A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI.

Maintaining quality in software development projects is becoming very difficult because the complexity of modules in the software is growing exponentially. Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. The SPAM-XAI model exhibited improved performance after experimenting with the NASA PROMISE repository's datasets. It achieved an accuracy of 98.13% on CM1, 96.00% on PC1, and 98.65% on PC2, surpassing previous state-of-the-art and baseline models with other evaluation matrices enhancement compared to existing methods. The SPAM-XAI model increases transparency and facilitates understanding of the interaction between features and error status, enabling coherent and comprehensible predictions. This enhancement optimizes the decision-making process and enhances the model's trustworthiness in the SDLC.

E2SVM: Electricity-Efficient SLA-aware Virtual Machine Consolidation approach in cloud data centers.

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.

Optimising barrier placement for intrusion detection and prevention in WSNs.

This research addresses the pressing challenge of intrusion detection and prevention in Wireless Sensor Networks (WSNs), offering an innovative and comprehensive approach. The research leverages Support Vector Regression (SVR) models to predict the number of barriers necessary for effective intrusion detection and prevention while optimising their strategic placement. The paper employs the Ant Colony Optimization (ACO) algorithm to enhance the precision of barrier placement and resource allocation. The integrated approach combines SVR predictive modelling with ACO-based optimisation, contributing to advancing adaptive security solutions for WSNs. Feature ranking highlights the critical influence of barrier count attributes, and regularisation techniques are applied to enhance model robustness. Importantly, the results reveal substantial percentage improvements in model accuracy metrics: a 4835.71% reduction in Mean Squared Error (MSE) for ACO-SVR1, an 862.08% improvement in Mean Absolute Error (MAE) for ACO-SVR1, and an 86.29% enhancement in R-squared (R2) for ACO-SVR1. ACO-SVR2 has a 2202.85% reduction in MSE, a 733.98% improvement in MAE, and a 54.03% enhancement in R-squared. These considerable improvements verify the method's effectiveness in enhancing WSNs, ensuring reliability and resilience in critical infrastructure. The paper concludes with a performance comparison and emphasises the remarkable efficacy of regularisation. It also underscores the practicality of precise barrier count estimation and optimised barrier placement, enhancing the security and resilience of WSNs against potential threats.

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain.

The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions.

Blockchain-Based Trust Management Framework for Cloud Computing-Based Internet of Medical Things (IoMT): A Systematic Review.

The internet of medical things (IoMT) is a smart medical device structure that includes apps, health services, and systems. These medical equipment and applications are linked to healthcare systems via the internet. Because IoT devices lack computational power, the collected data can be processed and analyzed in the cloud by more computationally intensive tools. Cloud computing in IoMT is also used to store IoT data as part of a collaborative effort. Cloud computing has provided new avenues for providing services to users with better user experience, scalability, and proper resource utilization compared to traditional platforms. However, these cloud platforms are susceptible to several security breaches evident from recent and past incidents. Trust management is a crucial feature required for providing secure and reliable service to users. The traditional trust management protocols in the cloud computing situation are centralized and result in single-point failure. Blockchain has emerged as the possible use case for the domain that requires trust and reliability in several aspects. Different researchers have presented various blockchain-based trust management approaches. This study reviews the trust challenges in cloud computing and analyzes how blockchain technology addresses these challenges using blockchain-based trust management frameworks. There are ten (10) solutions under two broad categories of decentralization and security. These challenges are centralization, huge overhead, trust evidence, less adaptive, and inaccuracy. This systematic review has been performed in six stages: identifying the research question, research methods, screening the related articles, abstract and keyword examination, data retrieval, and mapping processing. Atlas.ti software is used to analyze the relevant articles based on keywords. A total of 70 codes and 262 quotations are compiled, and furthermore, these quotations are categorized using manual coding. Finally, 20 solutions under two main categories of decentralization and security were retrieved. Out of these ten (10) solutions, three (03) fell in the security category, and the rest seven (07) came under the decentralization category.

Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net.

Early detection of vessels from fundus images can effectively prevent the permanent retinal damages caused by retinopathies such as glaucoma, hyperextension, and diabetes. Concerning the red color of both retinal vessels and background and the vessel's morphological variations, the current vessel detection methodologies fail to segment thin vessels and discriminate them in the regions where permanent retinopathies mainly occur. This research aims to suggest a novel approach to take the benefit of both traditional template-matching methods with recent deep learning (DL) solutions. These two methods are combined in which the response of a Cauchy matched filter is used to replace the noisy red channel of the fundus images. Consequently, a U-shaped fully connected convolutional neural network (U-net) is employed to train end-to-end segmentation of pixels into vessel and background classes. Each preprocessed image is divided into several patches to provide enough training images and speed up the training per each instance. The DRIVE public database has been analyzed to test the proposed method, and metrics such as Accuracy, Precision, Sensitivity and Specificity have been measured for evaluation. The evaluation indicates that the average extraction accuracy of the proposed model is 0.9640 on the employed dataset.

Immunity credentials using self-sovereign identity for combating COVID-19 pandemic.

The refugees and migrants are not recorded generally and deemed invisible by governments without providing them with identity and welfare services. The COVID-19 pandemic has badly impacted the economy, and the poor migrants and refugees have suffered most due to the closure of industries and informal sectors. Lack of legal identity made them more vulnerable and excluded them from getting benefits of even meagre government support and welfare schemes. Self-sovereign identity is a form of distributed digital identity that can provide immutable identity with full user control and interoperability features. Self-sovereign identities also ensure the privacy and security of personal information. SSI model can effectively provide migrants and refugees with an effective legal identity and include them in government welfare schemes and other schemes run by non-governmental agencies. Also, SSI can be used for uniquely identifying the people who have been already vaccinated or tested negative from COVID-19 within a stipulated time. This paper reviews the aspects of SSI application during the pandemic situation like COVID-19.

Lumbar Spinous Process-Splitting Laminoplasty: A Novel Technique for Minimally Invasive Lumbar Decompression.

Minimally invasive posterior spinous process-splitting laminoplasty preserving the paraspinal musculature has been introduced to treat patients with lumbar spinal stenosis. Despite its theoretical advantage of limiting muscular trauma, additional efforts are required to evaluate patients' clinical and functional results following this procedure. Between 2010 and 2012, 37 patients underwent spinous process-splitting laminoplasty for lumbar stenosis at a mean age of 68 years (range, 36-87 years) and were followed for minimum of 1 year (mean, 1.3 years). There were 22 (59%) men and 15 (41%) women. Mean number of levels treated with a spinous process-splitting laminoplasty was 2.2 (range, 1-6 levels). Patients had statistically significant improvements in their scores for all self-reported outcomes, including visual analog scale (VAS) for back and leg pain, Oswestry Disability Index (ODI), and Short Form 36 (SF-36) components. Mean VAS significantly decreased by 4.4±3.2 points for back pain and 3.9±3.7 points for leg pain (P<.0001). Mean ODI significantly decreased by 17.5±19.1 points (P<.0001), and mean SF-36 significantly increased by 29±30.4 points (P=.0017) for the physical component and 21.8±25.6 points (P=.0062) for the mental health component. Four (10.8%) patients had a dural tear requiring repair (3 were intraoperative), 3 (8%) had an epidural hematoma requiring evacuation, 1 (2.7%) had an infection requiring irrigation and debridement, and 2 (5%) had additional decompression for symptom recurrence secondary to instability. Lumbar spinous process-splitting laminoplasty is a novel minimally invasive technique that provides adequate decompression for the neuronal elements and may avoid extensive paraspinal muscular damage associated with conventional laminectomy. Patients demonstrated significant improvements in pain and overall heath and function scores at a minimum 1-year follow-up. [Orthopedics.2016; 39(5):e950-e956.].

Heat shock protein 70 regulates Tcl1 expression in leukemia and lymphomas.

T-cell leukemia/lymphoma 1 (TCL1) is an oncogene overexpressed in T-cell prolymphocytic leukemia and in B-cell malignancies including B-cell chronic lymphocytic leukemia and lymphomas. To date, only a limited number of Tcl1-interacting proteins that regulate its oncogenic function have been identified. Prior studies used a proteomic approach to identify a novel interaction between Tcl1 with Ataxia Telangiectasia Mutated. The association of Tcl1 and Ataxia Telangiectasia Mutated leads to activation of the NF-κB pathway. Here, we demonstrate that Tcl1 also interacts with heat shock protein (Hsp) 70. The Tcl1-Hsp70 complex was validated by coimmunoprecipitation experiments. In addition, we report that Hsp70, a protein that plays a critical role in the folding and maturation of several oncogenic proteins, associates with Tcl1 protein and stabilizes its expression. The inhibition of the ATPase activity of Hsp70 results in ubiquitination and proteasome-dependent degradation of Tcl1. The inhibition of Hsp70 significantly reduced the growth of lymphoma xenografts in vivo and down-regulated the expression of Tcl1 protein. Our findings reveal a functional interaction between Tcl1 and Hsp70 and identify Tcl1 as a novel Hsp70 client protein. These findings suggest that inhibition of Hsp70 may represent an alternative effective therapy for chronic lymphocytic leukemia and lymphomas via its ability to inhibit the oncogenic functions of Tcl1.