Proceedings on Engineering Sciences

6

Authors: Olesya P. Kazachenok

The article is devoted to the study of the prospects for increasing the financing of technologies and innovations through the development of remote digital investment transactions in Russia. Based on the best international experience of countries from the IMD Digital Competitiveness Ranking for 2022, using the method of regression analysis, econometric modelling of the significance of the risk of loss of an investor and the legal protection of remote digital investment transactions for financing technology and innovation was carried out. As a result, it was proved that the risk of investor losses and the legal protection of remote digital investment transactions determine the amount of technology and innovation financing. The development of remote-digital investment transactions in Russia opens up broad prospects for increasing funding for technology and innovation. For this purpose, author's recommendations are proposed. The originality of the article is that it revealed a new look at the financing of technologies and innovations - from the standpoint of remote digital investment transactions.

6

Authors: K. R. Priya Dharshini, R. Kalaivani

Wireless communication must be designed with the inbuilt characteristics inclusive of high data rates, transmission efficiency, high quality of data transmission and multimedia transmission in communication framework. In order to improve the spectral efficiency, higher bandwidth, demand in performance and efficiency MIMO system is used. MIMO system with multiple array of antennas utilize maximum spectrum and increase the spectral bandwidth, improve performance, efficiency and bandwidth. This paper aims to design the channel multiplexing based on MIMO PSK modulation and demodulation scheme. MATLAB tool is used to obtain design specifications. Xilinx FPGA is used to implement obtained design specifications using RTL coding.

8

Authors: Faisal Alharthi

The economic development of countries depends upon their technological competitiveness. Computer technology is a major component of technology. More the people are competent in computer technology, the greater the chance of economic development. Saudi Arabia is no exception to this common view. Although computer education was started in Saudi Arabia in 1984, the progress had been slow for a long time. However, now, with the implementation of Vision 2030, there is a high focus on elevating the skills of the Saudi workforce to the global levels. Computer technology has a big role in this regard. The full implementation and achievement of developing computer skills among the Saudi workforce are affected by many issues and challenges. This paper reviews these challenges and issues and the possible solutions offered for some of them, using published literature. A systematic review methodology was utilised for this research. A total of 29 papers were shortlisted and reviewed. Challenges were noted both in learning and in teaching sides. Despite cultural segregation of gender, women are increasingly enrolling in computer programming courses due to government support, family influence, and a stable workplace. Needs and demands on computer skills are rapidly changing with time. This necessitates frequent updating of computer curricula. Many problems are faced by freshers and their seniors. Lack of knowledge in problem-solving affects their ability to construct algorithms and codes and variables for their programming. This is aggravated by a lack of feedback on errors. Other challenges include inadequate practice, inadequacies of time allocated for labs and tutorials, poor quantity and quality of homework, poor selection of instructors, discouragement by universities on computer use, lack of competency of instructors to motivate students, and absence of quality assurance and monitoring. Solutions have been suggested for some of these like 3D animation software like ALICE and automatic assessment. Teaching challenges include workloads due to large classes affecting individual attention to weak students and the need to address students’ problems listed above. Common problems both students and teachers need to be solved through further research. Specific computer programming needs of healthcare professionals, hard of hearing students are to be addressed by implementing specific solutions. Some research in the future will also be helpful. The above challenges can be addressed by adapting solutions from research to the Saudi Arabia context.

8

Authors: Imaduddin, Komal Prasad Kanojia, Bharti Chourasia

he research paper proposes a novel approach for signal detection in cognitive radio networks, aiming to improve spectrum utilization and overall performance. The approach combines matched filter-based detection and the Salp Swarm Algorithm (SSA)-optimized energy detection.Matched filtering is a technique used to detect the presence of a known signal. It correlates the received signal with a reference waveform to determine if the signal is present. In the proposed approach, matched filtering is utilized to detect known signals in the cognitive radio network.On the other hand, energy detection is employed to identify unknown signals. Energy detection measures the energy level of the received signal and compares it to a predetermined threshold. If the energy exceeds the threshold, it is considered as a signal. In this approach, energy detection is optimized using the Salp Swarm Algorithm (SSA). SSA is a metaheuristic algorithm inspired by the behavior of salps in nature, and it is used to find an optimal energy threshold for energy detection in order to improve detection accuracy. The proposed approach is evaluated through simulations, and the results demonstrate its superiority over existing methods in terms of probability of detection, probability of false alarm, and receiver operating characteristics. This indicates that the proposed hybrid approach offers better performance in detecting both known and unknown signals, leading to more efficient spectrum utilization.

8

Authors: Naser Abed Hasen, Jawdat K. Abbas

This research studies geogrid reinforcement of sandy soils under the influence of the three earthquakes (Halabjah, Bolunun, and Ali Al-Gharbi) due to the recent increase in seismic activity and the need to reduce damage to foundations due to settlement. Sand samples prepared with a relative density of 70%, with and without geogrid reinforcement, were tested on a shaking (vibrating table) table. Five geogrid layers were used, respectively. Settlement occurs due to dynamic force, and the plastic foundation was used with dimensions of 100 * 100 * 30 mm. Withstands a static load of 5.294 kN/m2. Increasing the acceleration of the earthquake leads to an increase in the settlement of the foundation in the sandy soil. The greater the number of geogrids reinforcing layers, the leads to a decrease in the base settlement, where the rate of settlement decrease is greater, the lower the earthquake acceleration and the greater the number of geogrid reinforcement layers. The time of onset of settlement and the general pattern of settlement are similar in all cases studied. The rate of settlement is approximately twice as high when the soil is saturated than when the soil is dry, and the effect of geogrids is less for the soil when it is saturated than when it is dry.

8

Authors: Pankaj Kumar, Rajinder Singh Sodhi

This literature review explores of Analytical Hierarchical Process (AHP) application in conjunction with Intuitionistic Fuzzy Sets (IFS) as a powerful decision-making framework within the context of the food manufacturing industry. The review synthesizes a wide array of research contributions and findings to provide insights into the dynamic and uncertain nature of this industry. The study reveals that the AHP-IFS methodology offers a structured approach to address the complexities inherent in the food manufacturing sector, which undergoes distinct phases from inception to maturity, and potentially, decline or renewal. Decision criteria and factors, including market dynamics, technological advancements, regulatory compliance, sustainability concerns, and financial considerations, are analyzed and prioritized using AHP-IFS, considering their interdependencies and uncertainties. Strategic planning, resource allocation, and competitive analysis within the industry have been significantly enhanced through the application of this methodology. Moreover, as sustainability and environmental issues become increasingly important, AHP-IFS is also employed to evaluate the environmental impact of business decisions, aligning with global efforts to promote sustainability. Challenges associated with data availability and computational complexities have been acknowledged, but the potential for refinement and expansion of the methodology, including the incorporation of machine learning techniques, offers promising avenues for future research. Ultimately, AHP-IFS stands as a valuable tool to guide informed decision-making processes throughout the ever-evolving business life cycle of the food manufacturing industry, ensuring adaptability and competitiveness in a complex and uncertain landscape.

10

Authors: Amal Alowaimir

This paper aimed at a systematic review of the role of FinTech in enhancing the digital transformation of Saudi Arabian banks. Google Scholar was searched with related search terms to finally select 28 papers using the PRISMA process of screening and selection. There were very few papers directly dealing with the use of FinTech as a tool for digital transformation in banks, not specifically in traditional banks. There were a few papers on digital tools used in FinTech. Overall, the digital transformation of any kind of bank using FinTech tools had been slow and a small percentage of the total. The situation was worse in the case of Saudi banks despite the large amounts of budgetary allocations for technologies in banking as a part of its Vision 2030. This was because of the late entry of Saudi Arabia into these concepts. The need for Islamic banks to follow Sharia rules was not found to be a great problem. All these points indicate to the need for greater acceleration of using FinTech tools for the digital transformation of Saudi traditional banks. Some limitations of this review are the low rigour of the available papers due to methodological limitations, discussion papers without empirical data dominating and a wide range of aims provided in the papers. The need for substantially more work on Saudi banks has been highlighted. The validity of the conclusions from this review needs to be verified using empirical research.

10

Authors: Chanun Chanhom, Khwanta Benchakhan, Sunhanat Jakkapattarawong

This research paper scrutinizes the connection between demographic aspects and the perception of executive leadership at Suan Sunandha Rajabhat University (SSRU), as perceived by academic and support staff. Emphasizing demographic factors such as gender, academic qualifications, and job type, the study explores staff perspectives and their influence on the perception of executive leadership. Data was collected from 253 participants using stratified random sampling and a questionnaire, with findings analyzed via t-Test and One-way ANOVA. The results revealed no significant correlation between gender or academic qualifications and goal-directed leadership. However, a statistically significant difference was found between job classification and goal-directed leadership. These findings offer valuable implications for tailoring leadership approaches at SSRU, suggesting that job type may play a more significant role in shaping leadership perceptions than previously assumed. Consequently, this could inform future leadership strategies, contributing to improved staff engagement and organizational success at SSRU.

10

Authors: Yakov Kryukov, Dmitriy Pokamestov, Andrey Brovkin, Artem Shinkevich, Georgiy Shalin

Power Domain Non-Orthogonal Multiple Access (PD-NOMA) is a promising technique for future wireless networks. Currently, the growing number of researchers are addressing PD-NOMA issues. The great number of known results are based on the Shannon capacity theorem, assuming infinite code block length. However, they cannot be achieved in practice when modulation and coding schemes (MCS) with finite code block length are used. To solve this problem, we propose an approach which allows utilizing LTE MCSs in the link-level simulation of PD-NOMA. In this paper, we present an adaptive MCS map for a PD-NOMA system with two users, considering non-perfect Serial Interference Cancelation (SIC). In addition, we have developed a map for power allocation coefficients (PAC). The max-sum rate criterion and 10%-Block Error Rate constraint are utilized. The MCS and PAC maps allow access point to jointly and adaptively select MCS parameters for the users depending on their SNR values. The proposed MCS maps can be used in a link-level simulation of the PD-NOMA system to near-practical performance.

10

Authors: Rustam R. Gaynutdinov, Sergey F. Chermoshentsev

The paper proposes an approach to predicting the electromagnetic resistance of an unmanned aerial vehicle (UAV) when exposed to an electrostatic discharge. The approach is based on the study of the electromagnetic environment and electromagnetic interference in the interface communication lines of the UAV onboard equipment. The effect of a discharge of static electricity from an engine blade on a UAV fuselage made of a composite material is considered. From the results of the study of the electromagnetic environment in the intra-fuselage space, it can be seen that the strength of the electromagnetic field at the calculation points reaches sufficiently large values, therefore, the electromagnetic compatibility of the UAV onboard equipment may deteriorate. The predicted levels of electromagnetic interference in the interface communication lines will also lead to a malfunction of the onboard equipment of the UAV. Technical measures are proposed that will ensure the electromagnetic stability of the UAV when exposed to a discharge of static electricity.

10

Authors: S. B. Vinay Kumar, B. P. Singh, Raman Batra, Akash Kumar Bhagat

The network connection within the car, the "Controller Area Network" (CAN) bus serves as an alternative protocol for electric automobiles. Tragically, the lack of a data authentication technique in the CAN bus protocol makes it susceptible to several types of assaults, making it easier for attackers to infiltrate the network. The CAN dataset is collected and the collected datasets attains for preprocessing stage using z-score normalization. For feature extraction, a restricted boltzmann machine (RBM) is used to extract the data. Next, our proposed method (MCFO-DANN) is used to identify and mitigate CAN Bus attacks in electric vehicles. Evaluation against other CAN bus anomaly detection methods demonstrates the superiority of MCFO-DANN, exhibiting higher accuracy. This proactive security solution fortifies electric vehicles against cyber-threats, ensuring real-time monitoring and response, thereby preserving the integrity and safety of the CAN Bus network in electric automobiles.

10

Authors: Ranganathaswamy Madihalli Kenchappa, Rakesh Kumar Yadav, Alka Singh, Arvind Kumar Pandey

Industrial automation systems (IASs) are utilized in vital facilities to sustain society's fundamental services. As a consequence, protecting them against terrorist operations, natural catastrophes and cyber-threats is essential. The research on techniques for identifying cyber-attacks in IAS environments is lacking. The study proposed the Stochastic Turbulent water flow optimization based restricted Boltzmann machine (STWFO-RBM) to overcome the challenges. The proposed STWFO-RBM integrates anomaly detection into the fabric of industrial automation, enhancing system resilience and responsiveness. We collected datasets from the water industry and preprocessed them through min-max normalization, and then principal component analysis was used for feature extraction. The results show that the suggested technique applies to a real-world IAS situation, with state-of-the-art accuracy of 97%, F1 score of 96%, precision of 98%, recall of 95% and 6.1s of computational time. Our proposed method is better than the average of earlier endeavors.

10

Authors: Syed Rashid Anwar, Narmadha Thangarasu, Girija Shankar Sahoo, Kumud Saxena

The growing demand for superior metallic components in several industries has emphasized the necessity for effective and dependable inspection techniques. Conventional manual inspection procedures are lengthy, based on personal judgment and susceptible to human mistakes. In this study, we introduce a dynamic multi-layered auto-encoder with a robust deep neural network (DMAE+DNN) system for inspecting flaws in metallic surfaces.We acquired images of the metal surface defects. The neural network design is improved by incorporating a dynamic multi-layered auto-encoder, enabling the system to obtain highly detailed representations of surface data. The results demonstrate the improved performance of the suggested system, showcasing higher levels of recall, precision and F1-score in comparison to conventional defect detection methods. This technological development has the possibility to completely transform quality control procedures by minimizing the need for manual inspections and improving the overall quality of products that heavily rely on metal elements.

10

Authors: Sneha Kashyap, P. S. Raghavendra Rao, Pavan Chaudhary, Savita Yadav

The rising advancements in Industry 4.0 technologies have made more usual to acquire significant volumes of machine operating data in real time. In response to inconsistent data distribution and label scarcity in target domains, this work suggests a machine learning (ML) approach for rolling element bearing failure identification under a variety of circumstances. This study presents, a new method called Composite coyote optimized resilient linear regression (CCO-RLR) for defect recognition and classification in rolling element bearings. Early rolling bearing failure diagnosis is a crucial and time-sensitive operation that guarantees the dependability and security of mechanical fault systems. Initially, the rolling element bearings dataset is collected and preprocessed using Min-max normalization. For extracting the feature, Fourier transform (FT) is employed. The result shows that the CCO-RLR accuracy is 97.8% when compared with those existing methods. Our suggested method offers an effective means of quantifying flaws and significantly improving classification effectiveness.

10

Authors: Vinod Mansiram Kapse, Arun Kumar Marandi, Beemkumar Nagappan, Ankita Agarwal

The increased durability and performance features of polymer concrete panels have led to their widespread application in construction. The manufacturing of precise and effective techniques for identifying forms and flaws is vital to guarantee the high quality of these panels. To increase the accuracy of structure and defect-recognition in polymer concrete panels, this study presents a new Stochastic raven roosting optimization enhanced artificial neural network (SRRO-EANN) forecasting technique. The data sample used to assess the completed model fits the training dataset is referred to the test dataset. The Gaussian filter (GF) is a tool used in the pre-processing and Principal Component Analysis (PCA) feature extraction, leading to more effective utilization and understanding the defect capturing. The findings of the research indicate the effectiveness for the future development of forecasting technologies in the realm of quality control and building material inspection.

10

Authors: Amita Shukla, Amit Prakash Sen, D. Ganesh, B.P. Singh

The Hybrid Galactic Gravitational Optimization (HGGO) method of Three-area power system (3APS) control of frequency is the main subject of this work. The suggested method has advantages of easy implementation, computing efficiency and consistent convergence. The goal is to use the HGGO algorithm to fine-tune the Proportional-integral-derivative (PID) controller and create stable, trustworthy system. A thorough simulation of the three-area Load Frequency Control (LFC) system is carried out in MATLAB-SIMLINK environment. By optimizing PID control settings. The previous subject moves to the gravitational search algorithm (GSA), which is known for its optimization ability but is hampered by problems with local optima. This is addressed by the clustering-based learning used by the Hybrid Galactic Gravitational Optimization, which divides the programmed into clusters and uses a variety of techniques. To validate the proposed HGGO controller, load disturbances are applied to the power system and simulated outcomes for several HGGO-based load configurations are obtained.

10

Authors: Akhilendra Pratap Singh, Mohammad Shahid, Anupam Kumari, M. P. Karthikeyan

In the dynamic realm of manufacturing, it is essential to optimize production processes to attain efficiency and competitiveness. This study presents an innovative enhanced dragonfly optimization (EDFO) method to improve production by utilizing a diverse strategy that combines swarm intelligence and virtual cell development. The suggested methodology includes the parallel EDFO algorithm, which is a cutting-edge variety of swarm intelligence, to address the intricate optimization difficulties related to virtual cell creation. The virtual cell construction process entails the consolidation of machines into cells to optimize output and reduce manufacturing lead times. The benchmark test results offer valuable insights into the algorithm's capabilities and effectively demonstrate its effectiveness in optimizing virtual cell generation for various manufacturing conditions. The proposed approach, which simultaneously takes numerous essential characteristics, is a comprehensive solution for improving production efficiency in virtual cellular manufacturing systems due to its multifunctional nature.

10

Authors: Vijay Prasad, Pranab Das, Y. Jayanta Singh

Software quality assurance has grown in importance in the fast-paced world of software development. One of trickiest parts of creating and maintaining software is predicting how well it will perform. The term "computer evaluation" refers to use of advanced AI techniques in software quality assurance, replacing human evaluations and paving the way for a new era in software evaluation. We proposed Hybrid Elephant herding optimized Conditional Long short-term memory (HEHO-CLSTM) to estimate Software Quality Prediction. Software quality prediction and assurance has grown in importance in ever-changing world of software development. Software quality prediction encompasses a wide range of activities aimed at improving the quality of software systems via the use of data-driven approaches for prediction, evaluation and enhancement. We have collected Software Defects data and we feature extracted the attributes using linear discriminant Analysis (LDA). The suggested system improves the accuracy, AUC and Buggy instance compared with the current methods.

12

Authors: Nivedita Tiwari, Lalji Prasad

Traffic flow prediction is a research topic signified by several researchers in a league span of disciplines. Traffic flow prediction is an important aspect in Intelligent Transport Management System (ITMS). In this context, one of the most in-demand techniques of Machine Learning, especially Time series based techniques, helps in predicting traffic flow forecasting and increases the accuracy of the prediction model. In order to deliver extremely precise traffic forecasts, it is crucial that we put the prediction system into practice in the actual world. Our aim is to perform computations related to traffic on the traffic datasets and find out the accuracy for each model. For this purpose we are using three distinct time series models: Long Short Term Memory (LSTM), the Autoregressive Integrated Moving Average (ARIMA), and the Seasonal Autoregressive Integrated Moving Average (SARIMA). From the results obtained, it is concluded that the proposed model achieves highest prediction accuracy with the lowest root mean squared error.

12

Authors: Matthew Barsalou, Robert Perkin

There are many possible quality tools available for RCA and this research seeks to identify which quality tools are used by quality professionals specifically when performing an RCA. An anonymous survey was emailed to an organization’s quality leaders at locations around the world. The survey asked the respondents to list the quality tools that they use when performing an RCA as well as the quality tools that they think everybody should know for RCA. The results were then statistically analyzed. The Ishikawa diagram is both the most commonly used quality tool and the most recommended quality tool for RCA. Five whys and is/is-not were also commonly used quality tools. This research identifies which quality tools are actually used by quality professionals in industry when performing an RCA. This information can be useful for organizations when selecting a limited tool set for RCA.

12

Authors: Shweta Singh, Vivek Ranjan, Padmaja Tripathy, M. N. Nachappa

Finding a balance between the loads and generating demands in power systems is a significant challenge since power producers require important inputs such as security, reliability and quality. In this study, a unique method for balanced load frequency control (BLFC) is proposed in two-area power systems, integrating a customized World Cup Algorithm (CWCA)-Driven PID optimization. The primary objective is to enhance the stability and performance of power systems. For this purpose, the integral time-multiplied absolute error (ITAE) is minimized by evaluating the objective function using time-domain simulation. The proposed methodology aims to improve the overall response of the control system, ensuring a reliable and stable power supply. Within the scope of two-area power systems with BLFC, simulation results show the effectiveness and resilience of the CWCA-Driven PID Optimization. The outcomes of the proposed.

12

Authors: Ashuvendra Singh, Sandeep Singh Rawat, Navneet Kumar

The planning of tall buildings is an important part of urban growth, and more and more attention is being paid to sustainability, safety, and efficiency in the building process. Traditional design and manufacturing processes frequently have trouble maximizing these factors, which results in inefficiencies and higher prices. This research introduces a revolutionary strategy that combines dynamic firefly tuned-AdaBoost (DFT-AdaBoost) in order to address the drawbacks of conventional tall building design methodologies. The goal is to increase manufacturing efficiency while also enhancing the tall building design's sustainability and safety features. The DFO is employed in an iterative manner to modify various design parameters, including material types, sizes, and shapes. On the other hand, AdaBoosting is utilized to improve the predicted accuracy of the model. The iterative nature of this methodology enables the ongoing improvement of design solutions in order to get the necessary level of manufacturing efficiency. The findings of this study indicate notable enhancements in manufacturing efficacy pertaining to the construction of tall buildings. The utilization of the DFT-AdaBoost method enables the identification of optimized design parameters, resulting in the reduction of material waste and a decrease in production costs. This study highlights the DFT-AdaBoost approach's potential as a potent tool for improving manufacturing effectiveness in tall building designs. This method contributes to the construction of tall buildings that are more cost-effective, safe, and environmentally responsible by integrating real-time structural optimization with manufacturing process prediction.

14

Authors: J. Nancy Namratha, P. Venkata Subramanian, Rama Koteswara Rao Alla

A modern power system demands an open communication channel to support the vast number of real-time data exchanges, which may introduce time delays and communication failures thus creates new challenges in power systems. To cope up with these issues, the paper proposed an Internal Model-Based Robust Controller (IMBRC) and IMBRC-PID controller designs for the decentralized LFC (Load Frequency Control) of the modern power system. Initially, a finite-ordered linear model of the power system integrated with RES (Renewable Energy Sources) and aggregated Electrical Vehicles (EV) has been developed. Later the full-order model was employed in the proposed design to achieve complete decentralized, robust, more reliable, and effortless control performances. The Internal Model Compensator (IMC) filter time constant is tuned using Artificial Bee Colony (ABC) optimization algorithm. The objective function considered was the scalarized integral of squared and absolute errors with various weighting factors. The Least-Square Model (LSM) approximation of the IMBRC transfer function determines the PID controller gains. The controller's robustness is verified for the power system components affected by structured and unstructured uncertainties. The error performance indices and simulation results convey that the suggested design keeps the system robustly stable even when subject to varying time delays and uncertainties.