j.ijin.2022.04.001","url":null,"abstract":"<div><p>Cloud computing is a technology in which the resources are delivered as services. Users can access them anywhere, anytime via the Internet without any need to know the infrastructure knowledge, experience, or even authority that provides such services. It has become an important medium for enterprises to develop their networks because of these resources. With the growing need for cloud computing, security is becoming important for both individuals and business needs. Many researchers have studied the security of cloud computing. Still, security gaps or threats are increasing as the demand for cloud computing connectivity increases. Because a third party mostly provides cloud platforms to the cloud client, data protection in the cloud is the primary issue. This paper presents a novel, effective lightweight homomorphic cryptographic algorithm which contains two layers of encryption. The first layer uses the new effective, light-weight cryptographic algorithm and the second layer multiplicative homomorphic schemes considered for improving security data in cloud computing. This approach offers both symmetric and asymmetric cryptography features. The proposed approach's performance is evaluated using a variety of metrics, including memory, computational time and (key sensitivity), statistical analysis, image histogram, and entropy change analysis. The proposed algorithm's experimental findings showed a high level of security and an apparent improvement in encryption execution time, memory usage, and throughput. When compared to the cryptographic systems widely used in cloud computing.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"3 ","pages":"Pages 16-30"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603022000033/pdfft?md5=742bb8b72e5d6808048aeee52d95b60f&pid=1-s2.0-S2666603022000033-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73726481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An optimal construction of smart aged homes based on SDLC using smart sensors and agent networks","authors":"R. Subha ,&nbsp;Jun Zhang","doi":"10.1016/j.ijin.2022.09.003","DOIUrl":"10.1016/j.ijin.2022.09.003","url":null,"abstract":"<div><p>With the increased growth of the added population group and advancements in health informatics, the smart aged home needs to be equipped with intelligent systems and various electronic devices. Industry 5.0 is yet another revolution that bought about the transformation in the practices concerning the use of intelligent technology with smart sensors and agent networks. Smart sensors enabled industry 5.0 is equipped with automation and self-monitoring ability that assists with analyzing the issues without the intervention of human beings. We, in this paper, proposed an iterative development process based on the smart sensor and agent networks of the SDLC software development models. The proposed model is developed by synchronizing the smart aged home with the cyber-physical system of industry 5.0. The quality value matrix is introduced as a tool to identify the requirements of the residents and the stakeholders. The feasibility of the proposed work is assessed with the help of an agent-based simulation system.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"3 ","pages":"Pages 138-142"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603022000161/pdfft?md5=d22f609597bb46dbb1ec27cfd354bd20&pid=1-s2.0-S2666603022000161-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78703689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new secure 2PL real-time concurrency control algorithm (ES2PL)","authors":"Ebrahim Abduljalil ,&nbsp;Fursan Thabit ,&nbsp;Ozgu Can ,&nbsp;Pritam R. Patil ,&nbsp;S.B. Thorat","doi":"10.1016/j.ijin.2022.05.001","DOIUrl":"10.1016/j.ijin.2022.05.001","url":null,"abstract":"<div><p>A secure concurrency control algorithms must be devoid of covert channels originating from data conflicts between transactions, in addition to ensuring database integrity. Current secure concurrency control algorithms mistreat transactions with higher access classes. Because it better solves the priority inversion problem. A widely used concurrency control algorithm is the two-phase locking with high priority (2 PL-HP). The inclusion of cyclic restart, deadlock, unnecessary abort, pseudo-priority inversion, and starvation, on the other hand, may reduce its performance. In this paper, a new enhanced of secure two-phase locking protocol with multilevel secure distributed database systems is presented, the idea of enhanced secure Two-Phase locking protocol by adding the security level to assign the priority to the transaction. The enhanced add a secure manager to decide the level of the security and give the instruction to process the transaction in real lock with data x/virtual lock with version data x0 or decided to Abort the transaction based on the security level. The suggested study intends to reduce transaction miss rate by addressing the following issues: deadlock by splitting the execution stage into locking and processing phases, it reduces unnecessary cycle restart, transaction aborts by checking the security priority level (high, medium, low), and generating virtual lock and versions data. Finally, simulation performance confirms that improvement in transactions miss's percent and reduction in transactions' rollbacks compared to other secure concurrency control.</p></div>","PeriodicalId":100702,"journal":{"name":"International Journal of Intelligent Networks","volume":"3 ","pages":"Pages 48-57"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666603022000057/pdfft?md5=d2e2e645ad1d978c06bc941ecae293c4&pid=1-s2.0-S2666603022000057-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91479633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}