Wasit Journal of Computer and Mathematics Science

There is no doubting that contemporary technology creates new hazards, and these threats are many and significant, directly harming people's lives and threatening their stability. Because of the increased use of computers and Internet-connected cellphones in recent years, the problem of cybercrime has expanded substantially. Unquestionably, this kind of crime is now a reality that jeopardizes people's reputations and lives, therefore we must be aware of it to prevent being a victim. The exponential growth in internet connectedness is closely tied to a rise in cyberattack incidences, frequently with significant consequences. Malware is the weapon of choice for carrying out malicious intent in cyberspace, whether by exploiting pre-existing flaws or exploiting the unique properties of new technology. There is an urgent need in the cybersecurity area to develop more inventive and effective virus defense techniques. To do this, we first give an overview of the most often exploited vulnerabilities in the current hardware, software, and network layers. This follows criticism of the most recent mitigation efforts and the reasons why they may or may not be helpful. Following that, We'll talk about new attack methods for cutting-edge technologies including social networking, cloud computing, mobile technology, as well as critical infrastructure. We conclude by sharing our speculative findings on potential future research avenues.

Vehicular Ad hok Networks (VANET) is an emerging technology with both a bright future and significant concerns, particularly in terms of security. This study focuses on three-part VANET security frameworks. The first gives a thorough review of the needs, difficulties, and characteristics of VANET security. In order to create a secure VANET architecture with effective party communication, certain needs should be taken into account. We provide information on current security designs as well as widely used security standard protocols. The second concentrates on a brand-new categorization of the various assaults described in the VANET literature and the remedies that go with them. The third compares a few of these options using established security standards for VANET. Then, in order to assist researchers for future usage, we call attention to many outstanding topics and technological obstacles linked to VANET security

In the recent decade, global community has been one of the world's most prominent targets of terrorist strikes. There is still study being done and improved efficiency procedures needed in this sector, however the security forces and law enforcement agencies are creating and launching high security weaponry to secure their territories. There is a huge necessity to include the greater level of protection into military vehicles in the wake of several terrorist attacks and sniffing of security officers. Terrorists rely heavily on infiltration while avoiding detection by security measures because of the holes in these hypothetical situations. This paper presents the central methods of hash functions, cryptography and dynamic encryption that may be utilised by the military personnel to increase the safety, privacy, and resistance to sniffing of their communications with one another. This article details the many methods and algorithms that may be included into laser guided defensive weapons and vehicles to provide safe communication across the system.

Digital watermarking technology is increasingly used to protect copyright and demonstrate ownership of digital multimedia (such as text, music, photos, and videos). In order to safeguard intellectual property rights and rules of ownership for multimedia, this project suggests a text watermark algorithm. The process of hiding little text or grayscale images is the main focus, though. The masking of a watermark text in a high-color or high-density area of the block picture has been proposed using an embedding technique based on an energy function and the Least Significant Bits (LSB) method. Even with various format types and picture sizes chosen to conceal and cover a changing message size, the quality results demonstrate that the watermark image suffers from less distortion than the cover image, and the suggested algorithm is powerful to conceal a random watermark text even with smaller block sizes. An additional optional choice to encrypt the text watermark before embedding is also recommended because doing so would make it harder for hackers to read the text. This text can be encrypted using the Caesar cipher method before embedding is implemented in images.The experimental results of using the suggested algorithm for embedding and extracting watermark text for various sizes in a large number of images were satisfactory, giving a level of peak signal-to-noise ratio (PSNR) and Signal to Noise Ratio (SNR) with low mean square error (MSE) values. However, PSNR degrades more quickly than LBS as the watermark text size increases, so it was determined that it is more suitable for applying a watermark rather than a stego It is employed in order to share information securely

Digital marketing is the marketing component used for the promotion of products and services through Internet and online-based digital technologies like desktops, mobile phones and other digital media and platforms. The manner in which brands and corporations used technologies for marketing has evolved in the 1990s and 2000s. As digital platforms have been more integrated into everyday marketing plans and more people are using digital devices instead of visiting physical shops, digital marketing campaigns have become prevalent, with combinations of Search Engine Optimisation (SEO), search engine marketing (SEM) and content marketing as well as influence marketing. Non-Internet digital marketing includes non-internet channels, such as TV, SMS and MMS, callback and hold-tones for the mobile ring. Digital marketing differs from online marketing through an extension to non-Internet channels. In the coming years, the U.S. Accounted for Over 27 percent of global market, while China accounted for a 13.9 percent growth. The U.S. digital advertisement and marketing industry is expected to hit US$87.1 billion by 2020. Canada actually accounts for 26.99 percent of the global economy. China, the second largest economy in the world, will register a CAGR of 13.9 percent and hit an expected market value of US$139.3 billion by 2027. The other prominent markets in terms of growth are Canada and Japan, both expected to rise by 6.9% and 8.9% respectively. Europe will increase at 7.9 percent CAGR and US$135.5 Billion in the industry by the year 2027. The work integrates the association factors and international reports analytics on the economical perspectives with Internet marketing on assorted aspects.

E-learning is the acquiring of information that takes place through electronic devices and media, also known as online learning or electronic learning. E-learning can be understood as "learning is enabled electronically". Typically, online learning is conducted online. E-Learning typically occurs in the form of online courses, online degrees or online programmes. Online learning has many benefits over in person classes. These include students being able to undertake and study using self-paced learning and the opportunity to choose their study environment. This leads to low expenditure, low staff training, and low capital outlay. All these factors prove that e-learning will perform well in terms of business growth and development. Over the long-term, worldwide e-learning market is expected to grow significantly from $107 Billion in 2015 to $325 Billion in 2025. E-learning forces learners to concede some of their privileges. Higher cheating rate, peer pressure, and lack of skill development for online students are some challenges that need to be dealt with. This work integrates the usage patterns and reviews on the quality factors of e-learning based course materials for effectual outcomes in the academia

Intrusion detection corresponds to a suite of techniques that are used to identify attacks against computers and network infrastructures. As the cost of the information processing and Internet accessibility falls, more and more organizations are becoming vulnerable to a wide variety of cyber threats. Web mining based intrusion detection techniques generally fall into one of two categories; misuse detection and anomaly detection. In misuse detection, each instance in a data set is labelled as ‘normal’ or ‘intrusive’ and a learning algorithm is trained over the labelled data. These techniques are able to automatically retrain intrusion detection models on different input data that include new types of attacks, as long as they have been labelled appropriately. Evaluation results show that the proposed approach can reduce the number of alerts by 94.32%, effectively improving alert management process. Because of the use of ensemble approach and optimal algorithms in the proposed approach, it can inform network security specialist the state of the monitored network in an online manner.

This paper presents a useful biological approach for hand geometrybased recognition systems. Measurable hand geometry such as width, length, and finger area, were used to generate feature vectors. As useful properties, thirtyfive hand-shaped geometry scales are used. Artificial neural networks are used as distinct classifiers. The experimental result of all dataset reaches to the
performance of 98.30% as recognition rate

With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this search is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behavior with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioral scores are analyzed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring.

Prediction of software defects is one of the most active study fields in software engineering today. Using a defect prediction model, a list of code prone to defects may be compiled. Using a defect prediction model, software may be made more reliable by identifying and discovering faults before or during the software enhancement process. Defect prediction will play an increasingly important role in the design process as the scope of software projects grows. Bugs or the number of bugs used to measure the performance of a defect prediction procedure are referred to as "bugs" in this context. Defect prediction models can incorporate a wide range of metrics, including source code and process measurements. Defects are determined using a variety of models. Using machine learning, the defect prediction model may be developed. Machine inclining in the second and third levels is dependent on the preparation and assessment of data (to break down model execution). Defect prediction models typically use 90 percent preparation information and 10 percent testing information. Improve prediction performance with the use of dynamic/semi-directed taking in, a machine learning approach. So that the results and conclusion may be sharply defined under many circumstances and factors, it is possible to establish a recreated domain to house the entire method. Computer-aided engineering (CAE) is being used to identify software defects in the context of neural networks. Neural network-based software fault prediction is compared to fuzzy logic fundamental results in this research paper. On numerous parameters, neural network training provides better and more effective outcomes, according to the recommended findings and outputs.

Today’s the developing world shows various adventures in every field. In each field the small requirements are very essential to develop big calculations. By using different sources, we can modify it as our requirements and implement in various field. In earlier days the measurements are generally occur through measuring devices. But now a day’s digitalization as is on height. Therefore, we use a proper display unit for measurement of distance. We can use sources such as sound waves which are known as ultrasonic waves using ultrasonic sensors and convert this sound wave for the measurement of various units such as distance, speed. This technique of distance measurement using ultrasonic in air includes continuous pulse echo method, a burst of pulse is sent for transmission medium and is reflected by an object kept at specific distance. The time taken for the sound wave to propagate from transmitter to receiver is proportional to the distance of the object. In this distance measurement system, we had ultrasonic sensor HC-SR04 interfaced with arduino Uno R3. Programming and hardware part of ultrasonic sensor interfacing with arduino Uno R3.

Smart Health Costing provides the benefits of streamlined operations, improved management and control, superior patient care, strict cost control and improved productivity. HMS is powerful, flexible and easy to use and has been designed and developed to deliver real, conceivable benefits to hospitals. Most importantly, it is backed by reliable and dependable support. The Hospital Management System project is based on database, oriented and networking techniques. Because there are many areas where we keep records in the database we use SQL SERVER which is one of the best and easiest software to keep our information. This project uses C# as a front-end program which is object oriented programming and has a connection to SQL SERVER. The smart health costing system is specifically designed to meet the specific requirements of medium and large sized hospitals around the world. All required modules and features are specifically designed to suit your requirements. It covers all required modules right from patient registration, medicine details, doctor, wards, administrator, patient appointment, bill payment, discharge details etc. The human body is a very complex and developed structure consisting of millions of tasks. Man has understood all these complex functions, part by part from their research and experiments. With the advancement of science and technology, medicine has become an integral part of research. Gradually, medical sciences became a completely new branch of science. As of today, the health sector consists of medical institutions, such as hospitals, hospitals, etc., research and development institutions and medical colleges. Thus the health sector aims to provide the best medical facilities to the general public.

In this paper, we introduce the concept of SPG*-closed mapping and continuous mapping among which SPG-closed mappings, SPG*-closed mappings and SPG**-closed mappings and the relationship between them, and also SPG-continuous mappings, SPG*-continuous mappings and SPG**-continuous mappings and the relationship between them. Among the result we obtain are the concepts of SPG**-closed mappings and closed mappings are independent also PG**-continuous mappings and continuous mapping are independent.