SQL injection attack is one of the most serious security vulnerabilities in many Databases Managements systems. Most of these vulnerabilities are caused by lack of input validation and SQL parameters used particularity at this time of technology revolution. The results of a SQL injection attack (SQLIA) are unpleasant because the attacker could wipe the entire contents of the victim's database or shut it down. As such, SQLIA can be used as important weapons in cyber warfare. As an attempt of breaching of number of application data bases systems two SQL injection techniques were used to successful locating vulnerable points during this research which are Blind Text Injection Differential and Error based Exploitation. The motivations behind were to find out where the databases systems are most likely to face an attack and proactively shore up those weaknesses before exploitation by hackers. The success of both techniques is a result of poor web server (online database server) design especially in the selection of error messages (or answers) they display to website users if something goes wrong. The approach through examination of error messages (error codes) did enable to precisely know the backend Database Management System (DBMS) type and version and what exactly are parameters (variables) which can allow “illegally” injecting codes (a SQL query). Additionally, the paper presents SQLIA cases and their impact in Tanzania cyber space as well as it suggests the possible mitigation ways while reflecting the collected data with what currently existing in cyberworld as far as SQL injection attack is concern to present the reality.
Anak Agung Ngurah Gunawan, I. Made Satriya Wibawa, Anak Agung Ngurah Surya Mahendra, Anak Agung Ngurah Franky Kusuma Negara, Anak Agung Ngurah Frady Cakra Negara
Objective: Evaluating the diagnostic performance of SVM to classify benign and malignant by performing a meta-analysis.
Methods: The data used for this study were secondary data. It consisted of 221 mammogram images (mean age 57.5 years) with 164 malignant and 57 benign, taken from a radiological database that has been examined by a radiologist with more than 20 years of experience. Also, histopathological record data that had been examined by an oncologist with more than 20 years of experience. Mammograms were taken from January 2022 to June 2022. In all, 221 mammograms consisting of 164 malignant and 57 benign were used as SVM method training, and 20 mammograms consisting of 10 malignant and 10 benign were used to test the performance of the SVM method. It was then evaluated using pathology results as the gold standard.
Results: Benign had a significantly lower deviation (an average of 29.2661230 ± 10.14916673) than malignant (an average of 33.1841234 ± 11.70238757). The SVM method performance value obtained the values of TP, FP, TN, FN, accuracy, sensitivity, Specificity, and Precision, respectively 7,7, 3, 3, 50%, 70%, 30%, and 50%.
Conclusion: A proper performance to distinguish benign and malignant can be obtained using the physical deviation parameters with the SVM classification approach. However, these findings should be proven in larger datasets with different mammographic scanners. Our meta-analysis shows that the physical parameters and SVM have high sensitivity but low specificity. Of the nine physical parameters in the mammogram, only the parameter deviation was significant to distinguish between benign and malignant. The SVM method proved to be able to differentiate between benign and malignant.
This study harnesses the useful number properties of the residue number system (RNS) to minimise energy consumption in a wireless sensor network. In a traditional cluster-based wireless sensor network, large bit representations of aggregated packets are transmitted to the base station. However, large bit patterns of packets are slower compared to smaller bits. The proposed approach splits aggregated data into a pre-specified number of transmission channels using a moduli set. Cheap energy cost routes from the cluster heads are computed to deliver the chunked aggregated data to the base station. Forward and reverse converters are proposed to encode data into RNS and decode the RNS data that reaches the base station. MATLAB simulation is used to implement the proposed data splitting method and to evaluate network performance. The experimental results suggest that the proposed method is more effective at minimising transmission energy when compared with traditional approaches in which complete packets are transmitted.
This research sought to find out the influence of top management support on ERP Implementation; find out the impact of business processes reengineering on ERP Implementation; identify the impact of ICT Infrastructure on ERP Implementation; and establish the influence of tacit knowledge users (user involvement) on ERP Implementation. The study used descriptive research design, where it obtained a sample size of 70 respondents and selected the respondents using stratified proportionate sampling. The study data collected from primary sources using structured questionnaire directly administered to the respondents based on the drop and pick method. Data was analysed using quantitative analysis to produce descriptive statistics followed by inferential analysis for estimating a model and it results represented using figures and tables and explained using narrative and its data analysis assisted by SPSS software. The study concludes that; there is a positive and significant relationship between top management support and ERP Implementation; business processes reengineering positively significantly influences ERP Implementation, Information communication technology infrastructure has a significant moderate influence on its ERP implementation, and tacit knowledge users have significant moderate influence on ERP Implementation. The study recommends that UN-Habitat should; clearly spell out the role of top management involvement, review its business process policy to accommodate various system development activities improves existing ICT infrastructure to match the proposed requirement of the vendor to implement ERP system; and acquire the appropriate tacit knowledge users for ERP implementation possessing.
Aims: To find active compounds from natural ingredients that have the potential to be antivirals of SARS-CoV-2.
Study Design: Simulation research.
Place and Duration of Study: Physics Laboratory, Department of Physics Education, Universitas Kristen Indonesia, between December 2021 and August 2022.
Methodology: The method used is a computational simulation commonly known as docking simulation or molecular docking. There are several steps taken, namely ligand and receptor preparation, docking simulation and analysis of simulation results.
Results: The results obtained were from 22 ligand compounds of natural material selected as helicase receptor inhibitors, 14 ligand compounds were found that met the requirements according to Lipinski's five rules, namely Emodin, Luteolin, Curcumin, Kaemferol, Quercetin, Myricetin, Scutellarein, 10-Gingerol, Shogaol, Mangostin, Piseatanol, Diallyl disulfide, Cyperotundone and Eugenol. Of the 14 ligand compounds simulated with helicase receptors, it turned out that 14 stable ligand compounds were used as helicase receptor inhibitors. However, among the 14 ligands, myricetin is the most stable ligand with the smallest Gibbs free energy value, which is -8.7 kcal/mol.
Conclusion: An active ingredient compound has been found that has the potential as an antivirus sars-COV-2 in the Helicase receptor, Myricetin from clove plants (Syzygium aromaticum). These results can be used as a basis for drug development for the development of SARS-COV-2 antivirus in the future.
Malnutrition is characterised by the insufficient intake of certain nutrients and the inability of the body to absorb or use these nutrients. This health problem keep going to be a real challenge among children under five years of age in developing countries, including Yemen, despite good aids provided. So, malnutrition is a health problem that significantly participates to child mortality rate in Yemen. The overall prevalence of malnutrition among children in Dhamar Governorate has significantly higher rates compared to other Yemeni governorates.
In this paper, an intelligent predictive system using data mining classification techniques such as J48 decision tree, Bagging and Multi-Layer Perceptron Neural Network (MLPNN) for predicting malnutrition status of under-five children in Dhamar Governorate is proposed.
The main objective of the present paper is to study these classification techniques to predict the 2018-2019 Dhamar Governorate, Yemen Demographic and Health Survey (DGYDHS) dataset and find an efficient technique for prediction. This dataset is imbalanced, so Synthetic Minority Over-sampling TEchnique (SMOTE) is utilised to balance the dataset.
The obtained results were evaluated by the famous performance metrics like Accuracy, TP (True Positive)-rate, FP (False Positive)-rate, Precision, F-Measure, Receiver Operating Characteristics (ROC) graph and execution time. The obtained results revealed that the three classifiers with all attributes have higher predictive accuracy and are generally comparable in predicting malnutrition cases.
Recognizing the handwritten characters and converting them into machine-editable text is very tedious due to the diversity of writing styles and character patterns. Extracting data from images and identifying the characters becomes more complicated when a language consists of compound structures and characters, such as Bengali. There has been a lack of programs for recognizing Bengali scripted basic and com-plex numeric signs and letters with high accuracy. This paper develops a novel approach to extracting and identifying Bengali handwritten primary characters, digits, and primarily used compound characters. In this proposed model, an image containing Bengali handwritten text takes as input and processed. Then processed images are segmented into lines and characters. The features are extracted from segmented characters and recognized using a Convolutional Neural Network (CNN). The CNN obtains 98.23% accuracy in the training dataset and 96.02% in the validation dataset. Apart from that, the proposed model has gained 89.6% precision and 92.6% recall scores on scanned image data.
CCTV monitoring system is an essential security tool for visual surveillance accelerating the investigation in potential criminal activities when the need arises. Although expensive, universities mostly with public-access campuses in general, all need this system mainly to maintain safety and security in real-time, allowing legitimate students and staff to access campus resources and concurrently preventing any unauthorized persons access within the campus as well as responding to incidents with necessary action. C. K. Tedam University of Technology and Applied Sciences (CKT-UTAS) is a university in the Upper East Region of Ghana that does not have such a monitoring system. Since it is a newly established public university, its allocated funds are limited and could not be used to establish such an expensive system. To supplement their ongoing efforts in building security monitoring system, this study constitutes the blueprint procedures for building economical but reliable and efficient CCTV camera system for monitoring the property on campus and also the in and out of students and university staff members. The CCTV system was tested in monitoring vantage security post of the University. After observing and analyzing the trends in data from both the physical and our proposed automated monitoring approach, it can be concluded that the CCTV camera setup outperforms the physical and manual form or monitoring vantage security posts on University campus. Since the of monitoring security post using the proposed CCTV setup is advantageous in requiring lesser human effort and skills, it can be recommended for universities with low income-flow and low budget. This probably can make the university campus more secure and reliable.
Aims: The study aims to identify ways and potential solutions to automate the assembly and production process for passenger car gearboxes.
Object of Research: Assembly and production process for car gearboxes.
Subject of Research: Modern and evolutionary automation tools that have the potential to be implemented in the assembly processes of automotive transmission controls.
Methodology: To achieve this goal, as part of this study, it is planned to apply methods of bibliometric analysis of leading scientometric databases to obtain correlation relationships and analytical conclusions regarding the vector of development of automation means of passenger car gearbox assembly process.
Results: As a result of the research by means of scientometric analysis and correlation the vector of probabilistic technical solutions of integration and development of automation means of the sequence of production operations during the assembly of the transmission, as well as adaptive framework-design solutions for the implementation of tools of the fourth iteration of industrial-industrial progress in the production processes of assembly of the studied technical control means and logical-technological connection of the elements of the transmission system, which affect the overall process of automotive manufacturization.
Conclusion: The passenger car gearbox is a multi-component, complex system whose assembly is a complex multi-operational process, and given the high responsibility of this machine element, there is an urgent need to introduce modern automation tools into the assembly and production processes, which will significantly optimize global automatofactoring. The practical results of the present study consist in the formation of a focus scientometric database of profile data, identification of a potential vector of development of means and systems of automation of assembly-production operations, identification and formation of solutions for the implementation of modern means of automated production in the actual global automotive manufacturing, which allows to get the optimum ratio of production costs/quality of products by improving the manufacturability, productivity and flexibility of processes of assembly of multi-element and multi-component automotive systems and structures.
The complex nature of climate change with multitude of underlying factors poses a major hindrance in data analysis and decision making by policy makers. Here, we utilize data analytic techniques to identify the best set of climate change indicator variables that could predict precipitation and temperature data for India. The observed values of important climatic parameters namely, rain, maximum temperature, minimum temperature, and mean temperature in India were analyzed along with the observed values of selected socio-environmental indicators featured by WHO for climate change as exogenous variables for a period of 61 years. Data were pre-processed to identify ten exogenous indicators which were then modelled using Vector Error Correction Model (VECM). 1024 VECM models were built and evaluated for the prediction of the four endogenous variables using all possible combinations of the selected indicators. Seven exogenous variables were determined as the best set of indicators based on the AIC of the different models. The model built using the identified variables was compared to others to illustrate the probable impact of this combination of variables. The study thus demonstrates a simple but rational data-driven approach for use in decision making.
Aims: Android system is widely chosen by smartphone users in Indonesia. One of the reasons for its popularity is the large number of free applications supported by Google Play Store. The large number of applications on the Google Play Store provide many choices for the needs of the community. But sometimes, this actually makes people have difficulty in choosing an application they need. This research will create a decision support system (DSS) in choosing the best application, so that it can help the community in choosing the application they need.
Methodology: The factors used in choosing include rating, file size, compatibility and others. The method used to determine the best application in this research is the SMART (Simple Multi Attribute Rating Technique) method. This method can be used to support decisions in choosing between several alternatives. The implementation is made web-based with the PHP language to make it easier for the public to access this system.
Results: The result of this research is the ranking of educational applications based on the criteria of rating, reviews, size and installs that can be taken into consideration by users in choosing applications.
Conclusion: SMART method can be used easily to generate application rankings. The results of data processing may change if the priority or weight of a criterion changes.
Acceptance of new students at several tertiary institutions, both public and private, will affect the learning and teaching process at these tertiary institutions. Because knowing the number of new students accepted is one way that can be used in determining the teaching and learning process.
This study aims to predict the number of new students for the coming year with the Moving Average method and calculate the absolute mean error rate for this type of data analysis. This method is applied to one of the private universities in Indonesia. To know the prediction of the number of new students every year, or to find out the estimated number of new students for the coming year.
The data used is the previous 7 years at one of the Indonesian private universities.
The results of this study are expected to be able to assist these tertiary institutions in planning a good teaching and learning process, because the number of new students will affect the number of existing lecturer ratios.
The academic result is the most important thing in a student's career. This result depends on their academic performance and many other factors. Educational data mining can help both students and institutions develop their academic performance. For analysis of their performance, we can use new techniques Deep Learning, Convolution Neural Networks, Data Clustering, Optimization Algorithms, etc. In machine learning. Using Deep Learning, we will predict the student’s performance yearly in the form of CGPA and compare that with the real CGPA. A real dataset can boost the prediction performance. We used a real dataset from the Institute of Science, Trade & Technology (ISTT). We used a total of 18 data factors to predict the performance and the data factors are: Class Performance, Test Marks, Class Attendance, Due Time Assignment Submission, Lab Performance, Previous Semester Result, Family Education, Freelancer, Relationship with Faculty, Study Hours, Living Area, Social Media Attraction, Extra-Curricular Activity, Drug Addiction, Financial Support from Family, Political Involvement, Affair & Year Final Result.
The Internet of Things’ (IoT) market is expected to grow exponentially at the global level in the coming years, due to the proliferation of more reliable and faster networks resulting from the extensive rollout of 5 to 10 G mobile networks. By 2025, it is expected that worldwide projection of IoT connected devices will be pegged at 30.9 billion units. Despite the potential benefits of the new technology, security in IoT is a major threat. According to HP, 70% of IoT devices are vulnerable to sniffing attacks and reliable solution is yet to be found. The standard cryptographic algorithms such as RSA and AES provide good security but their utilization in IoT is questionably due to hardware and energy constraints for computationally expensive encryption schemes. However, elliptic curve- based cryptography, a recent paradigm in public key cryptography, achieves the same level of security with smaller key sizes. On the other hand, the total score of performance of an elliptic curve-based cryptosystem depends largely on the efficiency of the arithmetic operations performed in it. It is against this background that this paper proposes efficient elliptic curve arithmetic for implementing ECC based schemes suitable for IoT systems implementations. Elliptic curve point arithmetic implementations in projective coordinate systems over binary extension fields introduce higher efficiencies in software. In this regard, this paper has proposed an improved López-Dahab point arithmetic methods on non-supersingular elliptic curves over . The results show 69.20% improvement in Point Doubling, 44.68% in Point Addition and the scalar point multiplication execution time is decreased by 48.80%.
Wasting water has been a big problem in human society throughout the world. It can happen either in developed or developing countries. They tend to waste the water without knowing it. Moreover, people's awareness of the importance of using water wisely is still low. While they think the source of water is limitless, in fact, it is not. In contrast, the availability of freshwater in the world is limited. Furthermore, if the water is overused without reservation, it can trigger the phenomenon of water scarcity. However, the problem of wasting water can be reduced by using the water wisely and efficiently. With the growth of Internet of Things technology, it can help people use water efficiently by monitoring their water consumption. The Internet of Things (IoT) is a network of real-world items that may communicate with other electronic devices and systems via the internet by using sensors, software, and other technologies. IoT devices can be used for the purpose of household or the industrial. This paper focuses on designing and implementing a residential water tank system embedded with the Internet of Things technology for monitoring the water consumption in a household. The system will measure the water consumption from the residential water tank and send the data to a cloud server. Users will then be able to monitor it using a mobile device. With the data, users could change their habits of using water and start to use it wisely. Thus, the water shortage can be prevented.
Our society's diploma of reliance on IT and our online world is developing daily. Cyberspace, the call given to the worldwide and dynamic domain, composed of the infrastructure of the statistics era consisting of the net networks and statistics and telecommunications structures has supplied extraordinary globalization that gives new opportunities, but additionally includes new challenges, risks, and threats. Knowledge of its threats, dealing with the risks, and constructing suitable prevention, defense, detection, evaluation, investigation, and recuperation is essential. Given the present-day assessment of the statistics safety and intrusion detection, there's without a doubt a want for a choice and manipulation framework to cope with problems like assault modeling, evaluation of detected threats, and choice of reaction actions. We look at the goals of designing a mathematical version for gamified cybercrime tracking in a community environment.
Reactor is widely used in biology, chemical industry, metallurgy, environmental protection and other fields, playing an irreplaceable role. With the development of science and technology and the concept of green development, the application of artificial neural network to optimize the reactor reaction conditions has become a trend. Artificial neural network plays an important role in reactor optimization because of its strong fault tolerance, the ability to express complex nonlinear relations and perform complex operations. This paper will briefly describe the basic principle and research progress of artificial neural network, and its application in reactor design.
With the progress of science and technology and the continuous improvement of living standards, robots are more and more widely used in life and production, and robot grasping technology is also constantly improving. In practical application, accurate grasp detection of target objects is an important part of robot grasping task. In this paper, the parallel two-fingered gripper is used as the end of the robot arm's grasping, and the research status of grasping detection, which is the key part in the grasping process of the robot arm based on vision, is summarized. The 2D planar grasping and 6-DOF spatial grasping are compared and analyzed in detail. At the same time, it also summarizes the commonly used evaluation indexes of capturing data sets and capturing detection, and points out the challenges faced by vision-based robot capturing and the future direction of solving these challenges.
In the healthcare management domain, blood donation receives a particular interest due to its crucial and vital importance in saving people’s lives. In Iraq, the blood donation procedure usually consumes a lot of time for donors as it is carried out through a non-automated and paper-based process, which is done only in hospitals/ medical centers for those who are willing to donate. Patients who are in a need for blood donation may have to wait until they receive the service, and this may results in dramatic or undesired consequences. At the same, the blood donation procedure negatively affects people who are willing or wish to donate blood and mostly leads to ignore this matter by a lot of them unless there is a critical situation concerning one of their family members. This paper propose a Mobile-Base Registration System for Blood Donation (MBRS_BD) using Firebase Cloud Messaging (FCM) to manage the process of donor’s registration automatically using a smartphone to simulate, ease, and minimize the time required for that. Donor can register in any available Iraqi hospitals/ medical center using MBRS-BD and go in the exact time to complete his/her donation process.
The word "neural networks" has a strong connotation. It alludes to devices that resemble minds and may be laden with the Frankenstein mythos' science fantasy meanings. One of the top aims of this report is to deconstruct neural networks and demonstrate how they function. Although they do have much to do with minds, their research crosses over into other scientific disciplines, such as technology and math. While some numerical terminology is needed for quantified defining such laws, processes, and frameworks, the goal is to do this in a non-technical manner.
The huge amount of data generated from heterogeneous sources such as social networking sites, healthcare applications, sensor networks and many other sources are drastically increasing from time to time swiftly. Big Data is described as extremely large datasets that have grown beyond the capability to manage and analyze them with traditional database processing tools. Big data analytics is the use of advanced analytical techniques against a very large heterogeneous datasets that include structured, semi-structured and unstructured data from different sources. The larger the quantity of data by itself is not advantageous unless analyzed to produce valuable information. This deluge amount of data creates an operational risk in which, the risks arise from storage devices, security of tools or the technologies used to analyze the data. In this paper, we perform a systematic literature review to give comprehensive review of security challenges and risks related to big data analytics. Security mechanisms such as cryptographic and non-cryptographic techniques are used to secure big data during analytics. The security of big data at rest and in transit gets enough investigation while a few researches had done at securing data at processing stage. Even though a number of possible techniques were proposed for big data security, it still suffers performance issues. This article is trying to explor security issues that used for preserving the Confidentiality, Integrity and Availability (CIA triad), non-repudiation as well as Access control in the context of big data analytics. Finally, we identify open future research directions for security of big data analytics. This paper also can serve as a good reference source for the development of modern security-preserving techniques to address various challenges of big data analytics security and privacy-issues.
