The rampant issue of vehicle theft on a global scale demands more effective security solutions. Traditional methods like keys and alarms have shown limitations in deterring theft. This paper presents a novel approach utilizing face recognition technology to authenticate authorized users of vehicles. By employing a Raspberry Pi circuit and a camera, the system captures and registers the owner’s facial features. Once registered, only authorized individuals can initiate the vehicle’s operation. In the event an unauthorized user attempts to access the vehicle, the system conducts a facial scan and verifies it against the registered profile. If a match is not found, access is denied, and an alarm is triggered.

The idea of separating the management plane from the data-forwarding gear was first introduced by Software-Defined Networking (SDN), which has brought about a revolutionary new age in networking. This innovation makes networks programmable, flexible, and dynamically reconfigurable. These characteristics have great potential for the field of computer science in the cloud, where dynamic adjustments and reconfiguration are essential owing to on-demand consumption patterns. The many simulation and empirical assessment techniques that have been created expressly for SDN-enabled cloud settings are also highlighted by us. Finally, study conducted perform a critical review of the state of the research, highlighting gaps and suggesting possibilities for further study.

The rapid growth of electronic commerce (e-commerce) has revolutionized the way Small and Medium-sized Enterprises (SMEs) conduct business, enabling them to reach global markets and unlock new growth opportunities. However, this digital transformation has also exposed SMEs to an escalating array of cybersecurity threats that could compromise their data, financial integrity, and reputation. Blockchain technology, known for its decentralized and immutable nature, has emerged as a potential solution to enhance the security and resilience of e-commerce operations for SMEs. This research paper aims to investigate the current state of cybersecurity preparedness among SMEs engaged in e-commerce and identify the prevalent threats they face in the digital landscape. To achieve this goal, the study incorporates systematic literature review (SLR) method to examine the best technological solution to address the cybersecurity issues faced by e-commerce organizations in this paper of SMEs operating in e-commerce to gather data on their security practices, incident history, and perceptions of cybersecurity risks. In the context of this research, blockchain technology will be explored as a potential mechanism to enhance the security of e-commerce operations for SMEs. The inherent features of blockchain, such as decentralization, transparency, and immutability, could help protect sensitive data and thwart cyberattacks. By exploring the integration of blockchain into e-commerce systems, the research aims to shed light on the potential benefits and challenges associated with its adoption by SMEs. The research findings will not only shed light on the prevailing cybersecurity practices within SMEs and highlight their vulnerabilities but will also explore how blockchain technology can be leveraged to address these challenges. By understanding these patterns and exploring the feasibility of blockchain integration, the research aims to provide comprehensive recommendations for enhancing cybersecurity resilience among SMEs in e-commerce. This could include proposing tailored cybersecurity training programs, suggesting cost-effective security solutions leveraging blockchain technology, and advocating for regulatory support to safeguard SMEs from cyberattacks. The significance of this study lies in its potential to contribute to the existing body of knowledge on SMEs' cybersecurity landscape in the e-commerce domain, addressing a critical research gap in understanding and addressing the unique challenges faced by these businesses. As SMEs continue to drive economic growth and job creation in many economies, safeguarding their digital assets and ensuring the integrity of e-commerce transactions become imperative for sustainable economic development in the digital era. The exploration of blockchain technology in this context could pave the way for more secure and resilient e-commerce ecosystems, benefiting SMEs and the broader digital economy alike.

As the global e-commerce landscape continues to expand rapidly, the efficiency and security of supply chains have become critical areas of concern. With the increasing reliance on digital platforms and the interconnected nature of supply chains, the vulnerability to cyber threats has escalated significantly. This research paper delves into the multifaceted domain of e-commerce supply chains with a primary focus on cyber-security considerations. The objective of this study is to assess the existing cyber-security measures and vulnerabilities within e-commerce supply chains while proposing effective strategies to mitigate potential risks. Through an extensive literature review, the paper highlights various cyber-security challenges faced by e-commerce enterprises, including data breaches, ransomware attacks, and supply chain disruptions caused by cyber-attacks on logistics partners. By analyzing real-world case studies and industry reports, the research provides valuable insights into the consequences of cyber-security lapses and their impact on supply chain resilience and consumer trust. The research also explores the technological advancements and best practices that can fortify e-commerce supply chains against cyber threats. It examines the role of encryption, multi-factor authentication, secure payment gateways, and cloud-based security solutions in safeguarding sensitive data and maintaining the integrity of supply chain operations. Additionally, the study investigates the importance of employee training and awareness programs to cultivate a cyber-security-oriented organizational culture. Furthermore, this paper considers the legal and regulatory aspects of cyber-security in the context of e-commerce supply chains. By examining international laws, industry standards, and compliance requirements, it highlights the responsibilities and obligations of e-commerce businesses to protect customer data and secure the flow of goods throughout the supply chain. The efficiency and security of supply chains in the expanding global e-commerce landscape have become critical areas of concern due to the escalating vulnerability to cyber threats. This research paper aims to assess existing cyber-security measures and vulnerabilities within e-commerce supply chains and propose effective strategies to mitigate potential risks. By promoting a proactive cyber-security approach, e-commerce enterprises can not only safeguard their operations and customer data but also build a robust reputation in the marketplace, ultimately fostering sustainable growth in the dynamic digital economy.

study conducts a thorough assessment of ensemble machine learning methods, specifically focusing on the identification of Assamese words. This task is crucial for improving Content-Based Image Retrieval systems and safeguarding the digital heritage of Assamese culture. We analyze the efficacy of different algorithms, such as CatBoost, XGBoost, Gradient Boosting, Random Forest, Bagging, AdaBoost, Stacking, and Histogram-Based Gradient Boosting, by thoroughly examining their performance in terms of accuracy, precision, recall, Kappa, F1-score, Matthews Correlation Coefficient, and AUC. The CatBoost algorithm stands out as the top performer, achieving an accuracy rate of 97.7%, precision rate of 95%, and recall rate of 96%. XGBoost is also acknowledged for its substantial effectiveness. This comparative analysis emphasizes CatBoost's superiority in terms of precision and recall. Additionally, it underscores the strong ability of ensemble classifiers to enhance assistive technologies, promote social inclusivity, and seamlessly integrate the Assamese language into technological applications.

In this post, we discuss how to boost quality when faced with high loads and rapid diminishing. In order to improve bandwidth utilization and delay efficiency, a methodology was presented in the Transactions of the IEEE Conference on Connectivity (ICC), that employed either a sensible path or a relayed path depending on rate comparability. Unfortunately, since direct route or intermediary path dependability are less reliable amid dimming circumstances, speed decreases. was demonstrated to outperform due to higher communication stability offered by the secondary relay line in terms of typical capacity and latency metrics. Despite being superior to the direct way, only utilises the broadcast path as a supplementary alternative path, thus it is unable to benefit significantly from the relay method's 4 percentage. The collaborating Proposed method we progress in this editorial, called extremely rapid relay-based conscientious, researchers estimate the transceiver ratio of the starting point, reroute, and citation links and seems to be using broadcaster remains a central to consistently select between the relay path and the successful and prosperous for feedback and control and delay achievements.

The increase in data transmission network speed and reliability is inextricably linked to the fast growth of information technology. This calls for the ongoing improvement and upgrading of current standards. Next-generation communication networks are being developed and deployed with diligence by several organizations. This article provides a thorough examination of the software options available that are designed for analyzing and evaluating data network behaviors. It specifically emphasizes tools with substantial built-in functionality and flexibility that are ideally suited for mobile communication systems. It becomes clear that the NS3 network simulator is the best option. This article provides an overview as well as a unique NS3 solution that makes it possible to estimate the dependability of data transmission from a network node's first movement to its removal from a base station.

Education is developing with the economy. This led to the creation of e-Iearning tools for instructors and students. In e-learning, electronic resources are used to teach. E-learning can be taught in or out of school, but computers and the Internet are essential. That is, e-learning involves delivering education to a big group of people at once or over time. So, overall, in this research is about E-learning that make a successful environment. We chose this term since it is widely used in E-learning research to improve student academic achievement. Due to the difficulties of accessing adequate study materials during the Covid-19 epidemic, distance learning has grown more popular among students. This study proposes a unique learning technique that involves choosing and organizing important learning items using a recommender system. We also studied our method's efficacy. Using a recommended system to help online learning activities was proven to be effective. Software development methodology is a collection of principles and processes used to build software. Systematic software development is the goal. The research approach utilized for this topic is Lean Development Methodology. We also examine the advantages and disadvantages of e-learning. This research also introduces various new strategies and methodologies that may be employed in the future to improve system-learning. It also presents system that we choose and why we choose it. So, it aims to eliminate waste and increase production. Following the principles can help developers avoid nonproductive chores while still producing high-quality results. The ultimate objective is to develop an efficient and error-free system. Finally, E-learning is a social revolution. It's part of a bigger rethinking of how we teach future employees and students. The eLearning platform enabled the learning data visualization dashboard beautifully. User-centered design lets the dashboard adapt to its users' demands.

Magnetic Resonance Imaging (MRI) is a technology mainly used for disease prediction and treatment. Practically, due to poor quality of MRI images, sometimes it is advised to repeat the scan test again which causes some unavoidable situations with increase of costs. Therefore, only the improvement of the quality of MRI images can give us the relief from these unnecessary problems. So, we need an automated supervised machine learning algorithm to generate high resolution data without more efforts. In this paper, some computational techniques like convolutional networks, K-Nearest Neighbor classifier, and Generative Adversarial Network (GAN) are applied on the MRI images to get the high-resolution based MRI images. The methodology follows medical image localization, detection, segmentation, and classification. The validation results on real data of MRI data fundamentally determines its usefulness and demonstrates the effectiveness in compared to state-of-the-art super-resolution techniques.

This study uses rf signals spectrum to calculate the delay of various radio waves. The 5G connection is a cutting-edge networking who responds to requests from two systems within the connection or on a particular vlan with less delay than 3G and 4G. It maintains the crucial benefit of a modern network that gives more storage and quick connections across platforms or networks. The enhanced characteristics of 4G Hetnets are included in the 54 g. In order to deliver the lowest latencies and project scope in todays climate, rf power measuring is essential. The primary goals of measuring rf signals frequency are to ascertain link quality and assess secure data, a second difficulty faced by mobile devices.

Smart wearable technologies utilising devices connected to the web (IoT) are on the rise, and many of these new applications involve the identification of athletic performance. Many people across the world participate in soccer, also called football in some regions. Soccer players practise discrete actions (like shooting and passing) in order to ingrain them in muscle memory and speed up their reflexes during actual games. There is always a compromise between blur and noise when processing images. Denoising naturally softens an image because noise is high-frequency information. Deblurring, on the other hand, causes additional noise in the final product. The need to brighten an image in low-light conditions only adds to the difficulty. Noise is introduced into the image during the brightening process itself. Images taken while moving, especially those of wildlife (though not exclusively), will have more blur than those taken while still. Many previous projects have focused on a single problem, but very few have attempted to address the entire set of problems simultaneously. So, we set out to make a way to turn these lowlight, fuzzy images into high-contrast, clear images. A fuzzy invariant space is the result of the union of several fuzzy invariant spaces. After numerous iterations of processing a blurred image, the final stage is to utilise a progressive restoration procedure. The experimental findings demonstrate the effectiveness of the suggested technique in reducing calculation error, improving the recovery effect, and avoiding the noise caused by numerous deconvolutions. This work introduces new concepts and methods for recognition research by applying fuzzy image processing to the study being human mobility and the detection of activities in the realm of IoT. Using the Kinect, an IoT somatosensory camera, we are able to collect 15 3D skeletal elements via its software development kit (SDK). This led to the study of kinesiology and the creation of a motion resolution model that works well with the Internet of Things.

The rise of digital technology has essentially enhanced the overall communication and data management system, facilitating essential medical care services. Considering this aspect, the healthcare system successfully managed patient requirements through online services and facilitated patient experience. However, the lack of adequate data security and increased digital activities during Covid-19 made the healthcare system a soft target for hackers to gain unauthorized access and steal crucial and sensitive information. Countries such as the UK and the US recently received such challenges, highlighting the need for effective data maintenance. IoT emerged as one of the critical solutions for data management systems in terms of addressing data security which certainly can enhance overall data collection, storage, maintenance, prediction of potential data security breaches and taking appropriate measurements. The concerned research considers a secondary data collection process where necessary data is collected from original scholarly articles, books and journals. Apart from that, a positivism research philosophy, a deductive research approach and a descriptive research design have been considered for this study. Qualitative data analysis techniques have also been incorporated into this research. Upon viewing the pros and cons of IoT algorithms, DES, AES, triple data encryption standards, and RSA encryption can be used in the healthcare system to facilitate data protection.

Beginning in 2020, Covid has increased as a result of a burst put on by a respiratory infection with a substantial peaking fatality rate. The unforeseen occurrence and unchecked global spread of the COVID-19 illness highlight the limitations of current healthcare systems in responding to emergencies affecting public wellness. In these conditions, innovative developments like public blockchain and intelligent systems (AI) have emerged as possible treatments for the covid epidemic. In particular, block chain may help with early identification to combat pandemics. With the measures put in place to prevent infection by wearing masks, social seclusion with a 6m radius, routine testing, and two vaccine doses. This system includes mask measurement, people identification, temp sensors, information tracking, in-person interaction locating, and the current state of a user's medical chart. With the development of technology and increased smartphone usage, illnesses may be tracked and their spread controlled. Considering that the expansion of the business sector's rehabilitation and its continued broad distribution of Covid, it is more crucial to adhere to the instructions to avoid contamination.

The most common technique of analyzing data assembled to draw conclusions concerning the knowledge they contain, a lot of} with the utilization of specialized frameworks and programmes, is data examination. Researchers and professionals often use data analysis techniques and innovations to validate or, on the opposite hand, refute logical models, enabling organizations to form higher business decisions. Information analysis is turning into more and more popular in several fields, together with healthcare. Not solely will illustration play an enormous role in naturally displaying the results of knowledge analysis, however additionally throughout the whole method of collecting, cleaning, Associate in Nursing analyzing, and sharing information. this text outlines an approach for victimization Tableau as a business insight tool to represent and analyses aid data intelligently. Strategies: beginning with making the Tableau Work Space Individual ability 10.3, this analysis illustrates the foremost prevailing model-based technique of comprehending and visualizing Coronavirus data.

Sports stats and analysis has been propelled by sports fans through the ages. It has evolved into a much more complex and technologically advanced system of statistical encapsulation. Due to a plethora of data available and computerization of these data, the demand for sports advice and evaluation has drastically increased. The National Basketball Association (NBA) has acclimatized to these standards. In this study, win-loss percentage for hybrid basketball game scheme was calculated using designed features. The study takes 10 NBA seasons into account ranging from 2010-2021. Using 3 seasons of NBA data was deduced to be the right method. Hence, this can help calculate an accurate model for the season to follow. The final win-loss percentage was predicted by logistic regression, artificial neural networks (ANN).

An effective image classifier built on a convolutional neural network (CNN) is created, and it can analyse training features to detect and identify any object that is considered to be garbage. Additionally, it has the ability to count the quantity of identified items and give each one a price. Any person who brings rubbish to the ATD will have no trouble having it recognised by the image classifier, and they may take advantage of the recycle value that has been assigned to the object. As a result, it is feasible to exchange waste directly for its comparable price, which will encourage people to use the smart dustbin we suggest. By recycling the trash in it, you can offset the cost of installation and reduce the cost of operation and maintenance. The data set was created using photographs of various types of waste without paper cups in addition to images of garbage with paper cups, which is among the most polluting garbage. Convolutional neural networks that have already been trained on Squenzenet, VGG-19, and GoogLeNetwere used to identify paper cups in these photos. The SquenzeNet, Google Net, and Vgg-19 networks utilised in the study were found to have performance rates of 98.79%, 97.46%, and 95.86%, respectively.

The adoption of immersive technologies has altered the traditional shopping experience. We report the results of a study that set out to see how people react to a 17 million artificial intelligence (AI) embedded mixed reality (MR) exhibit in a shopping and entertainment complex that brings together cutting-edge technology and traditional retail. Our research shows that MR enjoyment, spatial immersion, and consumers’ perceptions of unique experiences are all positively impacted by the quality of AI (i.e. voice synthesis and recognition using machine learning) connected with an augmented object. All of these things work together to pique consumers’ interest and elicit favorable reactions, including the desire to make a purchase and the want to tell others about the experience. In conclusion, this research demonstrates that there are new opportunities to increase customer engagement thanks to interactive AI and MR technology.

Since the internet is today regarded as the fastest and most efficient method of transmitting signals, the privacy of signals or info is drastically decreasing. Therefore, it is essential to have a high level of privacy to hide the mysterious data. A common and effective method for protecting output information from unauthorized is encrypted. This method hides hidden data in an image using a cryptographic keys. However, it only provides a single level of protection for the data transmission. This legislation’s goal is to provide two levels of security. The first stage involves hiding information behind certain images using coded words, while the second stage involves encrypting using 2D deterministic algorithms. Employing colonial automata’s 2-D principles, decryption is completed. The surprise data is introduced into the image using the smallest bit (LSB) and Expanded Least Major But (ELSB) techniques in the first level of protection with the help of a secret word. The LSB technique was used in the dim image to encrypt the information contained in the image. In an RGB image, the data was masked using both Sb & ELSB techniques. While a ELSB technique only employs single Rgb aircraft, the LSB approach for RGB photographs uses all three vision plane. On the image in which emission data was then installed using Sb or ELSB techniques, 2D cell enary rules were implemented in the first security level. We used the 2D state machine principles 2, S, 32, and 12S to provide an extra layer of protection to data emission. Cellular Lattice rules provide increasingly complex and reliable information about the symmetric encryption. In this manner, a variety of ideas were used to get a great deal of safety. Unscrambling was done in an unparalleled manner, yet quickly after the demand.