Healthcare, which raises several challenges regarding security and privacy, is the largest sector of society dealing with patients’ health data. Numerous approaches have been developed regarding healthcare; however, these approaches lead to high response time and leakage of transaction privacy. Thus, this work proposes PDHSMICK, a partial key-based secure EHRs with distributed cloud for Healthcare Systems applying Mimetic Interpolation-based CRYSTALS-Kyber (MI-CRYSTAL-Kyber). Primarily, the patient and doctor register with the system. After that, the patient logs in and books an appointment with the doctor. Then, consultation is done, and their details are sent to the Third-Party Auditor (TPA), where they store the information in the log file. Afterwards, the patient verifies the log file and then uploads the information to the concerned Cloud Server (CS). During uploading, the files are split, and the attributes from the files and Distributed Cloud Server (DCS) are extracted. Then, by utilizing Interpolation-based Pearson Correlation Coefficient (I-PCC), a particular DCS is selected. After that, for authorization purposes, a hash code and digital signature are generated by utilizing Exponential based Extensible Output Functions (EX-EOF) and Double Mod-Digital Signature Algorithm (DM-DSA). Concurrently, the files are encrypted by employing MI-CRYSTAL-Kyber techniques. Lastly, the doctor logs on to the system, and hash code and digital signature verification are performed. The system allows the doctor to download the data if the verification matches. Also, the experimental assessment was validated, which shows the proposed technique’s efficacy.

“Vehicular Ad-hoc Network (VANET)” has emerged as a hotbed of research and development. This technology has been utilized to improve vehicle and road safety by transmitting information about traffic congestion and road conditions during emergencies in order to soothe drivers and passengers. However, such environments are more vulnerable to security risks. Thus, the primary challenge in such an environment is to provide authentication services while maintaining user confidentiality. To that end, a trustworthy authority (TA) is established, which offers services to users as well as authentication of communications transmitted between trusted authorities and VANET nodes. A trusted authority (TA) is established using VANETs to supply clients with a range of online premium services. As a result, preserving the confidentiality and authenticity of messages conducted between the TA and VANET nodes is crucial. Our proposed method minimizes recognition latency by up to 85% when compared to On-site Driver ID, and decreases authentication latency by up to 94%, respectively.

The integration of Artificial Intelligence (AI) into the space of software engineering marks a transformative period that reshapes traditional development processes and propels the industry into a new era of innovation. This exploration delves into the multifaceted impact of AI, from its roots in early symbolic AI to the contemporary dominance of machine learning and deep learning. AI’s applications span various domains, but its significance in software engineering lies in its ability to enhance efficiency, improve software quality, and introduce novel approaches to problem-solving. From automating routine tasks to streamlining complex development workflows, AI acts as a virtual collaborator, allowing human developers to focus on higher-order thinking and creativity. This study introduces the application of AI in software engineering. reverse-engineering, and development environments. Moreover, ethical considerations, challenges, and future trends, including explainable AI, reinforcement learning, and human-AI collaboration, are presented.

An extensive number of instruments and systems assessment tools are weak and not good enough in the appraisal of systems’ quality engineer success measurement. Thus, the comprehension of systems’ success is very serious. One of the purposes of this research topic is to develop a successful, novel, validated instrument for measuring system quality success based on the integration between theories of information systems (Seddon and DeLone & McLean) and software engineering theory (ISO 25010). To ensure the quality of the instrument before use, eight academic experts have validated. The reason for expanding the number of experts to eight is to accurately build and evaluate the instrument because this instrument is the first one erring. After expert validation done successfully, researchers started the process of instrument pre-test. Pre-test verification and validation results done by test the instrument of 74 users. The results of the statistical testing were perfect. The Composite reliability proposed value is 0.7, The average variance extracted value is 0.5. Cronbach’s alpha is higher than 0.7. The value of Spearman’s reliable rhea is >0.6. Results approved that this instrument is strong and perfect to be used as a valid tool for system success measurement.

Software security is of great concern as computers have entered almost all walks of life and people at large have become dependent on technology for not only entertainment and communication but for performing tasks involving money and a lot of stake. Software security not only involves securing the software but also user data and communication media. This paper states the several types of security threats that exist since the time networking has evolved, namely, malware, Trojans, viruses, denial of service attacks, and many more. This paper reviews several measures to address these threats. It includes logging, anti-malware, network security methods, and encryption methods. It has been identified that a lot of work has been done to deal with security threats, and it is not only limited to the protection of software but also extends to the protection of data and networks. The existing methods make extensive use of artificial intelligence, and it is identified that there is a need to develop a model that is able to identify known as well as unknown threats. There is a huge scope for research in this area.

This research’s main objective is to study and evaluate the detection and removal of undesired shadows from still images since these shadows might mask important information caused by light sources and other obstructions. A variety of methods for detecting and eliminating shadows as well as object tracking approaches based on movement estimation and identification are investigated. This includes shadow removal methods like background subtraction, which are intended to improve obstacle recognition of the source item and increase the accuracy of shadow removal from objects. When new items enter the frame, they are first distinguished from the background using a reference frame. The tracking procedure is made more difficult by the merging of the shadow with the foreground object. The approach highlights the difficulties in object detection owing to frequent occurrences of obstacles by using morphological procedures for shadow identification and removal. The proposed approach uses feature extraction is also discussed, highlighting its importance in image processing research and the use of suggested methods to get over obstacles in image sequences. The proposed method for shadow identification and removal offers a novel approach to improve image processing when dealing with still images. The purpose of this technique is to better detect and remove shadows from images, which will increase the precision of object tracking and detection. Depending on the type of images being processed, the process begins with initializing a background model, which is based on a static image background.