There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classification and machine learning play a significant role in the timely detection of thyroid diseases. This timely classification will indeed affect the timely treatment of the patients. Automatic and precise thyroid nodule detection in ultrasound pictures is critical for reducing effort and radiologists’ mistake rate. Medical images have evolved into one of the most valuable and consistent data sources for machine learning generation. In this paper, various machine learning algorithms like decision tree, random forest algorithm, KNN, and artificial neural networks on the dataset create a comparative analysis to better predict the disease based on parameters established from the dataset. Also, the dataset has been manipulated for accurate prediction for the classification. The classification was performed on both the sampled and unsampled datasets for better comparison of the dataset. After dataset manipulation, we obtained the highest accuracy for the random forest algorithm, equal to 94.8% accuracy and 91% specificity.

When designed, technologies and frameworks are not created to be as dynamic and flexible as to cater to the requirements of other domains, and so is the case with Blockchain technology. Specifically designed for cryptocurrency, Blockchain was not intended to be used in other domains. However, during the past few years, critics argued that Blockchain has the potential to deal with some unique requirements like confidentiality and immutability and can therefore be deployed in several areas other than cryptocurrency. The use of Blockchain to support Accounting Information Systems (AIS) through enterprise resource planning (ERP) is another motivating domain to investigate in this research. ERP is another promising technology that has gained significant attention across the globe. In this research, a hybrid solution is proposed to ensure AIS data integrity against any deliberate attempt or mala-fide intention for alteration or deletion from the database that can be verified at any later stage. Since Blockchain can be used to prevent any mutability in the stored data, the proposed solution presents a concept of Data Vaults backed by the Blockchain. To this end, we apply cryptographic primitives like SHA256 on the data inside the block and then chain that block to secure data vaults. So far, Blockchain has not yet proven itself as an alternative to any traditional database system. However, it can be applied in conjunction with the Relational Database Management Systems (RDBMS) to provide cost-effective yet robust solutions. This research demonstrates the application of a simple and lean version of Blockchain to assist enterprises in storing their financial and accounting data into data vaults, ensuring their data integrity against any alterations. The suggested cost-effective framework can be easily integrated into AIS and ERP systems to identify data breaches.

Smart cities’ have experienced an increasingly higher rate of urbanization and increase of the population leading to strengthening the pressing needs in waste management. In this paper, we present an intelligent waste classification system that utilises Convolutional Neural Networks (CNNs) for automatic segregation into twelve categories of waste, employing image data. The model is trained on 15,535 images from a publicly available dataset using preprocessing and data augmentation to increase generalisation and mitigate class imbalance. A performance comparison in terms of precision, recall, F1 score, and accuracy shows that the proposed ResNet-based model yields a classification accuracy of 98.16%, outperforming previous work on conventional deep learning architectures. Experimental results demonstrate that the model is a robust framework for handling various types of waste (organic, recyclable, and hazardous) and is a very general model, as confirmed by cross-validation and real-world tests. The proposed system demonstrates great promise for upscaling in automatic waste management towards long-term urban sustainability, improved recycling, and reduced environmental threats.

In software development, the main problem is recognizing the security-oriented issues within the reported bugs due to their unacceptable failure rate to provide satisfactory reliability on customer and software datasets. The misclassification of bug reports has a direct impact on the effectiveness of the bug prediction model. The misclassification issue surely compromises the accuracy of the system. Manually reviewing bug reports is necessary to solve this problem, but doing so takes a lot of time and is tiresome for developers and testers. This paper proposes a novel hybrid approach based on natural language processing (NLP) and machine learning. To address these issues, the intended outcomes are multi-class supervised classification and bug prioritization using supervised classifiers. After being collected, the dataset was prepared for vectorization, subjected to exploratory data analysis, and preprocessed. The feature extraction and selection methods used for a bag of words are TF-IDF and word2vec. Machine learning models are created after the dataset has undergone a full transformation. This study proposes, develops, and assesses four classifiers: multinomial Naive Bayes, decision tree, logistic regression, and random forest. The hyper-parameters of the models are tuned, and it is concluded that random forest outperformed with a 91.73% test and 100% training accuracy. The SMOTE technique was used to balance the highly imbalanced dataset, which was initially created for the justified classification. The comparison between balanced and imbalanced dataset models clearly showed the importance of the balanced dataset in classification as it outperformed in all experiments.

This rapidly changing digital world is always sensitive to improving security and resilience to protect the inhabitants of this ecosystem in terms of data, processes, repositories, communication, and functions. The transformation of this digital ecosystem is heavily dependent on cloud computing, as it is becoming the global platform for individuals, corporates, and even governments. Therefore, the concerns related to security are now linked closely with cloud computing. In this paper, a multi-cloud security framework takes a view on the development of security mechanisms to provide a diversion to the attacker. The purpose is to gain more time to analyze the attack and mitigate the intrusion without compromises. This mechanism is designed using the honeypot technology that has been around for some time but has not been used in cloud computing and other technologies. The proposed framework provides modules related to managing the multi-cloud platform, the intrusion detection and prevention system, and honeypots. The results show significant improvement in the accuracy of detecting attacks. These results are generated in a two-phase scenario, and the first phase has been analyzed without the engagement of the honeypot module presented in the framework. The second phase has been executed with same parameters and conditions by engaging the honeypot module. It includes a comparison taxonomy of both results and an in-depth study of existing honeypots, as well as critical design elements for current honeypot research and outstanding concerns for future honeypots in IoT, multi-cloud contexts.

Efforts were exerted to enhance the live virtual machines (VMs) migration, including performance improvements of the live migration of services to the cloud. The VMs empower the cloud users to store relevant data and resources. However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs. Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process. Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent. By virtualizing the network resources, the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing. Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in cross-data-center situations. This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold, thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement (SLA). In this research, we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes. This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine.