Cloud computing has revolutionized the modes of computing. With huge success and diverse benefits, the paradigm faces several challenges as well. Power consumption, dynamic resource scaling, and over- and under-provisioning issues are challenges for the cloud computing paradigm. The research has been carried out in cloud computing for resource utilization prediction to overcome over- and under-provisioning issues. Over-provisioning of resources consumes more energy and leads to high costs. However, under-provisioning induces Service Level Agreement (SLA) violation and Quality of Service (QoS) degradation. Most of the existing mechanisms focus on single resource utilization prediction, such as memory, CPU, storage, network, or servers allocated to cloud applications but overlook the correlation among resources. This research focuses on multi-resource utilization prediction using Functional Link Neural Network (FLNN) with hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The proposed technique is evaluated on Google cluster traces data. Experimental results show that the proposed model yields better accuracy as compared to traditional techniques.

The effective management of municipal solid waste is a critical global issue, affecting both urban and rural areas. To address the growing volume of solid waste, proactive planning is essential. Traditionally, solid waste is often disposed of without segregation, preventing recycling and the recovery of raw materials. Proper waste segregation is a fundamental requirement for effective solid waste management, allowing materials to be recycled efficiently. Emerging technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) offer powerful tools for identifying recyclable materials like glass, plastic, and metal within solid waste. The primary goal of this research is to contribute to a cleaner environment, reduce infant mortality, improve maternal health, and support efforts to combat HIV/AIDS, malaria, and other diseases. This study introduces an intelligent and smart solid waste management system (iSSWMs) designed to smartly collect and segregate solid waste. The proposed system focuses on three types of materials: plastic, glass, and metal. The first phase involves waste collection using smart bins connected to a mobile application, which sends notifications when the bins are full. In the second phase, we develop a deep learning-based mechanical model to segregate the waste, using the VGG-19 model, which achieved a performance accuracy of 99.7% during training. To the best of our knowledge, iSSWMs is a promising framework that integrates both waste collection and segregation through the use of cutting-edge technologies, delivering high accuracy and efficiency.

Acute lymphoblastic leukemia (ALL), one of the prevalent types of carcinogenic disease, has been seen a deadly illness exposing numerous patients across the world to potential threats of lives. It impacts both adults and children providing a narrow range of chances of being cured if diagnosed at a later stage. A definitive diagnosis often demands highly invasive diagnostic procedures thereby proving time-consuming and expensive. Peripheral Blood Smear (PBS) images have been playing a crucial role in the initial screening of ALL in suspected individuals. However, the nonspecific nature of ALL poses a substantial challenge in the analysis of these images thus leaving space for misdiagnosis. Aiming at contribute to the early diagnoses of this life-threatening disease, we put forward automated platform for screening the presence of ALL concerning its specific subtypes (benign, Early Pro-B, Pro-B and Pre-B) using PBS images. The proposed web based platform follows weighted ensemble learning technique using a Residual Convolutional Neural Network (ResNet-152) as the base learner to identify ALL from hematogone cases and then determine ALL subtypes. This is likely to save both diagnosis time and the efforts of clinicians and patients. Experimental results are obtained and comparative analysis among 7 well-known CNN Network architectures (AlexNet, VGGNet, Inception, ResNet-50, ResNet-18, Inception and DenseNet-121) is also performed that demonstrated that the proposed platform achieved comparatively high accuracy (99.95%), precision (99.92%), recall (99.92%), F1-Score (99.90%), sensitivity (99.92%) and specificity (99.97%). The promising results demonstrate that the proposed platform has the potential to be used as a reliable tool for early diagnosis of ALL and its sub-types. Furthermore, this provides references for pathologists and healthcare providers, aiding them in producing specific guidelines and more informed choices about patient and disease management.

Financial Technology (FinTech) has expanded beyond simple mobile banking to online money transfers, crowdfunding, and managing individual investments. This reinforces the importance of ensuring security and privacy in FinTech, e.g., using Blockchain (hereafter referred to as Blockchain-based FinTech applications). In this work, we will systematically summarize recent Blockchain-based FinTech applications. As a result of the advancement of BFinTech, several novel cyberattacks have been evolved to potentially impact BFinTech functionalities. Thus, this work highlights the open research challenges and future research directions in Blockchain-based FinTech applications.

Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs’ effectiveness. The drug-to-drug interactions are detected based on the interactions of chemical substructures, targets, pathways, and enzymes; therefore, machine learning (ML) and deep learning (DL) techniques are used to find the associated DDI events. The DL model, i.e., Convolutional Neural Network (CNN), is used to analyze the DDI. DDI is based on the 65 different drug-associated events, which is present in the drug bank database. Our model uses the inputs, which are chemical structures (i.e., smiles of drugs), enzymes, pathways, and the target of the drug. Therefore, for the multi-model CNN, we use several layers, activation functions, and features of drugs to achieve better accuracy as compared to traditional prediction algorithms. We perform different experiments on various hyperparameters. We have also carried out experiments on various iterations of drug features in different sets. Our Multi-Modal Convolutional Neural Network - Drug to Drug Interaction (MCNN-DDI) model achieved an accuracy of 90.00% and an AUPR of 94.78%. The results showed that a combination of the drug’s features (i.e., chemical substructure, target, and enzyme) performs better in DDIs-associated events prediction than other features.

The Internet of Things (IoT) has revolutionized many domains. Due to the growing interconnectivity of IoT networks, several security challenges persist that need to be addressed. This research presents the application of deep learning techniques for Distributed Denial-of-Service (DDoS) attack detection in IoT networks. This study assesses the performance of various deep learning models, including Latent Autoencoders, LSTM Autoencoders, and convolutional neural networks (CNNs), for DDoS attack detection in IoT environments. Furthermore, a novel hybrid model is proposed, integrating CNNs for feature extraction, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and Autoencoders for dimensionality reduction. Experimental results on the CICIOT2023 dataset show the enhanced performance of the proposed hybrid model, achieving training and testing accuracy of 96.78% integrated with 96.60% validation accuracy. This presents its efficiency in addressing complex attack patterns within IoT networks. Results’ analysis shows that the proposed hybrid model outperforms the others. However, this research has limitations in detecting rare attack types and emphasizes the importance of addressing data imbalance challenges for further enhancement of DDoS attack detection capabilities in future.