bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel hybrid generative adversarial network for synthesizing image from sketch
Authors: Murthy Pavithra Narasimha , Yeliyur Hanumanthaiah Sharath Kumar
Published: September 2023

In the area of sketch-based image retrieval process, there is a potential difference between retrieving the match images from defined dataset and constructing the synthesized image. The former process is quite easier while the latter process requires more faster, accurate, and intellectual decision making by the processor. After reviewing open-end research problems from existing approaches, the proposed scheme introduces a computational framework of hybrid generative adversarial network (GAN) as a solution to address the identified research problem. The model takes the input of query image which is processed by generator module running 3 different deep learning modes of ResNet, MobileNet, and U-Net. The discriminator module processes the input of real images as well as output from generator. With a novel interactive communication between generator and discriminator, the proposed model offers optimal retrieval performance along with an inclusion of optimizer. The study outcome shows significant performance improvement.
An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter
Authors: Malathi Venkatesan , Manikandan Arumugam
Published: September 2023

Underwater images are usually suffering from the issues of quality degradation, such as low contrast due to blurring details, color deviations, non-uniform lighting, and noise. Since last few decades, many researches are undergoing for restoration and enhancement for degraded underwater images. In this paper, we proposed a novel algorithm using modified anisotropic diffusion filter with dynamic color balancing strategy. This proposed algorithm performs based on an employing effective noise reduction as well as edge preserving technique with dynamic color correction to make uniform lighting and minimize the speckle noise. Furthermore, reanalyze the contributions and limitations of existing underwater image restoration and enhancement methods. Finally, in this research provided the detailed objective evaluations and compared with the various underwater scenarios for above said challenges also made subjective studies, which shows that our proposed method will improve the quality of the image and significantly enhanced the image.
An efficient reconfigurable geographic routing congestion control algorithm for wireless sensor networks
Authors: Pandith Mamatha M. , Ramaswamy Nataraj Kanathur , Srikantaswamy Mallikarjunaswamy , Ramaswamy Rekha Kanathur
Published: September 2023

In recent times, huge data is transferred from source to destination through multi path in wireless sensor networks (WSNs). Due to this more congestion occurs in the communication path. Hence, original data will be lost and delay problems arise at receiver end. The above-mentioned drawbacks can be overcome by the proposed efficient reconfigurable geographic routing congestion control (RgRCC) algorithm for wireless sensor networks. the proposed algorithm efficiently finds the node’s congestion status with the help queue length’s threshold level along with its change rate. Apart from this, the proposed algorithm re-routes the communication path to avoid congestion and enhances the strength of scalability of data communication in WSNs. The proposed algorithm frequently updates the distance between the nodes and by-pass routing holes, common for geographical routing. when the nodes are at the edge of the hole, it will create congestion between the nodes in WSNs. Apart from this, more nodes sink due to congestion. it can be reduced with the help of the proposed RgRCC algorithm. As per the simulation analysis, the proposed work indicates improved performance in comparison to conventional algorithm. By effectively identifying the data congestion in WSNs with high scalability rate as compared to conventional methods
Novel evaluation framework for sensing spread spectrum in cognitive radio
Authors: Bellary Kursheed , Budyal Vijayashree Rajendrakumar
Published: September 2023

The cognitive radio network is designed to cater to the optimization demands of restricted spectrum availability. A review of existing literature on spectrum sensing shows that there is still a broader scope for its improvement. Therefore, this paper introduces an efficient computational framework capable of evaluating the effectiveness of the spread spectrum concept in the context of cognitive radio network in a more scalable and granular way. The proposed method introduces a dual hypothesis using a different set of dependable parameters to emphasize the detection of optimal energy for a low signal quality state over the noise. The proposed evaluation framework is benchmarked using a statistical analysis method not present in any existing approaches toward spread spectrum sensing. The simulated outcome of the study exhibits that the proposed system offers a significantly better probability of detection than the current system using a simplified evaluation scheme with multiple test parameters.
Improved autocorrelation method for time synchronization in filtered orthogonal frequency division multiplexing
Authors: Suyoto Suyoto , Subekti Agus , Satyawan Arief Suryadi , Marta Dinata Mochamad Mardi , Mitayani Arumjeni , Adhi Purwoko
Published: September 2023

Time synchronization is essential in multicarrier systems such as filtered orthogonal frequency division multiplexing (F-OFDM) because it determines the whole system’s performance. Differ with OFDM, where subcarrier allocation is not flexible. In F-OFDM, the subcarrier allocation is more flexible, and the whole subcarrier in one symbol can be grouped into several subbands. The use of subcarriers that are limited to only one subband can reduce the performance of time synchronization based on autocorrelation (AC) methods. In this study, we first compare the performance of the AC-based time synchronization algorithms used in F-OFDM when training symbols are limited to one subband. Secondly, we made improvements to the AC-based time synchronization with the averaging technique of its timing metric, thus increasing the accuracy of time estimates in the F-OFDM system. The averaging technique of the timing metric improved the performance of the AC method in cases where the training symbol is limited to one subband, as shown in the simulation results.
Scedasticity descriptor of terrestrial wireless communications channels for multipath clustering datasets
Authors: Blanza Jojo , Trinidad Emmanuel , Materum Lawrence
Published: September 2023

Fifth-generation (5G) wireless systems increased the bandwidth, improved the speed, and shortened the latency of communications systems. Various channel models are developed to study 5G. These channel models reproduce the stochastic properties of multiple-input multiple-output (MIMO) antennas by generating wireless multipath components (MPCs). The MPCs that have similar properties in delay, angles of departure, and angles of arrival form clusters. The multipaths and multipath clusters serve as datasets to understand the properties of 5G. These datasets generated by the Cooperation in Science and Technology 2100 (COST 2100), International Mobile Telecommunications-2020 (IMT-2020), quasi deterministic radio channel generator (QuaDRiGa), and wireless world initiative new radio II (WINNER II) channel models are tested for their homoscedasticity based on Johansen's procedure. Results show that the COST 2100, QuaDRiGa, and WINNER II datasets are heteroscedastic, while the IMT-2020 dataset is homoscedastic.
Towards fostering the role of 5G networks in the field of digital health
Authors: Turab Nidal M. , Al-Nabulsi Jamal Ibrahim , Abu-Alhaija Mwaffaq , Owida Hamza Abu , Alsharaiah Mohammad , Abuthawabeh Ala
Published: September 2023

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future.
Improved Javanese script recognition using custom model of convolution neural network
Authors: Susanto Ajib , Mulyono Ibnu Utomo Wahyu , Sari Christy Atika , Rachmawanto Eko Hari , Setiadi De Rosal Ignatius Moses , Sarker Md Kamruzzaman
Published: September 2023

Handwriting recognition in Javanese script is not widely developed with deep learning (DL). Previous DL and machine learning (ML) research is generally limited to basic characters (Carakan) only. This study proposes a deep learning model using a custom-built convolutional neural network to improve recognition accuracy performance and reduce computational costs. The main features of handwritten objects are textures, edges, lines, and shapes, so convolution layers are not designed in large numbers. This research maximizes optimization of other layers such as pooling, activation function, fully connected layer, optimizer, and parameter settings such as dropout and learning rate. There are eleven main layers used in the proposed custom convolutional neural network (CNN) model, namely four convolution layers+activation function, four pooling layers, two fully connected layers, and a softmax classifier. Based on the test results on the Javanese script handwritten image dataset with 120 classes consisting of 20 basic character classes and 100 compound character classes, the resulting accuracy is 97.29%.
Adopting DevOps practices: an enhanced unified theory of acceptance and use of technology framework
Authors: Salih Ahmad Mahdi , Syed-Mohamad Sharifah Mashita , Keikhosrokiani Pantea , Samsudin Nur Hana
Published: September 2023

DevOps software development approach is widely used in the software engineering discipline. DevOps eliminates the development and operations department barriers. The paper aims to develop a conceptual model for adopting DevOps practices in software development organizations by extending the unified theory of acceptance and use of technology (UTAUT). The research also aims to determine the influencing factors of DevOps practices’ acceptance and adoption in software organizations, determine gaps in the software development literature, and introduce a clear picture of current technology acceptance and adoption research in the software industry. A comprehensive literature review clarifies how users accept and adopt new technologies and what leads to adopting DevOps practices in the software industry as the starting point for developing a conceptual framework for adopting DevOps in software organizations. The literature results have formulated the conceptual framework for adopting DevOps practices. The resulting model is expected to improve understanding of software organizations’ acceptance and adoption of DevOps practices. The research hypotheses must be tested to validate the model. Future work will include surveys and expert interviews for model enhancement and validation. This research fulfills the necessity to study how software organizations accept and adopt DevOps practices by enhancing UTAUT.
Towards a hybrid recommendation approach using a community detection and evaluation algorithm
Authors: Adraoui Meriem , Souabi Sonia , Retbi Asmaâ , Idrissi Mohammed Khalidi , Bennani Samir
Published: September 2023

In social learning platforms, community detection algorithms are used to identify groups of learners with similar interests, behavior, and levels. While, recommendation algorithms personalize the learning experience based on learners' profile information, including interests and past behavior. Combining these algorithms can improve the recommendation quality by identifying learners with similar needs and interests for more accurate and relevant suggestions. Community detection enhances recommendations by identifying groups of learners with similar needs and interests. Leveraging their similarities, recommendation algorithms generate more accurate suggestions. In this article, we propose a novel approach that combines community detection and recommendation algorithms into a single framework to provide learners with personalized recommendations and opportunities for collaborative learning. Our proposed approach consists of three steps: first, applying the maximal clique-based algorithm to detect learning communities with common characteristics and interests; second, evaluating learners within their communities using static and dynamic evaluation; and third, generating personalized recommendations within each detected cluster using a recommendation system based on correlation and co-occurrence. To evaluate the effectiveness of our proposed approach, we conducted experiments on a real-world dataset. Our results show that our approach outperforms existing methods in terms of modularity, precision, and accuracy.
Assessment of the main features of the model of dissemination of information in social networks
Authors: Imanberdi Assel , Lira La , Aitolkyn Kulmuratova , Leila Rzayeva , Abitova Gulnara , Aigerim Bakiyeva , Ainur Orynbayeva , Assem Baimakhanbetova
Published: September 2023

Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks.
Detection of lung pathology using the fractal method
Authors: Abdikerimova Gulzira , Shekerbek Ainur , Tulenbayev Murat , Sultanova Bakhyt , Beglerova Svetlana , Dzhaulybaeva Elvira , Zhumakanova Kamshat , Rysbekkyzy Bakhytgul
Published: September 2023

Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis. 
Performance evaluation of botnet detection using machine learning techniques
Authors: Padhiar Sneha , Patel Ritesh
Published: September 2023

Cybersecurity is seriously threatened by Botnets, which are controlled networks of compromised computers. The evolving techniques used by botnet operators make it difficult for traditional methods of botnet identification to stay up. Machine learning has become increasingly effective in recent years as a means of identifying and reducing these hazards. The CTU-13 dataset, a frequently used dataset in the field of cybersecurity, is used in this study to offer a machine learning-based method for botnet detection. The suggested methodology makes use of the CTU-13, which is made up of actual network traffic data that was recorded in a network environment that had been attacked by a botnet. The dataset is used to train a variety of machine learning algorithms to categorize network traffic as botnet-related/benign, including decision tree, regression model, naïve Bayes, and neural network model. We employ a number of criteria, such as accuracy, precision, and sensitivity, to measure how well each model performs in categorizing both known and unidentified botnet traffic patterns. Results from experiments show how well the machine learning based approach detects botnet with accuracy. It is potential for use in actual world is demonstrated by the suggested system’s high detection rates and low false positive rates.
Hybrid filtering methods for feature selection in high-dimensional cancer data
Authors: Md Noh Siti Sarah , Ibrahim Nurain , Mansor Mahayaudin M. , Yusoff Marina
Published: September 2023

Statisticians in both academia and industry have encountered problems with high-dimensional data. The rapid feature increase has caused the feature count to outstrip the instance count. There are several established methods when selecting features from massive amounts of breast cancer data. Even so, overfitting continues to be a problem. The challenge of choosing important features with minimum loss in a different sample size is another area with room for development. As a result, the feature selection technique is crucial for dealing with high-dimensional data classification issues. This paper proposed a new architecture for high-dimensional breast cancer data using filtering techniques and a logistic regression model. Essential features are filtered out using a combination of hybrid chi–square and hybrid information gain (hybrid IG) with logistic regression as classifier. The results showed that hybrid IG performed the best for high-dimensional breast and prostate cancer data. The top 50 and 22 features outperformed the other configurations, with the highest classification accuracies of 86.96% and 82.61%, respectively, after integrating the hybrid information gain and logistic function (hybrid IG+LR) with a sample size of 75. In the future, multiclass classification of multidimensional medical data to be evaluated using data from a different domain.
A large-scale sentiment analysis using political tweets
Authors: Tun Yin Min , Khaing Myo
Published: September 2023

Twitter has become a key element of political discourse in candidates’ campaigns. The political polarization on Twitter is vital to politicians as it is a popular public medium to analyze and predict public opinion concerning political events. The analysis of the sentiment of political tweet contents mainly depends on the quality of sentiment lexicons. Therefore, it is crucial to create sentiment lexicons of the highest quality. In the proposed system, the domain-specific of the political lexicon is constructed by using the supervised approach to extract extreme political opinions words, and features in tweets. Political multi-class sentiment analysis (PMSA) system on the big data platform is developed to predict the inclination of tweets to infer the results of the elections by conducting the analysis on different political datasets: including the Trump election dataset and the BBC News politics. The comparative analysis is the experimental results which are better political text classification by using the three different models (multinomial naïve Bayes (MNB), decision tree (DT), linear support vector classification (SVC)). In the comparison of three different models, linear SVC has the better performance than the other two techniques. The analytical evaluation results show that the proposed system can be performed with 98% accuracy in linear SVC.
Building a recommendation system based on the job offers extracted from the web and the skills of job seekers
Authors: Mgarbi Hanae , Chkouri Mohamed Yassin , Tahiri Abderrahim
Published: September 2023

Recruitment, or job search, is increasingly used throughout the world by a large population of users through various channels, such as websites, platforms, and professional networks. Given the large volume of information related to job descriptions and user profiles, it is complicated to appropriately match a user's profile with a job description, and vice versa. The job search approach has drawbacks since the job seeker needs to search a job offers in each recruitment platform, manage their accounts, and apply for the relevant job vacancies, which wastes considerable time and effort. The contribution of this research work is the construction of a recommendation system based on the job offers extracted from the web and on the e-portfolios of job seekers. After the extraction of the data, natural language processing is applied to structured data and is ready for filtering and analysis. The proposed system is a content-based system, it measures the degree of correspondence between the attributes of the e-portfolio with those of each job offer of the same list of competence specialties using the Euclidean distance, the result is classified with a decreasing way to display the most relevant to the least relevant job offers.
Approach for Enneagram personality detection for Twitter text: a case study
Authors: Abdelhamid Esraa , Ismail Sally S. , Aref Mostafa
Published: September 2023

Understanding people’s emotions and orientations attracts researchers nowadays. Current personality detection research concentrates on models such as the big five model, the three-factor model. The Enneagram is deeper than these models for providing a comprehensive view. This theory is a unique personality model because it illustrates what drives human behavior. This recognition helps in building smarter recommendation systems and intelligent educational systems. Enneagram personalities are realized through a long questionnaire-based test. People are not concerned about doing a test because it is time-consuming. A proposed case study employs Twitter’s text to detect Enneagram personality because it requires no time or effort. The proposed case study is based on an approach that uses a combination of ontology, lexicon, and statistical technique. This proposed case study uses the biography description text and 40 tweets of a Twitter profile text. The highest probability percentage is peacemaker personality which is 15.58%. This result means that the identified personality is the peacemaker. The outcome is equivalent to the determination of the Enneagram’s specialized people. This result promises more positive outcomes. This is the first automated approach to determine the Enneagram from text.
Accurate metaheuristic deep convolutional structure for a robust human gait recognition
Authors: Yousef Reem Nehad , Khalil Abeer Tawkool , Samra Ahmed Shaaban , Ata Mohamed Maher
Published: September 2023

Gait recognition has become a developing technology in various security, industrial, medical, and military applications. This paper proposed a deep convolutional neural network (CNN) model to authenticate humans via their walking style. The proposed model has been applied to two commonly used standardized datasets, Chinese Academy of Sciences (CASIA) and Osaka University-Institute of Scientific and Industrial Research (OU-ISIR). After the silhouette images have been isolated from the gait image datasets, their features have been extracted using the proposed deep CNN and the traditional ones, including AlexNet, Inception (GoogleNet), VGGNet, ResNet50, and Xception. The best features were selected using genetic, grey wolf optimizer (GWO), particle swarm optimizer (PSO), and chi-square algorithms. Finally, recognize the selected features using the proposed deep neural network (DNN). Several performance evaluation parameters have been estimated to evaluate the model’s quality, including accuracy, specificity, sensitivity, false negative rate (FNR), and training time. Experiments have demonstrated that the suggested framework with a genetic feature selector outperforms previous selectors and recent research, scoring accuracy values of 99.46% and 99.09% for evaluating the CASIA and OU-ISIR datasets, respectively, in low time (19 seconds for training).
Encountering social engineering activities with a novel honeypot mechanism
Authors: Abualhija Mwaffaq , Al-Shaf’i Nid’a , Turab Nidal M. , Hussein Abdelrahman
Published: September 2023

Communication and conducting businesses have eventually transformed to be performed through information and communication technology (ICT). While computer network security challenges have become increasingly significant, the world is facing a new era of crimes that can be conducted easily, quickly, and, on top of all, anonymously. Because system penetration is primarily dependent on human psychology and awareness, 80% of network cyberattacks use some form of social engineering tactics to deceive the target, exposing systems at risk, regardless of the security system's robustness. This study highlights the significance of technological solutions in making users more safe and secure. Throughout this paper, a novel approach to detecting and preventing social engineering attacks will be proposed, combining multiple security systems, and utilizing the concept of Honeypots to provide an automated prevention mechanism employing artificial intelligence (AI). This study aims to merge AI and honeypot with intrusion prevention system (IPS) to detect social engineering attacks, threaten the attacker, and restrict his session to keep users away from these manipulation tactics.
Resource allocation for fog computing based on software-defined networks
Authors: Nejad Sepideh Sheikhi , Khademzadeh Ahmad , Rahmani Amir Masoud , Broumandnia Ali
Published: September 2023

With the emergence of cloud computing as a processing backbone for internet of thing (IoT), fog computing has been proposed as a solution for delay-sensitive applications. According to fog computing, this is done by placing computing servers near IoT. IoT networks are inherently very dynamic, and their topology and resources may be changed drastically in a short period. So, using the traditional networking paradigm to build their communication backbone, may lower network performance and higher network configuration convergence latency. So, it seems to be more beneficial to employ a software-defined network paradigm to implement their communication network. In software-defined networking (SDN), separating the network’s control and data forwarding plane makes it possible to manage the network in a centralized way. Managing a network using a centralized controller can make it more flexible and agile in response to any possible network topology and state changes. This paper presents a software-defined fog platform to host real-time applications in IoT. The effectiveness of the mechanism has been evaluated by conducting a series of simulations. The results of the simulations show that the proposed mechanism is able to find near to optimal solutions in a very lower execution time compared to the brute force method.
Spiking ink drop spread clustering algorithm and its memristor crossbar conceptual hardware design
Authors: Paeen Afrakoti Iman Esmaili , Nazerian Vahdat , Sutikno Tole
Published: September 2023

In this study, a novel neuro-fuzzy clustering algorithm is proposed based on spiking neural network and ink drop spread (IDS) concepts. The proposed structure is a one-layer artificial neural network with leaky integrate and fire (LIF) neurons. The structure implements the IDS algorithm as a fuzzy concept. Each training data will result in firing the corresponding input neuron and its neighboring neurons. A synchronous time coding algorithm is used to manage input and output neurons firing time. For an input data, one or several output neurons of the network will fire; confidence degree of the network to outputs is defined as the relative delay of the firing times with respect to the synchronous pulse. A memristor crossbar-based hardware is utilized for hardware implementation of the proposed algorithm. The simulation result corroborates that the proposed algorithm can be used as a neuro-fuzzy clustering and vector quantization algorithm.
Predicting air quality in smart city using novel transfer learning based framework
Authors: Sonawani Shilpa , Patil Kailas
Published: September 2023

Air quality is a matter of concern these days due to its adverse effect on human health. Multiple new air pollution monitoring and prediction stations are being developed in smart cities to tackle the issue. Recent advanced deep learning techniques show excellent performance for air quality predictions but need sufficient training data for model performance. The data insufficiency issue at a new station can be resolved using the proposed novel transfer learning-based framework to predict pollution concentration at the new station. The prediction ability at a new station can be significantly enhanced by this effective technology. The performance of the model is assessed on various stations in Delhi, India.
Real-time soil monitoring and irrigation system for taro yam cultivation
Authors: Rahim Azril Abdul , Mohamad Roslina , Shuhaimi Nurain Izzati , Buclatin Willie C.
Published: September 2023

The yam cultivation industry faces hurdles in satisfying the world’s rising demand. Meanwhile, agricultural activities encounter issues such as monitoring systems for soil composition that are not directly integrated with the irrigation system. Hence, this research aims to develop an irrigation and soil monitoring system using NodeMcu ESP8266 for taro yam cultivation.
 A soil composition and irrigation device were developed using NodeMcu ESP8266 to control the irrigation system autonomously. Then, the real-time monitoring systems using Blynk 2.0 was designed. The final method is used to test the irrigation and soil monitoring system in a taro yam growing area. The results show that the irrigation and soil monitoring system reduced water usage by about 32.5%, as the 3.6 liters of water used daily before applying the irrigation and soil monitoring device was reduced to 2.43 liters per day. In conclusion, the developed device can conveniently display the ambient air’s humidity, temperature, and soil moisture conditions on smartphones and desktops. In addition, this system can maintain soil moisture according to a set value, which can prevent excessive water use for taro yam cultivation.
Internet of things-blockchain integration: a robust data security approach for end-to-end communication
Authors: Parmar Martin , Shah Parth
Published: September 2023

The integration of internet of things (IoT) and blockchain technology has been proposed as a promising solution to address the challenges related to security, scalability, and privacy. This integration enables the creation of secure and decentralized systems that allow devices to interact with each other and exchange data in a transparent and trustworthy manner. The significant research work has been carried out by researcher on IoT and blockchain integration to utilize their combine potential in smart applications. However, due to limitation and scope of emerging technologies, it is essential to develop robust security frameworks that address the challenges and ensure the secure data communication from IoT node to blockchain network. IoT devices come up with constrained nature and limited security mechanisms along with weak access control policy. Our work includes heterogeneous IoT and blockchain ecosystem that senses and store data intelligently after proper validation with verification into decentralized distributed blockchain network. Our proposed work includes a robust approach to convey potential data security issue to protect end to end information with suitable authorization, access control policy, device to device data protection and data integrity specially when data moving from IoT devices to blockchain network.
A multilayer model to enhance data security in cloud computing
Authors: Awadh Wed Akeel , Alasady Ali Salah , Hashim Mohammed S.
Published: September 2023

Cloud computing has introduced substantial advancements to the field of information technology, offering businesses enhanced features, flexibility, reliability, scalability, and a wide range of services. However, it also presents security challenges like data theft and manipulation. To minimize the risks associated with these threats, a model that depends on encryption and steganography is proposed with the aim of applying a security model in a cloud environment. The proposed cloud security model utilizes a multi-layer model that includes a Rivest-Shamir-Adleman (RSA), advanced encryption standard (AES), identity-based encryption (IBE) methods, least significant bit (LSB) method, and the Brotli method. This comprehensive approach effectively safeguards data integrity, confidentiality, and privacy against potential intruders. In addition, it enhances the flexibility and efficiency of the cloud by enabling the secure storage and transmission of large amounts of data.
Biometric face authentication system for secure smart office environments
Authors: Siswanto Apri , Efendi Akmar , Kadir Evizal Abdul
Published: September 2023

The use of the smart office concept in security has increased significantly recently. One of the areas of concern is the use of facial finger biometric technology for authentication systems, for example, authentication to enter a particular room in an office. This paper aims to describe a new prototype for office door automation and security that combines facial biometric technology and NodeMCU. Hopefully, this system will help increase the safety and comfort of office employees with easy installation and low cost. This system automatically controls (opens or closes) the door based on the biometrics of the user’s face registered in the database on the NodeMCU microcontroller. The main system comprises a NodeMCU microcontroller, face sensors, and a door lock system.
Rainfall analysis and prediction using ensemble learning for Karnataka State
Authors: Meti Govardhana , Krishnegowda Ravi Kumar Guralamata , Swamy Gudihalli Savitha
Published: September 2023

Accurate prediction of rainfall can save farmers from crop damage as erratic rains in India have caused agricultural losses. Due to continuous climate change, rainfall and weather prediction has become essential. Rain causes damage to people and property. So, rainfall forecasting is crucial for crop as our country’s economy is still heavily dependent on agriculture. Using machine learning (ML) and deep learning (DL) models, we can train these models for the rainfall dataset and make predictions. In this paper, we estimate rainfall over the Karnataka region using a stacking ensemble model on a rainfall dataset collected between 1901 and 2015.
Feasibility study of rooftop solar photovoltaic system for 33/11 kV primary substation at Al Suwairah
Authors: Saleem Shaik Abdul , Suraya Syed , Irshad Shaik Mohammad
Published: September 2023

This article presents an overview of the technical and financial feasibility analysis of integrating a photovoltaic (PV) source with the conventional power system to supply the auxiliary load at the Al Suwairah 33/11 kV primary substation (PSS) in Suhar, Sultanate of Oman. This study estimates the required electrical energy for the primary substation's auxiliary load. A method is implemented to utilize the available area in the primary substation for determining the solar photovoltaic system's capacity. PVSyst software is employed to determine the ideal system capacity. The proposed solar PV system's payback period and overall annual savings have been evaluated using a series of simulated analyses. The proposed PV can inject 172.5 MWh/year with an average performance ratio of 0.826. The economic evaluation by the photovoltaic system (PVSyst) considering the present auxiliary load at the Al Suwarah PSS has suggested a total payback period of 8.9 years which is quite feasible. The cost of power generated by the proposed system is 49.2% less than the cost of power supplied from the utility service provider.
Optimal control strategy of photovoltaic system based autonomous micro grid for power quality improvement
Authors: Mohammad Karimulla Syed , Kumar Chekka Ravi
Published: September 2023

Due to the increasing number of distributed generation units being used in remote regions, the need for reliable and flexible operation of these systems has led to the development of microgrid technology. When a microgrid operates in an autonomous mode, it can experience unpredictable loads and other factors that can affect its operation. An optimal control mechanism is required to maintain the system's stability. Hence, in this paper optimal control strategy of autonomous microgrid for power quality improvement is presented. A test case of single phase 3.5 kW photovoltaic (PV) system based autonomous micro grid is considered. Particle swarm optimization (PSO) and Harris hawks optimization (HHO) optimal control strategies are implemented under standard test case and variable test cases. In all the cases PV mean voltage-Vmpv (V), PV mean current-Impv (A), RMS voltage-Vrms (V), RMS current-Irms (A), mean PV power-Ppv (W), autonomous grid power-Pg (W), efficiency (%), total harmonic distortion (THD) (%), inverter losses (%) are evaluated. In all the cases HHO optimal control strategy for autonomous microgrid exhibits the best performance in comparison with PSO optimal control strategy. The inverter efficiency is improved, inverter losses are reduced and the THD is improved.
Optimization of seasonal tilt adjustment photovoltaic system in Karbala, Iraq, by using the albedo benefit
Authors: Duhis Ahmed Hussein , Mosheer Ahmed Daud , Abed Ansam M.
Published: September 2023

This study aims to identify a cost-effective method for enhancing incident irradiation on the collector plane. Two different orientations for photovoltaic (PV) systems have been considered. The PV system is installed with albedo (0.25) on a concrete surface. The system is an off-grid with 690 Wp power, used for office applications with a yearly energy (1068) Khp/yr, located at Karbala Governorate /Iraq. The first orientation has an optimal seasonal tilt angle of 13o and 53o for summer and winter, respectively. In contrast, the second orientation is one-axis tracking, horizontal E-W axis, which produces more output power. To avoid the cost of using a tracking system and improve the amount of the output power of the first system, the old concrete surface of this system was treated with white Portland cement with a higher albedo value of 0.87 and then compared to the parameters (incident irradiation in the collector plane, transposition factor and the product available energy) with a second system. It was using Perez transposition model in PV syst software in this study. The results showed a higher value of comparing parameters when using inexpensive materials like white Portland cement, which improved the output PV system power than the tracking system.
Application of optimal control for wind integrated power system
Authors: Chetty Nelson Dhanpal , Sharma Gulshan , Kumawat Manoj , Bokoro Pitshou N.
Published: September 2023

This paper presents the modeling and application of an optimal controller for frequency and tie-line power stability for a two-area interconnected hydro-thermal power plant having tandem compound non-reheat turbines integrated with wind power generation having doubly fed induction generator (DFIG) wind turbines in each area. The power system is interconnected using AC tie-lines. The designed optimal controller was implemented and the system dynamic responses for three power system model states were obtained considering a 1% load fluctuation in one of the areas. The optimal control strategy presented in this paper depends on formulating an error value and finding the feedback gains corresponding to each state of the system, which are easily attainable as outputs. The analysis was undertaken and verified by calculating the performance index value, the closed ring real and imaginary values, finding the feedback gains, and through graphical simulations of the three system models under investigation. The output of the optimal controller was enhanced when the DFIG-based wind turbines were installed in each area combined with a superconducting magnetic energy storage (SMES) unit and a thyristor control phase shifter (TCPS).
Analysis of fuzzy and neural controllers in direct torque controlled synchronous motors
Authors: Ambarapu Sudhakar , Daniel Ravuri , Puli Sreekanth , Mummana Satyanarayana , Kolukula Nitalaksheswara Rao , Rajanna Bodapati Venkata
Published: September 2023

In this study, multiple intelligent control systems for direct torque-controlled Synchronous motors are implemented and compared. Using a lookup table to pick a vector through the inverter voltage space, the direct torque control (DTC) system can be obtained. To replicate the state selector in relation to the look-up table, intelligent controllers are deployed. Intelligent logic controllers like fuzzy and neural are used to regulate the performance of permanent magnet synchronous motors (PMSM). In steady-state applications, neural and fuzzy controllers reduce the torque ripple and stator current harmonic distortion. These outcomes are compared with those obtained when the synchronous motor was put under the basic direct torque control method using a proportional integral (PI) controller. The accuracy and effectiveness of the suggested control topologies have been verified using computer simulation software like MATLAB/Simulink.
Microcontroller-based air quality monitoring design using mamdani fuzzy method
Authors: Sujono Hari Agus , Sulistyowati Riny , Satriya Wahyu Agung
Published: September 2023

Hazardous gases such as carbon monoxide (CO) and sulfur dioxide (SO2) not only emerge in the outdoor environment but also inside the house or factory which will endanger the occupants or workers. The detection devices are mainly found on public roads since detected gas mostly consists of CO and SO2. The design of air quality monitoring devices is proposed with applied sensors such as TGS2201 and MQ136. The device, when activated, will detect the gas components in the room. The results of the detection will be displayed in the value of gas concentration and quality. If the concentration exceeds the specified threshold, the fan will act to neutralize the contained air. Fuzzy logic is applied with the membership function using the Sugeno model. The test is applied in the box with maximum air pollution of 80 ppm according to the motor vehicle gas, and out of the box at a public road with maximum air pollution of 51 ppm. Each method has the differences of 29 ppm.
Backstepping nonlinear control of a five-phase PMSG aerogenerator linked to a Vienna rectifier
Authors: Mansouri Adil , El Magri Abdelmounime , El Myasse Ilyass , Lajouad Rachid , Elaadouli Nabil
Published: September 2023

The objective of this study is to create a nonlinear control system designed for a Vienna converter-connected five-phase aerogenerator. The control strategy will be based on the space vector pulse width modulation (SVPWM) technique, which is more suitable for controlling rectifier switches. Due to the higher complexity of the five-phase permanent magnet synchronous generator (PMSG) machine than the conventional three-phase generator, it poses a difficult control problem requiring an advanced mathematical model. Integrating an aerogenera tor with a Vienna converter enhances efficiency and power quality. The primary purpose of this study is to develop a nonlinear controller utilizing the retrogres sion control strategy alongside the SVPWM approach. Furthermore, a speed optimizer based on an artificial neural network is implemented to improve the system’s overall efficiency and meet multiple objectives. The proposed control system improves the overall operation and efficiency of Vienna’s integrated wind turbine and rectifier system
Digital twins technology in the educational process of the aviation equipment repair
Authors: Kairat Koshekov , Ildar Pirmanov , Karygash Alibekkyzy , Saule Belginova , Indira Karymsakova , Markhaba Karmenova , Aizhan Baidildina
Published: September 2023

The article considers the digital transformation of the aviation industry based on twins of maintenance and repair. As a result of digitization, a set of documents and application software are formed in a virtual reality environment. The concept of a digital twin is presented in the context of a model-based system design of the helicopter maintenance process according to the technical regulations. Based on statistical modeling technologies, a model for assembling aircraft units has been developed, in which time characteristics are qualitative estimates of learning processes and the effectiveness of digital twins. The results of experimental studies based on the method of analysis of students' certification using digital twins in the assembly of aircraft units are presented. It has been established that at the production site of an aircraft repair enterprise it is effective to apply training at the first stage using digital twins, and at the second stage using real objects. Based on analytical and experimental studies, regression models are proposed for the relationship between the optimal number of trainings at the second stage and the relative coefficient of training time for successful training and certification of mechanics and electronics engineers.
Computational analysis of hybrid grey wolf and particle swarm optimization for water level control in coupled tank
Authors: Shaikh Hafiz Minhajuddin , Kulkarni Neelima R. , Bakshi Mayuresh V.
Published: September 2023

Water level control at a precise set point is a major concern in process control systems such as bulk drug production industries. Loss in production at the initial stage is observed until the water level reaches desired level. Pharmaceutical industries can however be benefited if they could maintain the precise water level control at the initial stage of production with the help of the performance optimization using various techniques. To achieve optimized value of the selected performance index, a hybrid of particle swarm optimization (PSO) and grey wolf optimization (GWO) is found to provide the desired performance in the best possible manner. The mathematical model of the coupled tanks is first developed using the state space analysis which can then be converted to transfer function form so as to accomplish the simulation. In this paper, integral square error (ISE) is selected as the performance index for the minimization to improve overall performance of the system. Observations from the hybrid GWO-PSO optimization articulate that overshoot and settling time can be improved as per the industry needs. Performance evaluation of different optimization techniques is discussed in this paper.