International Journal on Recent and Innovation Trends in Computing and Communication
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IoT-enabled Building Automation Systems: Challenges, Opportunities, and Case Studies in Energy Efficiency and user comfort
Full text linkThe integration of Internet of Things (IoT) technology into building automation systems (BAS) has ushered in a new era of smart buildings, revolutionizing the way we design, manage, and inhabit built environments. This abstract provides a comprehensive exploration of IoT-enabled BAS, focusing on the challenges, opportunities, and case studies that shape their role in driving energy efficiency and enhancing user comfort. IoT-enabled BAS face numerous challenges that must be addressed to realize their full potential. These challenges include interoperability issues stemming from the diversity of IoT devices and protocols, concerns regarding data security and privacy, the scalability of IoT deployments to encompass large buildings or portfolios, the inherent complexity of IoT ecosystems, and the upfront costs associated with deployment and maintenance. Despite challenges, IoT-enabled BAS present significant opportunities for improving building performance and occupant well-being. By leveraging real-time data analytics, predictive algorithms, and automated controls, these systems can optimize energy use, personalize occupant comfort preferences, enable remote monitoring and management, facilitate predictive maintenance strategies, and contribute to sustainability goals through efficient resource utilization
BDD for Testing Microservices and Distributed Systems
Full text linkThis research uses Behaviour-Driven Development (BDD) to test microservices and distributed systems for scalability, fault tolerance, and concurrency. By using natural language specifications, BDD helps stakeholders collaborate and record and validate system behaviours. Unit testing, integration testing, and end-to-end (E2E) testing are evaluated inside the BDD framework. Integration testing balances coverage, maintainability, and complexity best. Compared to TDD and ATDD, BDD excels in behaviour specification and stakeholder alignment, complementing TDD's unit test coverage and ATDD's acceptance criteria validation
The Role of Cybersecurity in Protecting Intellectual Property
Full text linkThe protection of intellectual property (IP) in today’s world characterized by digitalization and interconnectedness cannot be overemphasized due to the significance of cybersecurity. This research seeks to establish how the cybersecurity measures interact with the management of IP assets across different domains. They analyze existing forms of threats that include hacking, data breaches, andinternal threats that are a major concern to the IP’s integrity and confidentiality. Furthermore, this research focuses on how current and emergent technologies and initiatives can be utilized in the prevention and management of the threats; with relevant aspects discussed including encryption, access controls, and consciousness monitoring as key principles in IP shield. Looking at case studies and the present field practices, this work expects to reveal important benchmarks and innovative ideas in cybersecurity that the organization needs to follow and adapt to reduce vulnerability and protect the long-term security of its valuable intellectual assets. In this research paper, the need to incorporate adequate cybersecurity measures geared towards the protection of IP has been presented as crucial in the present day organizations. It highlights legal compliance, new age technology, and organizational support in building up the lines of defense against emerging cyber threats. In dissociating cybersecurity and IP protectionism in this piece, the paper addresses a gap in literature by increasing the existing knowledge of how effective preventive measures can be taken in order to reduce threats and secure ideas, financial and innovative value inherent in intellectual properties in the face of the modern threat landscape of computerization
Accountability Frameworks for Autonomous AI Decision-Making Systems
Full text linkAs artificial intelligence systems become more sophisticated at making judgments on their own, it will become increasingly difficult to enforce accountability, responsibility, and adherence to moral and legal standards. In order to support the structured responsibility for assignment and proof of AI systems, this paper will address the nature of an accountability framework and its associated issues. Important elements like openness, human oversight, and flexibility are incorporated into the proposed framework to regulate AI in order to meet the accountability difficulties that have been highlighted. Through industrial case studies, some important guidelines for implementing and expanding the framework were also supplied, ensuring that businesses boost compliance, trust, and responsible adoption of AI technology
The Design and Assessment of Eudragit Gum Nanoparticles Containing Moronic Acid for Cancer Treatment
Full text linkThe employment of therapeutic techniques in the treatment of cancer is frequently associated with multidrug resistance or drug tolerance. The effectiveness of plant secondary metabolites in the fight against cancer can be significantly increased by synthesizing them at the nanometric scale. Moronic acid (MA) is one type of pentacyclic triterpenoid that inhibits the growth of cancer cells by blocking the regulation of cell growth. In this work, we used the oil-in-oil (O/O) emulsion solvent evaporation method to synthesize Eudragit nanoparticles (MENPs) loaded with MA. Enhancing the synergy and bioavailability of the nanoparticles was the aim of this intervention. The zeta potential of +28 mV observed in MENPs indicates the relative stability of the nanoformulations. MA was discovered to have 71.8% encapsulation efficiency. Transmission electron microscopy revealed that the MENPs' particle sizes ranged from 42 to 58 nm. The antioxidant and anticancer capabilities of the MENPs were significantly stronger and they showed a continuous release pattern when compared to the individual MA particles in their free state. The in vitro investigations showed that the combination of MA encapsulated in Eudragit had a greater inhibitory effect on the growth of A-549, MCF-7, and Hela cell lines as compared to the solo administration of MA. This finding confirms the potent anticancer properties of the encapsulated compounds
Improved Environmental Adaptation Method for Scheduling Workflows in Cloud
Full text linkCloud users are expanding at rapid rate which forces the cloud data centres execute billions of commands each second. A random user request must be planned and processed on the workflow without knowing the sequence of future requests. This makes workflow scheduling on distributed environment as NP-hard problem. In this work we present an optimization-based scheduling approach that responds to cloud’s dynamic nature. The suggested technique derives from the Environmental Adaptation Method (EAM), an evolutionary algorithm established to handle optimization problems. After EAM’s original proposal, multiple better versions were made to fix inherent issues. Most of the revised algorithms performed well in lower dimensions but degraded performance is seen in higher ones. Most of these methods were binary encoded, which poses issues for real-valued parameters owing to conversion cost. Improved Environmental Adaptation Method with Real parameters (IEAM-R) was presented to deal with real valued problems to increase IEAM’s convergence rate. IEAM-R performs effectively on lower-dimensional benchmark functions, but not on larger dimensions. We changed IEAM-R and created a new algorithm to increase the diversity of solutions in higher dimensions. Exploration and exploitation must be redesigned to im-prove convergence rate. On all 24 benchmark functions, the proposed modified optimization algorithm with fine-tuned operators, outperforms its predecessors and other state-of-the-art algorithms. The technique is then used to the workflow scheduling issue in cloud computing, where it reduces the overall cost of cloud operation as compared to other heuristic and metaheuristic approaches
Use Confidential Computing to Secure Your Critical Services in Cloud
Full text linkConfidential computing has emerged as a critical technology to secure sensitive data in cloud environments. This cutting-edge approach protects data during processing, ensuring confidentiality and integrity even in shared or untrusted environments. This paper provides an in-depth exploration of confidential computing, including its evolution, key components, and offerings from major public cloud providers. We also examined the importance of confidential computing for securing AI workloads and a cost analysis of implementing such solutions. Through case studies and real-world examples, we demonstrate the benefits of confidential computing in ensuring regulatory compliance, mitigating risks, and driving business growth. As the cloud landscape continues to evolve, confidential computing plays a vital role in safeguarding sensitive data and workloads, making it an essential component of modern cloud security strategies
AI-Driven Financial Planning: A Study on Predictive Modelling
Full text linkThe integration of artificial intelligence (AI) in financial planning has revolutionized the domain of personal and institutional finance, primarily through the use of predictive modelling techniques. These models facilitate precise forecasting of market trends, asset prices, and individual financial behaviours. This research explores the evolution of AI-driven financial planning, focusing on the theoretical and practical dimensions of predictive modelling. Key components include time-series forecasting, reinforcement learning, explainable AI, and data preprocessing. Through rigorous analysis of real-world applications and model architectures, the study provides a comprehensive assessment of the technical landscape, challenges, and future prospects of AI in financial decision-making
Landmark Annotation and Mandibular Lateral Deviation Analysis of Posteroanterior Cephalograms using a Convolutional Neural Network
Full text linkBackground/purpose: Facial asymmetry is relatively common in the general population. Here, we propose a fully automated annotation system that supports analysis of mandibular deviation and detection of facial asymmetry in posteroanterior (PA) cephalograms by means of a deep learning-based convolutional neural network (CNN) algorithm.Materials and methods: In this retrospective study, 400?PA cephalograms were collected from the medical records of patients aged 4 years 2 months–80 years 3 months (mean age, 17 years 10 months; 255 female patients and 145 male patients). A deep CNN with two optimizers and a random forest algorithm were trained using 320?PA cephalograms; in these images, four PA landmarks were independently identified and manually annotated by two orthodontists.Results: The CNN algorithms had a high coefficient of determination (R2), compared with the random forest algorithm (CNN-stochastic gradient descent, R2?=?0.715; CNN-Adam, R2?=?0.700; random forest, R2?=?0.486). Analysis of the best and worst performances of the algorithms for each landmark demonstrated that the right latero-orbital landmark was most difficult to detect accurately by using the CNN. Based on the annotated landmarks, reference lines were defined using an algorithm coded in Python. The CNN and random forest algorithms exhibited similar accuracy for the distance between the menton and vertical reference line.Conclusion: Our findings imply that the proposed deep CNN algorithm for detection of facial asymmetry may enable prompt assessment and reduce the effort involved in orthodontic diagnosis