Using Machine Learning for Security Issues in Cognitive IoT

Cognitive learning is progressively prospering in the field of Internet of Things (IoT). With the advancement in IoT, data generation rate has also increased, whereas issues like performance, attacks on the data, security of the data, and inadequate data resources are yet to be resolved. Recent studies are mostly focusing on the security of the data which can be handled by machine learning. Security and privacy of devices intrusion detection their success in achieving classification accuracy, machine deep learning with intrusion detection systems have greatly increased popularity. However, the need to store communication centralized server compromise privacy and security. Contrast, Federated Learning (FL) fits appropriately as a privacy-preserving decentralized learning technique that trains locally transfer the parameters the centralized instead of purpose current research provide thorough and application FL intrusion detection systems. Machine Learning (ML) and Deep Learning (DL) approaches, which may embed intelligence in IoT devices and networks, can help to overcome a variety of security challenges. The research includes a detailed overview of the application of FL in several anomaly detection domains. In addition, it increases understanding of ML and its application to the field of the Cognitive Internet of Things (CIoT). This endeavour also includes something crucial . The relevant FL implementation issues are also noted, revealing potential areas for further research. The researcher emphasised the flaws in current security remedies, which call for ML and DL methods. The report goes into great detail on how ML and DL are now being utilised to help handle various security issues that IoT networks are facing. Random Neural Networks that have been trained using data retrieved by Cognitive Packets make the routing decisions. A number of potential future directions for ML and DL-based IoT security research are also included in the study. The report concludes by outlining workable responses to the problem. The paper closes by offering a beginning point for future study, describing workable answers to the problem of FL-based intrusion detection system implementation

Customer Churn Prediction Model Using Artificial Neural Networks (ANN): A Case Study in Banking

Customer Churn has a great impact on banking industries as it accelerates a loss of revenue and customer loyalty. The focus of the research is to create a model for the banking sector using Artificial Neural Networks (ANNs) which can predict if the customer will churn. The prediction is based on the input features and the independent variable of the trained dataset. The hyperparameters are altered during model training using the forward propagation algorithm and cross-validation techniques which enable the model to perform well with respect to accuracy and precision rate. The achieved results illustrate that the suggested model has an accuracy of 86% at predicting customer attrition. In comparison to the logistic regression model outcomes, ANN models are more effective for predicting customer churn in the banking industry. The study suggests vital perceptions of how to employ machine learning approaches to increase client retention and decrease customer churn. Banks can use this model to spot clients who are at risk of churning and take proactive measures to keep them

Road Deterioration detection A Machine Learning-Based System for Automated Pavement Crack Identification and Analysis

Road surfaces may deteriorate over time because of a number of external factors such as heavy traffic, unfavourable weather, and poor design. These flaws, which may include potholes, fissures, and uneven surfaces, can pose significant safety threats to both vehicles and pedestrians. This research aims to develop and evaluate an automated system for detecting and analyzing cracks in pavements based on machine learning. The research explores the utilisation of object detection techniques to identify and categorize different types of pavement cracks. Additionally, the proposed work investigates several approaches to integrate the outcome system with existing pavement management systems to enhance road maintenance and sustainability. The research focuses on identifying reliable data sources, creating accurate and effective object detection algorithms for pavement crack detection, classifying various types of cracks, and assessing their severity and extent. The research objectives include gathering reliable datasets, developing a precise and effective object detection algorithm, classifying different types of pavement cracks, and determining the severity and extent of the cracks. The study collected pavement crack images from various sources, including publicly available databases and images captured using mobile devices. Multiple object detection models, such as YOLOv5, YOLOv8, and CenterNet were trained and tested using the collected dataset. The proposed approaches were evaluated using different performance metrics, The achieved results indicated that the YOLOv5 model outperformed CenterNet by a significant margin

Neoj4 and SARMIX Model for Optimizing Product Placement and Predicting the Shortest Shopping Path

Product placement of top-selling items in highly visible aisles inside supermarkets plays a crucial role in enhancing customer shopping experience. Moreover, it is important for retailers to assure that their customers can effortlessly navigate the store and locate the items they are searching for in a timely manner. The research proposes a novel and effective approach that combines two methods; the SARIMAX model for forecasting sales of each product based on historical data; by using the predicted result, placing the most demanding item in highly visible aisles. And the use of Graph Database Management Systems (GDBMS) such as Neo4j to find the shortest path for consumers to navigate throughout the store to finish the shopping as per their shopping list. By leveraging the power of data analytics and machine learning, retailers can make data-driven decisions that result in improved sales andcustomer satisfaction. Retailers investing in these technologies and strategies will likely see a significant increase in customer satisfaction and sales

Scalable Machine Learning Model for Highway CCTV Feed Real-Time Car Accident and Damage Detection

This study investigates the potential advantages of employing computer vision algorithms to enhance real-time accident detection and response on highways using CCTV feed. Traditional techniques rely on retrospective data, which can decrease response times and precision. Computer vision algorithms have the potential to enhance detection speed and precision, resulting in quicker emergency response and monitoring of traffic flow. The primary objective of this study is to identify the advantages of utilising computer vision algorithms and the data gathered through them to enhance road safety measures and reduce the occurrence of accidents. This study is anticipated to result in quicker emergency response times, the identification of areas where statistically more accidents are likely to occur, and the use of collected data for research purposes, which can lead to enhanced road safety measures. Using computer vision algorithms for accident detection and response has the potential to reduce the human and monetary costs associated with traffic accidents

Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

This paper examines deep learning models for accurate and efficient identification and classification of pavement distresses. In it, a variety of related studies conducted on the topic as well as the various identification and classification methods proposed, such as edge detection, machine learning classification informed by statistical feature extraction, artificial neural networks, and real-time object detection systems, are discussed. The study investigates the effect of image processing techniques such as grayscaling, background subtraction, and image resizing on the performance and generalizability of the models. Using convolutional neural networks (CNN) architectures, this paper proposes a model that correctly classifies images into five pavement distress categories, namely fatigue (or alligator), longitudinal, transverse, patches, and craters, with an accuracy rate of 90.4% and a recall rate of 90.1%. The model is contrasted to a current state-of-the-art model based on the You Only Look Once framework as well as a baseline CNN model to demonstrate the impact of the image processing and architecture building techniques discussed on performance. The findings of this paper contribute to the fields of computer vision and infrastructure monitoring by demonstrating the efficacy of convolutional neural networks (CNNs) in image classification and the viability of using CNNbased models to automate pavement condition monitoring

Solution of IoT Security and Privacy Challenges A Systematic Literature Review

Technology has changed the way people live and work by promoting the way to complete day-to-day tasks and activities in a sufficient and significant way by doing mo re for doing less. Data, information, and knowledge can be saved and organized to be easier and more meaningful to retrieve and reuse. However, using technology is usually combined with raising security breaches and privacy concerns. The Internet of Things (IoT) is the internet kind of everything where an interconnected variety of physical devices are connected to one another the internet and are equipped to share and take decisions without human interaction. Such interconnected physical devices may shape a smart house in which all its devices are connected and data among them are shared. Different approaches are therefore provided to implement and run IoT applications whether in smart houses or any other form. However, connected devices raise a security concern and are usually associated with different privacy breaches. Therefore, taking into consideration these concerns may open the door to learning more about the models, frameworks, and approaches of such attacks and the developed solutions to combat those illegal behaviors. The main aim of this study is to review the literature to gather IoT security issues and to understand and differentiate them in order to allocate the developed solution to one another. Several scientific articles will be included and analyzed to identify IoT security issues, solutions trends, and perspectives. Data will be therefore collected, extracted, and synthesized to reach a consensus about security breaches and effective frameworks for potential solutions. The main contribution of this study is to overview the current evidence-based practices of security issues and state-ofthe-art technologies, simulators, modelers, and tools to resolve security and privacy issues

An Effective Galaxy Classification Using Fractal Analysis and Neural Network

Astronomy is always in a quest of revealing the mysteries of our Universe. There is a vast amount of astronomical data collected and this information comes from stars, galaxies and other celestial objects. While exploring this type of astronomical data, we can identify some complex selfsimilar patterns. Such self-similar patterns are shown in our own galaxy and are called fractals. This research work has been developed for finding such self-similarity that can be measured from galaxy clusters and this feature can be learned through a suitable neural network. This research work gives an insight about calculating the fractal dimension of galaxy images using a box counting algorithm and training the images using LeNet - 5. The box counting fractal dimension is a specified range of values for each particular class of galaxy. By using the fractal dimension as a primary feature of different classes of galaxy and with the help of LeNet-5 network model classifying the galaxy images into ten specified classes according to its morphological properties. The model produced an accuracy of 74% when implemented with the baseline algorithm. When implemented with LeNet- 5 it produced an accuracy of 96%. The precision recall and f1-Score value of the LeNet-5 model was also calculated. The precision recall and f1-Score value for class 1, class 2, class 4 and class 6 were higher than those of the other classes

Securing IoT Devices Against Emerging Security Threats: Challenges and Mitigation Techniques

The increasing prevalence of IoT devices has brought about numerous security challenges due to their relatively simple internal architecture and lowpowered hardware warranted by their small footprint requirement. As there are billions of IoT devices in use today, the sheer number of such devices pose a great security challenge as they are often constrained by a number of hardware and software limitations in addition to being designed with a focus on convenience, ease of use, mass production, and low cost, rather than security. The seemingly exponentially increasing number of such devices make it harder to keep track of — and patch — insecure IoT devices. This paper explores the common security threats, attacks, and vulnerabilities relating to IoT devices and highlights the challenges associated with securing them against emerging security threats and cyberattacks. Due to their role as gateways to connected devices and susceptibility to forming botnets or facilitating man-in-the-middle attacks, IoT devices are a lucrative target for cybercriminals. The paper discusses various remediation and mitigation techniques that can be implemented to better secure IoT devices, including access control mechanisms, secure communication protocols, and regular updates and patches. By better understanding the security challenges associated with IoT devices and implementing effective mitigation techniques, individuals and businesses can ensure the safety, security, and privacy of their connected devices and networks