Journal of Intelligent Systems and Internet of Things最新文献

{"title":"Clustered IoT Based Data Fusion model for Smart Healthcare Systems","authors":"A. Abdelaziz, A. N. Mahmoud","doi":"10.54216/jisiot.060202","DOIUrl":"https://doi.org/10.54216/jisiot.060202","url":null,"abstract":"Futuristic sustainable computing solutions in e-healthcare applications were depends on the Internet of Things (IoT) and cloud computing (CC), has provided several features and realistic services. IoT-related medical devices gather the necessary data like recurrent transmissions in health limitations and upgrade the exactness of health limitations all inside a standard period. These data can be generated from different types of sensors in different formats. As a result, the data fusion is a big challenge to handle these IoT-based data. Moreover, IoT gadgets and medical parameters based on sensor readings are deployed for detecting diseases at the correct time beforehand attaining the rigorous state. Machine learning (ML) methods play a very significant task in determining decisions and managing a large volume of data. This manuscript offers a new Hyperparameter Tuned Deep learning Enabled Clustered IoT Based Smart Healthcare System (HPTDLEC-SHS) model. The presented HPTDLEC-SHS technique mainly focuses on the clustering of IoT devices using weighted clustering scheme and enables disease diagnosis process. At the beginning level, the HPTDLEC-SHS technique exploits min-max data normalization technique to convert the input data into compatible format. Besides, the gated recurrent unit (GRU) model is utilized to carry out the classification process. Finally, Jaya optimization algorithm (JOA) is exploited to fine tune the hyperparameters related to the GRU model. To demonstrate the enhanced performance of the HPTDLEC-SHS technique, an extensive comparative outcome highlighted its supremacy over other models.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133408821","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":"Granulation-Based Data Fusion Approach for a Critical Thinking Worldview Information Processing","authors":"A. Madhuri, Veerapaneni Esther .., S. Praveen, M. Altaee, Ibrahim N. Abdullah","doi":"10.54216/jisiot.090104","DOIUrl":"https://doi.org/10.54216/jisiot.090104","url":null,"abstract":"Natural computation, motivated by the organic game arrangement, is a knowledge base field that formalizes the measurements seen in living organic entities to plan machine techniques to tackle complicated issues or to plan artificial structures with additional traditional behaviors. Seeable corporations disconnected from natural wonders, reminiscent of mind demonstration, self-association, self-redundancy, Darwinian resistance, self-evaluation, discernment, and granulation, nature is crammed as a supply of motivation to advance competition. Computational devices or frameworks accustomed solve complex problems. The ideal, nature-motivated primary computation models used for such sweetening incorporate artificial neural organizations, spongy reasoning, arduous set, biological process calculations, shape mathematics, DNA registration, artificial life, And granular or insight-based processing. The granulation of information within the granular register is an innate attribute of human thought and therefore the life of thought acted call at regular daily existence. This paper illustrates the importance of normal recording in terms of granulation-based data preparation models, for example, neural organizations, soft and ugly sets, and their hybridization. we have a tendency to emphasize the bio-sensitive inspiration, designing standards, application zones, open scan problems, and testing issues for these models.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116107356","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 Glowworm Swarm Optimization Driven Gated Recurrent Unit Enabled Botnet Detection in IIoT Environment","authors":"T. Gaber, J. B. Awotunde, Chin-Shiuh Shieh","doi":"10.54216/jisiot.060103","DOIUrl":"https://doi.org/10.54216/jisiot.060103","url":null,"abstract":"Accurate and prompt detection of security attacks in the Industrial Internet of Things (IIoT) is important to reduce security risks. Since a massive number of IoT devices are placed over the globe and the quantity gets increased, an effective security solution is necessary. A botnet is a computer network comprising numerous hosts executing on standalone software. In this view, this article develops a novel Glowworm Swarm Optimization Driven Gated Recurrent Unit Enabled Botnet Detection (GSOGRU-BD) model in IIoT Environment. The presented GSOGRU-BD model intends to effectually identify the presence of botnet attacks in the IIoT environment. To do so, the GSOGRU-BD model initially pre-processed the input data to get rid of missing values. In addition, the GSOGRU-BD model involves the GRU model for the effective recognition and classification of botnets. Besides, the GSO algorithm is used for optimal hyperparameter tuning of the GRU model. Comparative experimental validation of the GSOGRU-BD model is tested using a benchmark dataset and the results reported the better outcomes for the GSOGRU-BD model.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122087599","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":"Site Selection for an Offshore Wind Power Station Under a Fuzzy Environment","authors":"M. Tofigh, N. Hashim.","doi":"10.54216/jisiot.060201","DOIUrl":"https://doi.org/10.54216/jisiot.060201","url":null,"abstract":"The Malaysian government’s support for offshore wind power production has led to an increase in a few proposals. An important factor in the overall efficiency of any offshore wind farm is the site selection process, which is a multi-criteria decision-making (MCDM) task. However, classical MCDM techniques often fail to choose a suitable site because of three main challenges. First, compensation is regarded as a problem in the processing of information. Second, data usage and data leakage are often ignored in the decision-making process. Third, interaction difficulty in fuzzy environments is easily ignored. This study provides a framework for making site selection decisions for offshore wind farms while addressing the constraints. Fuzzy VIKOR is used in the second stage of the AHP process to analyze the site’s results with respect to evaluation criteria for offshore wind farms. A comprehensive index system, which incorporates the veto criteria and evaluation criteria for selecting offshore wind power station sites, is devised. Then, the system is used to transmit imprecise information to decision makers by using a triangular fuzzy set. Likelihood-based valued comparisons indicate that imprecise choice information can be correctly used, and issues of information loss can be logically avoided. A case study of Malaysia is used to demonstrate the validity and practicality of the site selection technique. This research offers a theoretical basis for accurate offshore wind power evaluation in Malaysia.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122712141","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":"Optimized Resource Allocation Algorithm for Crowd-Creation Space Computing Based on Cloud Computing Environment","authors":"Mustafa El, Aaras Y Y.kraidi","doi":"10.54216/jisiot.040101","DOIUrl":"https://doi.org/10.54216/jisiot.040101","url":null,"abstract":"The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"2 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131225648","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":"Intelligent Edge Computing for IoT: Enhancing Security and Privacy","authors":"Lobna Osman, Olutosin Taiwo, A. Elashry, A. Ezugwu","doi":"10.54216/jisiot.080105","DOIUrl":"https://doi.org/10.54216/jisiot.080105","url":null,"abstract":"Edge computing is a distributed computing paradigm that involves processing data at or near the edge of the internet of things (IoT) network, instead of centralized server. This makes the cyber-attacks increasingly sophisticated, and traditional security measures become no longer sufficient to protect against them. Concurrently, privacy concerns arise when sensitive data is involved in Edge computing applications containing confidential information. In this paper, we propose a privacy-preserved federated learning (FL) approach for cyber-attack detection in edge based IoT ecosystem. A novel lightweight convolutional Transformer network (LCT) network is designed to precisely identify cyber-attacks though learning attack patterns from IoT traffics in local edge devices, where model is personalized though fine-tuning. The privacy of model and data is preserved in our system via incorporating differential privacy and secure aggregation during the cooperative training process on edge devices. We evaluate our proposed approach on a real-world dataset of network traffic (NSL-KDD) containing various types of attacks, and the experimental results show that our personalized FL approach outperforms traditional FL in terms of detection accuracy. We also show that our approach is effective in handling non-stationary data and adapting to changes in the network environment.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121619876","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":"Collaborative Segmentation of COVID-19 From non-IID Topographies in the Internet of Medical Things (IoMT)","authors":"A. Sleem, Ibrahim Elhenawy","doi":"10.54216/jisiot.070201","DOIUrl":"https://doi.org/10.54216/jisiot.070201","url":null,"abstract":"The Internet of Medical Things (IoMT) offers numerous advantages in the diagnosis, monitoring, and treatment of a wide variety of illnesses for both patients. COVID-19 has caused a global pandemic and turned out to be the utmost crucial danger threatening the whole world. Thus, scholars’ attention moved toward Deep learning (DL) and IoMT for developing automated systems for COVID-19 diagnosis andor prognosis based on chest computed tomography (CT) scans, and it has shown great success in several tasks, including classification and segmentation. Nevertheless, developing and training a superior DL approach necessitates accumulating a substantial amount of patients’ CT scans together with their labels. This is an expensive and time-consuming task that restricts attaining large enough data from a single siteinstitution, However, owing to the necessity for protecting data privacy, it is difficult to accumulate the data from several sites and store them at a centralized server. Federated learning (FL) alleviates the need for centralized data by spreading the public segmentation model to different institutional models, training the segmentation model at the institution, and followingly calculating the mean of the parameters in the public model. Nevertheless, researchers advocated that private information could be restored using the parameters of the model. This study presents a privacy-protection technique for the challenge of multi-site COVID-19 segmentation. To tackle the challenge, we introduce the FL technique, in which a distributed optimization procedure is developed, and randomization techniques are proposed to change the joint parameters of private institutional segmentation models. Bearing in mind the complete heterogeneity of COVID-19 distributions from diverse institutions, we develop two domain adaptation (DA) techniques in the proposed FL design. We explore several applied characteristics of optimizing the FL approach and analyze the FL approach in comparison with alternate training approaches. Finally, the results validate that it is auspicious to employ multi-site non-shared CT scans to improve the COVID-19 infection segmentation.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117000924","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":"Development of Sustainable assessment Model of solar hydrogen production techniques: An integrated MCDM approach","authors":"M. Ismail, S. Zaki, M. Ibrahim","doi":"10.54216/jisiot.060102","DOIUrl":"https://doi.org/10.54216/jisiot.060102","url":null,"abstract":"Energy has a critical role in human survival and societal progress. Hydrogen is a possible energy carrier for long-term power generation. Known as both an environmental nuisance and an essential hydrogen source, Hydrogen Sulfide (H2S) may be found in large quantities in the waters of the Black Sea. The primary goal of this research is to determine which breakdown processes, such as thermal, thermochemical, electrochemical, plasma, photochemical and thermal suit sustainability requirements better than others. The most acceptable hydrogen generation technique is chosen based on characteristics such as financial viability, environmental viability, effectiveness, simplicity of the process, energy consumption, safety and dependability, application and operational adaptability, and technological maturity. This paper proposes innovative additions to the CoCoSo approach. The COCOSO method is used to compute the weights of criteria and rank the alternatives. This paper proposed 8 criteria and 5 alternatives.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123201983","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":"Intelligent and Secure Detection of Cyber-attacks in Industrial Internet of Things: A Federated Learning Framework","authors":"A. Sleem","doi":"10.54216/jisiot.070105","DOIUrl":"https://doi.org/10.54216/jisiot.070105","url":null,"abstract":"The increasing integration of traditional industrial systems with smart networking and communications technology (such as fifth-generation networks, software-defined networking, and digital twin), has drastically widened the security vulnerabilities of the industrial internet of things (IIoT). Nevertheless, owing to the lack of sufficient instances of high-quality attacks, it has been incredibly difficult to resist the cyberattacks that directed at such a substantial, complicated, and dynamic IIoT. This work introduces an intelligent federated deep learning framework, termed FED-SEC, for automatic and early identification of cyber-attacks against IIoT infrastructure. In particular, a new convolutional recurrent network designed to detect cyberattacks within IIoT data. Then, a secure federated learning scheme presented to promote making use of mobile edge computing to enable the distributed IIoT entities to cooperate together to train a unified model for cyberattack detection in a privacy-preserved manner. More, a safe communication channel constructed via an improved Homomorphic Encryption scheme aiming to keep the model parameters secure against any leakage of inferential attacks, especially throughout the training procedure. Massive experimentations on multiple public datasets of IIoT cyberattacks proved the high-level efficacy of the FED-SEC in discovering different categories of cyber-attacks against IIoT and the superiorities over cutting-edge approaches.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125302697","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":"Intelligent Video Moving Target Detection Based on Multi-Attribute Single Value Medium Neutrosophic Method","authors":"Gopal Chaudhary, Manju Khari, Amena Mahmoud","doi":"10.54216/jisiot.050105","DOIUrl":"https://doi.org/10.54216/jisiot.050105","url":null,"abstract":"Competition in social sports has many benefits for athlete training due to this competition gives researchers a chance to making and developing new methods and ways that support them. The competition in sport growth rapidly these days. During the last several years, there has been a significant increase in the volume of traffic using multimedia. In addition, some of the most recent paradigm shifts suggested, such as IoT, bring about the introduction of new kinds of traffic and applications. Software-defined networks, often known as SDNs, are beneficial to network management since they enhance its capabilities. When used with SDN, artificial intelligence (AI) has the potential to solve network issues using categorization and estimate strategies. So, in this paper discuss and develop a new method for sports video moving target detection. This method is based on multi-criteria decision making (MCDM) because targeting detection has many criteria and sub-criteria. This paper collected five main criteria and twenty sub-criteria impacts in target detection of sports video. We use the Analytical hierarchy Process (AHP) to determine the importance of these criteria and their weights. These criteria were evaluated under a neutrosophic environment. An application is provided to measure the outcome of the proposed method.","PeriodicalId":122556,"journal":{"name":"Journal of Intelligent Systems and Internet of Things","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124404788","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}