The proliferation of academic credential fraud and the inefficiencies inherent in traditional verification systems have created an urgent need for secure, transparent, and efficient record management solutions in higher education. This comprehensive research examines the effectiveness of blockchain technology in addressing these challenges through a systematic analysis of contemporary implementations, performance metrics, and case studies. Our investigation reveals that blockchain-based systems demonstrate significant improvements in security, verification efficiency, and fraud prevention, with initial title registration averaging 2.97 seconds and verification processes completing in under one second. While challenges in scalability and interoperability persist, the technology shows remarkable potential for transforming academic credentialing, with institutions worldwide implementing blockchain solutions that have issued over 32,000 digital credentials and achieved 100% fraud detection rates.

Cloud security breaches make it unattractive to utilize. Data security in cloud computing is currently an issue. Data that isn’t safeguarded and storage that isn’t secure enough are the major problems of cloud computing. Businesses must carefully evaluate security issues before transitioning to cloud computing. This study’s major goals are to define cloud computing and examine cyber attacks on cloud infrastructure. The elimination of non-technical difficulties may lead to fewer security concerns. Yet, servers and services can be harmed by flooding attacks, DOS and cryptography. This study tries to show the risks connected to cloud computing and data storage. Once the problems with cloud security have been identified, the following stage is to hunt for a fix.

Without utilization of computer and its related technology, modern day’s life cannot be headway. It has also transformed into an incredibly troublesome task. The genuine challenges included are shorter life cycles, cost effective and higher software quality goals. Despite these challenges the software developers have started to give cautious thought on to the procedure to develop software, testing and reliability investigation of software and to reinforce the method. Developer most fundamental decisions related to the perfect release time of Software. Software development method incorporates a piece of vulnerabilities and ambiguities. We have proposed a multi objective software release time issue under fuzzy environment using a software reliability growth model to overcome such vulnerabilities and ambiguities. Further we have discussed the fuzzy environment framework to deal with the issue. Considering model and issue, we can especially address the issue of when to release software under these conditions. Results are illustrated numerically.

The recent research trend has highlighted that multiple stakeholders are involved during requirement gathering in agile software development. Hence, leading to an increased number of duplicate user stories in agile product backlog during requirement gathering. The objective of this paper is to evaluate the existing techniques employed in identifying and eliminating the duplicate user stories from agile product backlog and to overcome the existing gaps with the help of a newly proposed clustering algorithm. An agile user story is expressed as a function of input and output parameters. That said multiple user stories having similar set of input parameters are most likely to be duplicate causing a redundancy. The newly proposed algorithm is used for clustering user stories having similar set of input parameters through various iterations and then removing the identified duplicate user stories from agile product backlog. This paper also introduces the concept of mass clustering which means clustering a number of user stories in single run. Experimental results prove the proposed model is capable of handling small and large releases ranging between 100 to 1000 user stories with similar efficiency. The proposed clustering algorithm outperformed the clustering algorithms and resulted in 37% decrease in agile product backlog by eliminating duplicate user stories causing redundancy. The experimental results are obtained from the logs of the MATLAB tool. However, the provided algorithm is generic in nature and can be implemented using R, Python or SAS programming tools. The provided algorithms employs proven matrix operations. The proposed clustering algorithm overcomes the limitation of existing user story management methods and clearly out performs when compared with other clustering algorithms. Finally, this paper gives recommendations about the usage of the provided clustering algorithm during agile release planning for eliminating duplicate user stories from agile product backlog.

Every software vendor uses tools for managing transactions with customer. perceived quality is customer satisfaction criteria with overall system quality. In software metrics terminology, general definition of perceived quality is the logical feel of the system lying between system assurance and product(s) reliability. We would like to propose a model for perceived quality as a customer point of view measured using Weibull distribution. Our study is not limited to defects but including all the transactions involved. We are considering intensity, time between requests, active number at any point 18of time, and timeline of each transactions. The distribution can reveal the quality perspective and focus areas for improving the perception.

Home automation is an exciting field allowing people to remotely control various household devices. This research uses a cloud environment and NodeMCU to achieve home automation. NodeMCU is an open-source firmware and development board based on the ESP8266 Wi-Fi module. It is widely used in Internet of Things (IoT) projects and is compatible with various sensors, actuators and other devices. A cloud platform is used to manage and control the home automation system. Many cloud service providers include AWS, GCP, Microsoft Azure, etc. The cloud platform offers various services such as databases, communication queues or storage, which can be selected according to the requirements. Use cases are defined and the system is designed accordingly. This includes the equipment used in the research. Sensors such as temperature, humidity, light, motion, or door/window sensors can be used to collect data. Devices such as relays, motors or LEDs are used to control devices. System implementation is done by writing NodeMCU code, configuring cloud services and integrating devices. The system has been tested, debugged and optimized to improve performance. System maintenance is essential after commissioning. This includes updating the software, fixing bugs or adding new features. The system may need to scale as additional devices or users are added.

In response to the imperative for resilient power distribution networks, we present a comprehensive approach to enhance the resiliency of an IEEE 123 bus distribution network through multi-objective optimization, primarily via demand response scheduling. Our framework focuses on minimizing cost and optimally reducing the load burden during islanded scenario. Objective functions encompass critical resiliency metrics, including reliability, restoration time, load shedding, and tie-switch adoption. We enforce constraints to ensure the network's practical feasibility, covering voltage limits, capacity restrictions, and operational considerations. NSGA II identifies Pareto-optimal solutions by assessing trade-offs between resiliency objectives, offering versatile strategies for bolstering network resiliency. These findings provide actionable insights for decision-makers, contributing to the fortification of distribution networks against disruptive events.

Objective: From the literature review, it is evident that the concept of “regression testing” inherited in agile software testing originates from software maintenance practices. Therefore, the existing algorithms for regression testing revolve around the software maintenance principles rather than agile methodology. The objective of this paper is to evaluate the degree of fitness of the existing regression test-suite development algorithms for performing the regression testing in agile. Methods: This paper performs a systematic literature review for research work published from 2006 to 2018, which includes survey of the existing regression testing algorithms to identify and overcome the challenges associated with them while performing regression testing in agile. This research paper considers the four research questions into scope for analyzing the fitness of existing regression test-suite development algorithm for performing regression testing under agile methodology. Further, this paper attempts to propose approach for the development of the regression test-suite suitable for regression testing under agile methodology. Results: The current regression test-suite development algorithm were found unsuitable for performing the regression testing under agile methodology due to the newly identified four key challenges associated with them. Conclusion: The current regression test-suite development algorithms aligned with software maintenance principles rather than agile methodology. In addition, the newly proposed approach for regression test-suite development found to be easily adaptable by agile teams as it aligns with agile methodology principles. Finally, this paper recommends the adoption of agile principle through the newly proposed approach for developing regression test-suite for performing regression testing under agile methodology.

From the recent literature review, it is evident that existing agile methodology lacks the method to evaluate the requirement understanding of agile team members for a given set of requirement chosen for agile software development. Hence, there is a need to introduce a requirement understanding check to ensure every agile team member follows the given requirement clearly without any ambiguity. To fill this existing gap, this research paper proposes to extend the usage of story cards to evaluate the understanding of the given requirement and to highlight any challenges and risks in the early stage of requirement understanding under agile software development methodology, if any. This paper primarily focuses to introduce a robust requirement understanding evaluation process in agile methodology. The research results were found to be motivating and were analyzed by comparing the data-points using time-series for performing agile query analysis, agile team velocity analysis and agile team involvement analysis for two agile teams where one team delivered the sprint output using agile traditional method while another team opted for proposed approach. A considerable decrease of 33.07% was observed in the number of queried raised and a significant increase of 26.36% in agile velocity was observed for agile sprint under proposed approach when compared to agile traditional approach. Also, a significant shift from 40%-80% team involvement under traditional agile method was uplifted to 80%-90% team involvement under proposed approach.

These days the Object-Oriented (OO) paradigm is used extensively in the development of software systems. The OO metrics can be employed to access the quality of these OO systems. Many metrics and metric suits have been developed by the scientists to access the quality of software. The properties of object-oriented designs can be measured using object-oriented metrics. With the help of metrics, estimations of project milestones can be attained more accurately. It is possible to develop software projects with nominal faults. A study shows that an estimated 42% on account of corrective maintenance can be saved by using object-oriented metrics. This paper assesses the capability of Object Oriented metrics to identify fault-proneness in Object Oriented software systems using statistical and regression analysis. Three projects from the NASA data set have been used to determine the applicability of object-oriented CK metrics. The CK metrics are used to predict the bugs in the class. The fault-proneness using OO metrics has been calculated. The information collected using this method will increase the quality and reliability of the OO software systems.

Abstract Agile methods adopted in the software industries have given a new edge as well as scope to software outcomes. To determine anomalies that has been triggered by analytic hierarchical process (AHP) or any other fuzzy AHP methods in evaluation and selection in the establishment of two-way assessments of agile methods, a model methodology dependent on the AHP and fundamental hypothesis of the triangular fuzzy number (TFN) is depicted by communicating fuzzy AHP completely and numerically. In this paper we uncover the disagreement of Fuzzy analytic hierarchical process (FAHP) to decide the process which is considered best in agile according to the benchmark where semantic terms are correlated with triangular fuzzy numbers. Furthermore, the research paper also discovers the process that is considered best amongst the various criteria of decision-making issues for agile methodology. This paper further applies two-way assessment that discusses the viewpoint of software developers as well as managers and focuses on the significance of different identified attributes and then the overall utility is calculated considering the threshold measure from the perspective of the organization.

The success of an organization is to deliver the good-quality products as per the client's needs. But few organizations are unable to deliver the successful product due to number of software barriers. The research is based on the study and analysis of different requirement barriers, which causes problem in agile implementation. Several authors identified the barriers for successful implementation of software, but none have found the barriers at the initial stage of requirements. The motivation behind this work is to classify the main requirement barriers to the effective implementation of software projects in agile development. For the study, interviews were conducted with developers and testers. The results recognized the key barriers and it will deliver the roadmap to managers to take suitable steps to overwhelm the major barriers to effective software implementation.

the agile software development process has a set of standard agile ceremonies, which are must for an agile team to conduct in order to preserve its agility. However, there is always a challenge for agile teams to decide how much agile sprint time should be spent on hosting agile ceremonies and agile product build tasks. This is due to the fact that if most of the agile sprint time is spent on hosting agile ceremonies then the time for agile product build tasks will be reduced significantly impacting the velocity of the agile team. Hence, there is a need to find a solution with the help of which agile team can strike a right balance among various agile ceremonies and agile product build tasks during a sprint. To overcome this gap, this research paper analyses the data for 14 agile sprints to understand the agile time distribution across agile sprint ceremonies and agile product build tasks under current approach. Because of which, this research paper proposes the values for two newly introduced agile co-efficient namely coefficient of agile ceremony time and co-efficient of agile product build time to suggest the ideal time to be spent on agile ceremonies during a sprint. From the research results, it was evident that the velocity of the agile team following the proposed approach reported an increase in velocity by 13.96% for sprints when compared to the velocity of the agile team following the existing approach. The research work also highlighted that the duration chosen for agile ceremony by agile teams is independent of sprint lengths.

DoS (denial of service) assault is the most prevalent assault these days. It imposes a major risk to cybersecurity. At the point when this assault is propelled by numerous conveyed machines on a solitary server machine, it is called as a DDoS (distributed denial of service) assault. Additionally, DoS bypass on DHCP (dynamic host configuration protocol) server assault is a rising and famous assault in a system. The authors have proposed a stochastic intrusion detection game-based arrangement utilizing controlled Markov chain that figures the transition probabilities starting with one state then onto the next in a state transition diagram. At first, the authors have conjectured these assaults, and after that, they proposed a methodology that uses the idea of master and slave IPS (intrusion prevention system). This approach works well when mapped to these estimated assaults and accordingly helps in the recognition and counteractive action of these assaults in a cloud domain.

Agile methodology promotes changing requirements and its journey has come a long way in the software industries giving edge as well as dimensions to software products. According to the statistics, agile methodology is more successful than traditional projects. The agile team manages the project more efficiently and delivers quality product. In agile methodology, customer satisfaction is a priority due to rapid development as well as continuous delivery which is the core of the Dynamic System Development (DSD). Thus, the industry keeps pace with the new expertise and changing market situation. This paper reveals the utilization of the Fuzzy Analytic Hierarchical Process (FAHP) which is a Multi-Criteria Decision Making (MCDM) process used in the software industry by correlating the selected alternatives using fuzzy triangular numbers, which decides on the best process of agile. Thus, the methodology of Fuzzy AHP for agile process selection is discussed in this research paper. Moreover, by expressing Fuzzy AHP comprehensively and numerally in this paper, the best likely process selection for agile methodology amongst various criteria as well as decision making issues is implemented.

The technological era has changed drastically from whatit used to be few decades ago. Each person at this time may have more than one device which produces data, and being in this era, data is neither limited nor it is resistible. In decade like this every device produces data, each click produces data; this data could be structured, semi-structured and unstructured containing location data to logs of sensors. The amount of data being produced every day is just increasing and there is no near future that this will slow down. This has come as a blessing to almost all sectors as this data can be used to make their future decision, thus decreasing the changes of failure. This paper aims to give pattern analysis in academic for mentor systems. We have analyzeda case study of an academic dataset of mentor systems and analyzed this to give the behavior and pattern included in the data to make better decision.

The novel approach to physical security based on visible light communication (VLC) using an informative object-pointing and simultaneous recognition by high-framerate (HFR) vision systems is presented in this study. In the proposed approach, a convolutional neural network (CNN) based object detection method is used to detect the environmental objects that assist a spatiotemporal-modulated-pattern (SMP) based imperceptible projection mapping for pointing the desired objects. The distantly located HFR vision systems that operate at hundreds of frames per second (fps) can recognize and localize the pointed objects in real-time. The prototype of an artificial intelligence-enabled camera-projector (AiCP) system is used as a transmitter that detects the multiple objects in real-time at 30 fps and simultaneously projects the detection results by means of the encoded-480-Hz-SMP masks on to the objects. The multiple 480-fps HFR vision systems as receivers can recognize the pointed objects by decoding pixel-brightness variations in HFR sequences without any camera calibration or complex recognition methods. Several experiments were conducted to demonstrate our proposed method’s usefulness using miniature and real-world objects under various conditions.

With the advent of disruptive methodologies to ensure higher success rates in software engineering processes, the agile software development principles have gained the interest of large organizations. Agile methodology is characterized by iterative and incremental nature, customer-oriented development, adaptability and brisk development cycles. This study analyses the criteria of gathering data and evaluation is conducted by the expert’s judgement from which valuable information is extracted to deliver quality software. Critical Success Factors (CSF) is dynamic in implementing an autonomous methodology using entropy approach from the organisation perspective CSF must be known initially following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) which is a multiple-criteria decision-making (MCDM) approach. The approach’s aim is to in parallel minimize and maximise the critical factors. CSF which needs more attention are emphasized from this research findings and is arranged from high to low. We conclude this study by selecting the best software methodology using entropy and TOPSIS.