Publisher: Agora University press   (Total: 2 journals)   [Sort by number of followers]

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Agora Intl. J. of Juridical Sciences     Open Access   (Followers: 2)
Intl. J. of Computers Communications & Control     Open Access   (Followers: 6, SJR: 0.326, CiteScore: 1)
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International Journal of Computers Communications & Control
Journal Prestige (SJR): 0.326
Citation Impact (citeScore): 1
Number of Followers: 6  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1841-9836 - ISSN (Online) 1841-9844
Published by Agora University press Homepage  [2 journals]
  • A Personalized mHealth Monitoring System for Children and Adolescents with
           T1 Diabetes by Utilizing IoT Sensors and Assessing Physical Activities

    • Authors: Nurassyl Zholdas, Madina Mansurova, Octavian Postolache, Maksat Kalimoldayev, Talshyn Sarsembayeva
      Abstract: The problem of diabetes mellitus is becoming alarming due to the increase in morbidity among children. Patients are undergoing vital insulin replacement therapy, the dose depends on the level of glucose in the blood. The glucose level prediction program, taking into account the impact of physical activity on the body, the use of mobile health capabilities will allow us to develop personalized tactics for a child patient and minimize the risks of a critical health condition. The target group of this study are children and adolescents with type 1 diabetes. This study provides an IoT based mHealth monitoring system, including sensors, medical bracelets, mobile devices with applications. The mobile healthcare application for personalized monitoring can implement the functions of more effectively targeting young users to support their own health and improve the quality of life. In addition to monitoring blood glucose levels, the effect of physical activity on the condition of patients is also taken into account. The use of the proposed method for calculating the probable change in the patient’s blood glucose level after the end of physical activity will allow the doctor to make individual recommendations for the diet before the start of physical activity and its intensity.
      PubDate: 2022-04-15
      DOI: 10.15837/ijccc.2022.3.4558
      Issue No: Vol. 17, No. 3 (2022)
       
  • Analysis of a Public and Private Networks for Nutrient Measurement System
           using LoRawan Network

    • Authors: Doan Perdana, Cahya Ariateja, Ibnu Alinursafa, Ongko Cahyono
      Abstract: Lorawan network is ideal for IoT devices that continuously monitor a device and provide information to the gateway if the monitored data is outside the permitted threshold. These devices only require a small bandwidth and are therefore capable of operating on batteries for a long period of time. This study evaluates the design of a tool to measure soil nutrients with parameters of Nitrogen (N), Phosphorus (P), Potassium (K) using NPK sensors and IoT-based systems. The microcontroller used is ESP 32 which is connected to two types of networks. And will be integrated by Antares and the Android app. The purpose of making two types of networks in order to obtain data for analysis or development of the next tool. The result of designing this system is to create a device that can help farmers or the community in the process of measuring nitrogen, phosphorus, and potassium levels directly through the Android application so that soil control and fertilization can be more effective moreover yields can be maximized.
      PubDate: 2022-04-10
      DOI: 10.15837/ijccc.2022.3.4619
      Issue No: Vol. 17, No. 3 (2022)
       
  • Fault Detection in Three-phase Induction Motor based on Data Acquisition
           and ANN based Data Processing

    • Authors: Ovidiu Gheorghe Moldovan, Remus Vladimir Ghincu, Alin Octavian Moldovan, Dan Noje, Radu Catalin Tarca
      Abstract: The main objective of this paper is to investigate how a failure in the functioning of a normal electrical system represented by a three-phase asynchronous motor will modify the voltages and currents present in the system and if it is possible to design a system that is able to automatically detect the fault, based on the use of modern data acquisition system and powerful computer processing capabilities. The detection of faulty signals is realised using Feedforward Artificial Neural Networks.
      PubDate: 2022-04-08
      DOI: 10.15837/ijccc.2022.3.4788
      Issue No: Vol. 17, No. 3 (2022)
       
  • Substantial Phase Exploration for Intuiting Covid using form Expedient
           with Variance Sensor

    • Authors: Radha Raman Chandan, Pravin R. Kshirsagar, Hariprasath Manoharan, Khalid Mohamed El-Hady, Saiful Islam, Mohammad Shahiq Khan, Abhay Chaturvedi
      Abstract: This article focuses on implementing wireless sensors for monitoring exact distance between two individuals and to check whether everybody have sanitized their hands for stopping the spread of Corona Virus Disease (COVID). The idea behind this method is executed by implementing an objective function which focuses on maximizing distance, energy of nodes and minimizing the cost of implementation. Also, the proposed model is integrated with a variance detector which is denoted as Controlled Incongruity Algorithm (CIA). This variance detector is will sense the value and it will report to an online monitoring system named Things speak and for visualizing the sensed values it will be simulated using MATLAB. Even loss which is produced by sensors is found to be low when CIA is implemented. To validate the efficiency of proposed method it has been compared with prevailing methods and results prove that the better performance is obtained and the proposed method is improved by 76.8% than other outcomes observed from existing literatures.
      PubDate: 2022-03-31
      DOI: 10.15837/ijccc.2022.3.4539
      Issue No: Vol. 17, No. 3 (2022)
       
  • Integration of Fuzzy with Incremental Import Vector Machine for Intrusion
           Detection

    • Authors: Arun Kumar Ramamoorthy, K. Karuppasamy
      Abstract: IDM design and implementation remain a difficult undertaking and an unsolved research topic. Multi-dimensional irrelevant characteristics and duplicate information are included in the network dataset. To boost the effectiveness of IDM, a novel hybrid model is developed that combines Fuzzy Genetic Algorithms with Increment Import Vector Machines (FGA-I2VM), which works with huge amounts of both normal and aberrant network data with high detecting accuracy and low false alarm rates. The algorithms chosen for IDM in this stage are machine learning algorithms, which learn, find, and adapt patterns to changing situations over time. Pre-processing is the most essential stage in any IDM, and feature selection is utilized for pre-processing, which is the act of picking a collection or subset of relevant features for the purpose of creating a solution model. Information Gain (IG) is utilized in this FGA-I2VM model to pick features from the dataset for I2VM classification. To train the I2VM classifier, FGA uses three sets of operations to produce a new set of inhabitants with distinct patterns: cross over operation, selection, and finally mutation. The new population is then put into the Import Vector Machine, a strong classifier that has been used to solve a wide range of pattern recognition issues. FGA are quick, especially considering their capacity to discover global optima. Another advantage of FGA is their naturally parallel nature of assessing the individuals within a population. As a classifier, I2VM has self-tuning properties that allow patterns to attain global optimums. The FGA-efficacy I2VM model’s is complemented by information gain, which improves speed and detection accuracy while having a low computing cost
      PubDate: 2022-03-21
      DOI: 10.15837/ijccc.2022.3.4481
      Issue No: Vol. 17, No. 3 (2022)
       
  • Optimization of Three-dimensional Face Recognition Algorithms in Financial
           Identity Authentication

    • Authors: Cong Luo, Xiangbo Fan, Ying Yan, Han Jin, Xuan Wang
      Abstract: Identity authentication is one of the most basic components in the computer network world. It is the key technology of information security. It plays an important role in the protection of system and data security. Biometric recognition technology provides a reliable and convenient way for identity authentication. Compared with other biometric recognition technologies, face recognition has become a hot research topic because of its convenience, friendliness and easy acceptance. With the maturity and progress of face recognition technology, its commercial application has become more and more widespread. Internet finance, e-commerce and other asset-related areas have begun to try to use face recognition technology as a means of authentication, so people’s security needs for face recognition systems are also increasing. However, as a biometric recognition system, face recognition system still has inherent security vulnerabilities and faces security threats such as template attack and counterfeit attack. In view of this, this paper studies the application of threedimensional face recognition algorithm in the field of financial identity authentication. On the basis of feature extraction of face information using neural network algorithm, K-L transform is applied to image high-dimensional vector mapping to make face recognition clearer. Thus, the image loss can be reduced.
      PubDate: 2022-03-21
      DOI: 10.15837/ijccc.2022.3.3744
      Issue No: Vol. 17, No. 3 (2022)
       
  • Dynamic Traffic Light System to Reduce The Waiting Time of Emergency
           Vehicles at Intersections within IoT Environment

    • Authors: Yahya Tashtoush, Mohammed Al-refai, Ghaith Al-refai, Dirar Abdul-Kareem Darweesh, Noor Zaghal, Omar Darwish
      Abstract: Traditional traffic light system, which works based on fixed cycle can be a main reason for traffic jam, due to lack of adaptation to road conditions. Traffic jam has a bad impact on drivers and road users due to the time delay it causes for road users to reach their destinations. This delay can cause a life threat in case of emergency vehicles, such as ambulance vehicles and police cars. One key solution to solve traffic jam on intersections is the dynamic traffic lights, where traffic light operation adapts based on the intersection traffic conditions. Since few of researches projects in the literature interested in solving traffic jam problem for emergency vehicles, the contribution of this paper is to introduces a novel approach to operate traffic light system. The new approach consists of two algorithms which are pure operation mode and hybrid operation mode. These operation modes aim to reduce the waiting time of emergency vehicles on traffic intersections. They assume that there is a smart infrastructure system uses Internet of Things (IoT) that can detect emergency vehicles arrival to an intersection. The smart infrastructure system switches traffic light operation from fixed cycle mode to dynamic mode. The dynamic mode manages traffic lights at intersections to reduce the waiting time of emergency vehicles. The paper presents a simulation of the proposed algorithms, highlights their advantages. In order to evaluate the efficiency of the new technique, we compared our approach with Wen algorithm in the literature and the Traditional traffic light system. Our evaluation study indicated that the proposed algorithms outperformed Wen technique and the Traditional system under different traffic scenarios
      PubDate: 2022-03-21
      DOI: 10.15837/ijccc.2022.3.4482
      Issue No: Vol. 17, No. 3 (2022)
       
  • IoT-inspired Framework for Real-time Prediction of Forest Fire

    • Authors: Abdullah Aljumah
      Abstract: Wildfires are one of the most devastating catastrophes and can inflict tremendous losses to life and nature. Moreover, the loss of civilization is incomprehensible, potentially extending suddenly over vast land sectors. Global warming has contributed to increased forest fires, but it needs immediate attention from the organizations involved. This analysis aims to forecast forest fires to reduce losses and take decisive measures in the direction of protection. Specifically, this study suggests an energy-efficient IoT architecture for the early detection of wildfires backed by fog-cloud computing technologies. To evaluate the repeatable information obtained from IoT sensors in a time-sensitive manner, Jaccard similarity analysis is used. This data is assessed in the fog processing layer and reduces the single value of multidimensional data called the Forest Fire Index. Finally, based on Wildfire Triggering Criteria, the Artificial Neural Network (ANN) is used to simulate the susceptibility of the forest area. ANN are intelligent techniques for inferring future outputs as these can be made hybrid with fuzzy methods for decision-modeling. For productive visualization of the geographical location of wildfire vulnerability, the Self-Organized Mapping Technique is used. Simulation of the implementation is done over multiple datasets. For total efficiency assessment, outcomes are contrasted in comparison to other techniques.
      PubDate: 2022-03-14
      DOI: 10.15837/ijccc.2022.3.4371
      Issue No: Vol. 17, No. 3 (2022)
       
  • Covid-19 Patients’ Hospital Occupancy Prediction During the Recent
           Omicron Wave via some Recurrent Deep Learning Architectures

    • Authors: Heni Bouhamed, Monia Hamdi, Rahma Gargouri
      Abstract: This paper described a suggested model to predict bed occupancy for Covid-19 patients by country during the rapid spread of the Omicron variant. This model can be used to make decisions on the introduction or alleviation of restrictive measures and on the prediction of oxygen and health human resource requirements. To predict Covid-19 hospital occupancy, we tested some recurrent deep learning architectures. To train the model, we referred to Covid-19 hospital occupancy data from 15 countries whose curves started their regressions during January 2022. The studied period covers the month of December 2021 and the beginning of January 2022, which represents the period of strong contagion of the omicron variant around the world. The evolution sequences of hospital occupancy, vaccination percentages and median ages of populations were used to train our model. The results are very promising which could help to better manage the current pandemic peak.
      PubDate: 2022-03-14
      DOI: 10.15837/ijccc.2022.3.4697
      Issue No: Vol. 17, No. 3 (2022)
       
 
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