Periodicals of Engineering and Natural Sciences (PEN - International University of Sarajevo)
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Enhanced IoT Wi-Fi protocol standard’s security using secure remote password
Get PDFIn the Internet of Things (IoT) environment, a network of devices is connected to exchange information to perform a specific task. Wi-Fi technology plays a significant role in IoT based applications. Most of the Wi-Fi-based IoT devices are manufactured without proper security protocols. Consequently, the low-security model makes the IoT devices vulnerable to intermediate attacks. The attacker can quickly target a vulnerable IoT device and breaches that vulnerable device\u27s connected network devices. So, this research suggests a password protection based security solution to enhance Wi-Fi-based IoT network security. This password protection approach utilizes the secure remote password protocol (SRPP) in Wi-Fi network protocols to avoid brute force attack and dictionary attack in Wi-Fi-based IoT applications. The performance of the IoT security solution is implemented and evaluated in the GNS3 simulator. The simulation analysis report shows that the suggested password protection approach supports scalability, integrity and data protection against intermediate attacks
Design of a microwave based mobile thermo-chemical unit for biomedical waste treatment
Get PDFBiomedical waste (BMW) contains pathogenic microorganisms that may severely harm the community and environment. Due to the Covid pandemic-2019, isolated wards at health care units and even due to the home treated patients; vast quantities of BMW are generated. Covid-19 converts even ordinary waste such as gloves, testing kits, and personal protective equipment into high-risk BMW. The appropriate disposal of such waste involves safety, affordability, and efficacy; hence can be considered a complex issue. A solution proposed in this article is an OSBMWTU (on-site biomedical waste treatment unit) by using microwave radiation. The possibility of enhancing the thermal effect of microwave radiation by using chemical additives was tested. The proposed machine reduces waste volume, inactivates microorganisms, and disposes BMW on-site. Findings suggest that adding butter spray to microwave radiation enhances thermal effectiveness by 43%, increasing treatment temperature while minimizing time, power, and running costs. The proposed machine will work automatically after filling the BMW, thus, minimizing the human involvement. It prevents bio-hazardous waste accumulation and decreases its volume by up to 80%. The designed machine is characterized by safety, low cost, and small dimensions. A machine that can handle 72 kg BMW/day can be set up on-site in an area of 1.5 m2. The suggestion of the proposed machine as a BMW management and treatment system will reduce environmental pollution due to BMW during COVID-19 and even after the pandemic
Jackknifing K-L estimator in Poisson regression model
Get PDFAt the point when there is collinearity between the reaction variable and various illustrative factors, displaying the connection between the reaction variable and a few informative factors is troublesome. While surveying count information, the Poisson relapse model (PRM) is generally utilized in applied research. A shrinkage assessor is a consistently utilized answer for the multicollinearity issue. One of these shrinkage assessors is the Kibria and Lukman assessor (K-L). In this paper, a jackknifed variant of the K-L assessor in the Poisson relapse model is proposed, which consolidates the Jackknife interaction with the K-L assessor to diminish inclination. As far as outright inclination and mean squared blunder, our Monte Carlo recreation discoveries infer that the proposed assessor can give a critical improvement over other contending assessors
The role of creative accounting in increasing the marketing of shares and their profits in the Iraqi stock exchange
Get PDFAs a result of creative accounting, many firms in the Iraqi stock market are able to achieve two very significant goals: first, to boost the market value of their shares and thus gain the biggest trading volume in shares, and second, to lower their earnings in order to decrease the tax burden. First by growing its profits, then by evading taxes and denying the state its right to its money. Using this research, we hope to demonstrate how financial institutions that use innovative accounting tools in the preparation of their financial statements impact trade activity. Trading volume is critical to accurately forecasting stock price patterns, allowing investors to maximize their wealth. So, without accounting information, the financial markets can\u27t play their function in drawing in investment, ensuring a well-balanced use of resources, and making reasonable economic decisions, because they don\u27t have the accounting knowledge to do so. There were four sections to the research, which included a look at the research methodology and previous studies, a look at creative accounting practices, and an analysis of the financial statements for the top and bottom ten companies in terms of trading volume using Miller\u27s model to detect profit manipulation that occurred in those companies. The final section contained the most significant findings and recommendations
Detection of hand gestures with human computer recognition by using support vector machine
Get PDFMany applications, such as interactive data analysis and sign detection, can benefit from hand gesture recognition. We offer a low-cost approach based on human-computer interaction for predicting hand movements in real time. Our technique involves using a color glove to train a random forest classifier and then predicting a naked hand at the pixel level. Our algorithm anticipates all pixels at a rate of around 3 frames per second and is unaffected by differences in the surroundings. It\u27s also been proven that HCI-based data augmentation is more effective than any other way for enhancing interactive data. In addition, the augmentation experiment was carried out on multiple subsets of the original hand skeleton sequence dataset, each with a different number of classes, as well as on the entire dataset. On practically all subsets, the proposed base architecture improved classification accuracy. When the entire dataset was used, there was even a modest improvement. Correct identification could be regarded as a quality indicator. The best accuracy score was 94.02 percent for the HCI-model with support vector machine (SVM) classifier
Optimization algorithms for transportation problems with stochastic demand
Get PDFThe purpose of this paper is to solve the stochastic demand for the unbalanced transport problem using heuristic algorithms to obtain the optimum solution, by minimizing the costs of transporting the gasoline product for the Oil Products Distribution Company of the Iraqi Ministry of Oil. The most important conclusions that were reached are the results prove the possibility of solving the random transportation problem when the demand is uncertain by the stochastic programming model. The most obvious finding to emerge from this work is that the genetic algorithm was able to address the problems of unbalanced transport, And the possibility of applying the model approved by the oil products distribution company in the Iraqi Ministry of Oil to minimize the total costs, Where the approved model was able to minimize the total costs by 25%. A future study investigating optimization heuristic with stochastics demand would be very interesting
Compressive strength assessment of normal and self-compacting concrete made with recycled coarse aggregate using in-situ tests
Get PDFThere is an increasing trend in construction for using recycled coarse aggregate (RCA) concrete, which is a more sustainable approach for reducing natural resource consumption. One typical method for developing more environmentally friendly structures is to partially substitute natural aggregate. limited studies investigated the use of in-situ tests to assess the compressive strength of concrete made with RCAs. In this study, ultrasonic pulse velocity (UPV) and core sampling (CST) tests were used to evaluate the compressive strength of normal vibrated (NVC) and self-compacting (SCC) concretes made with recycled coarse aggregates (RCAs). Four different compressive strengths ranging from 25 to 55 MPa were adopted for each concrete type to consider the effect of replacing 0, 20%, 40%, 60%, 80%, and 100% of the required natural coarse aggregates (NCAs) with (RCAs). Exponential relationships were adopted to relate the UPV values with the compressive strength for both RCA-NVC and RCA-SCC. Also, suggested factors were adopted to correct equivalent core strengths for both RCA-NVC and RCA-SCC. The results of both test methods (UPV) and (CST) showed good correlations to estimate the compressive strength of RCA-NVC and RCA-SCC with confidence limits of 93%
Using machine learning algorithm for detection of cyber-attacks in cyber physical systems
Get PDFNetwork integration is common in cyber-physical systems (CPS) to allow for remote access, surveillance, and analysis. They have been exposed to cyberattacks because of their integration with an insecure network. In the event of a violation in internet security, an attacker was able to interfere with the system\u27s functions, which might result in catastrophic consequences. As a result, detecting breaches into mission-critical CPS is a top priority. Detecting assaults on CPSs, which are increasingly being targeted by cyber criminals and cyber threats, is becoming increasingly difficult. It is potential that (AI) Artificial Intelligence as well as (ML) Machine Learning will make this the worst of times, but it also has the potential to be the best of times. There are a variety of ways in which AI technology can aid in the growth and profitability of a variety of industries. Such data can be parsed using ML and AI approaches in designed to check attacks on CPSs. We present the new framework for the detection of cyberattacks, which makes use of AI and ML. We begin a process to cleaning up the data in the CPS database by applying normalization to eliminate errors and duplication. The features are obtained by using a technique known as Linear Discriminant Analysis (LDA). We have suggested the SFL-HMM together with HMS-ACO process as a method used for detection of the cyber-attacks. A MATLAB simulation used to evaluate the new strategy, and the metrics obtained from that simulation are compared to those obtained from the older methods. According to the findings of several studies, the framework is significantly more effective than conventional methods in maintaining high levels of privacy. In addition, the framework outperforms conventional detection algorithms in words of detection rate, the rate of the false positive, and calculation time, respectively
Characterization of fatigue properties of 3D printed polylactic acid
Get PDFThe goal of this research is to determine how tiredness behavior may be measured (PLA). A To A variety of technical data sets were S-N curves were chosen and statistically re-analyzed. as described in the following section, to generate a negative reference value reverse slope also an improved tolerance limit of 106 2 failure cycles. The average effect of stress on fatigue can be indicated via administering the highest level of stress achievable during the cycle, according to experimental data examined after treatment. Furthermore stress/strength study may be effectively performed until the printing orientation seems to possess minimal influence on PLA\u27s general tiredness behavior. carried out via taking the printing orientation into account. A homogeneous, linearly elastic polymer is described. When acceptable experimental findings are not available, the paper explains how to conduct a fatigue evaluation (with a survival probability better than 95%). The study demonstrates how to do so via utilizing standard fatigue curves with a negative-inverse regression of 5.5 and a tolerance limit (2 106 cycles to failure) equivalent to 10% of the material\u27s maximum
Enhancing imputation techniques performance utilizing uncertainty aware predictors and adversarial learning
Get PDFOne crucial problem for applying machine learning algorithms to real-world datasets is missing data. The objective of data imputation is to fill the missing values in a dataset to resemble the completed dataset as accurately as possible. Many methods are proposed in the literature that mostly differs on the objective function and types of the variables considered. The performance of traditional machine learning methods is low when there is a nonlinear and complex relationship between features. Recently, deep learning methods are introduced to estimate data distribution and generate values for missing entries. However, these methods are originally developed for large datasets and custom data types such as image, video, and text. Thus, adopting these methods for small and structured datasets that are prevalent in real-world applications is not straightforward and often yields unsatisfactory results. Also, both types of methods do not consider uncertainty in the imputed data. We address these issues by developing a simple neural network-based architecture that works well with small and tabular datasets and utilizing a novel adversarial strategy to estimate the uncertainty of imputed data. The estimated uncertainty scores of features are then passed to the imputer module, and it fills the missing values by paying more attention to more reliable feature values. It results in an uncertainty-aware imputer with a promising performance. Extensive experiments conducted on some real-world datasets confirm that the proposed methods considerably outperform state-of-the-art imputers. Meanwhile, their execution time is not costly compared to peer state-of-the-art methods