This study explores how Deep Q Networks (DQNS) can be applied to classifying data with standard datasets. Though DQNs are designed for sequential decisions, this study uses the Iris and Diabetes datasets, which are both used for classification, to test the performance. It investigates DQNs with different data imbalances and compares their results based on the quantity of data. Models are assessed using accuracy, precision, recall, F1 score, ROC-AUC, the amount of memory required, and their training speed. As demonstrated by the results, DQNs can match other algorithms in accuracy while attaining accuracy rates between 84% and 95% for various datasets and modified configurations. Based on the study, performance can be affected by adjusting hyperparameters and the distribution of data classes. DQNs are more complex than common classifiers such as SVMs and decision trees, and their main contribution in simple classification is yet to be proven. It adds to the enthusiasm for using reinforcement learning models in the context of supervised learning. The paper highlights the value of correct evaluation, points out risks linked to model over-fitting and includes new areas to pursue in the future such as benchmarking, clarifying models and using hybrid systems.

The increasing energy demands, depletion of traditional energy sources, and significant environmental changes have necessitated the search for alternatives to petroleum fuels. Among the available alternatives, straight vegetable oil (SVO) is a viable option due to its properties being similar to fossil diesel (FD). This study shows that the viscosity of straight Karanja oil significantly decreases and aligns with FD when heated to temperatures between 105°C and 110°C. The viscosity of Karanja oil was reduced using a specially developed coiled-type heat exchanger to recover waste heat from engine exhaust flue gas. A compression ignition (CI) engine was operated at a constant speed (1,500 rpm) under varying loads from 10% to 100% of engine rated capacity in 10% increments. While FD exhibited superior performance due to its lower viscosity, heating Karanja oil to 105°C reduced its viscosity, enhancing engine performance However, the brake thermal efficiency (BTE) was poor and brake-specific fuel consumption was higher when using heated Karanja oil compared to FD. Preheated straight Karanja oil (PSKO) showed better performance as compared to unheated straight Karanja oil (USKO). The highest BTE for all tested fuels was recorded at 80% of the engine’s rated load. Although NOx concentration was lower in USKO than FD, however, when PSKO was used, NOx emissions started increasing while CO emissions were decreased. the best diesel engine performance and the lowest emission levels were achieved with Karanja oil heated at 105°C.

Interference screws are widely used for soft tissue-to-bone or bone-to-bone graft fixation, with the choice of material being crucial for successful outcomes. This study compares the performance of interference screws made of titanium, polyetheretherketone (PEEK), and poly-L-lactic acid (PLLA) under torsional and tensile loads using a finite element model. The mechanical results showed that the mean value of the moment to failure was 15.06 Nm for titanium, 1.54 Nm for PEEK, and 0.797 Nm for PLLA. The mean load to failure of the interference screw was 6493.13 for titanium, 640.71 for PEEK, and 31.76 Nm for PLLA. The titanium exhibits the highest moment and load to failure under torsional and tensile loads. PLLA exhibits the lowest and PEEK exhibits the intermediate results. PLLA exhibits less deformation under tensile and torsional load, which makes it suitable for load-bearing applications.

Ransomware-as-a-Service (RAAS), a new cybercriminal actor, is making ransomware attacks more potent and widespread. This research comprehensively assesses Ransomware-as-a-Service (RAAS) ecosystem phishing detection and prevention solutions. Seven studies compare RAAS-enabled phishing detection and prevention effectiveness, challenges, and trends. The findings recommend a multi-layered, context-aware approach for organizational resilience to shifting cyber threats. This thorough phishing attack detection and security study examines ransomware-as-a-service. Phishing attacks leverage human weaknesses to steal sensitive data and are becoming more sophisticated. Since RAAS makes ransomware attacks easier, even non-technical people may launch deadly ones. Money is making ransomware assaults more common and severe, putting people, organizations, and key infrastructure at risk. These new attacks must be detected and mitigated to safeguard digital assets. This study compares RAAS ecosystem phishing attack defence detection and mitigation technologies to identify strengths, weaknesses, and emerging trends.

The Internet of Vehicles (IoV) is becoming an interesting topic among researchers and it has emerged as a rapidly advancing field within Vehicular Ad-hoc Networks, facilitating intelligent communication between vehicles and the cloud through the integration of Internet of Things (IoT) technologies. The IoV surroundings face serious challenges due to the highly interrelated nature of vehicles and infrastructure in certifying privacy and security. Traditional approaches to authentication lack the strength required to protect against developing fears, leaving systems vulnerable to attacks. This survey addresses the gap by employing formal analysis approaches to prove authentication protocols, targeting to reinforce safety and confidentiality in IoV systems. The IoV communication model consists of Vehicle-to-Vehicle, Vehicle-to-Infrastructure, Vehicle-to-Personal Devices, and Vehicle-to-Cloud. Smart automobiles are equipped with cameras, radars, on-board units, and sensors to help reduce the number of accidents by giving drivers or autonomous vehicles up-to-date information on roads, traffic signals, and other pertinent entities. As human lives are at risk, security and privacy in the IoV communication paradigm are critical and cannot neglected. Security and privacy breaches may cause accidents because the attacker can inject false information into the system as the communication channel is open and unsecured. The researchers proposed many authentication protocols to provide secure communication between IoV entities. Although surveys on IoV security and privacy issues deal with communication and computation costs, they lack formal analysis of the authentication protocols. This survey reviews the informal analysis and formal analysis methods used by various authentication protocols. Furthermore, the challenges and future work are also included in this survey.