A method to enhance privacy preservation in cloud storage through a three-layer scheme for computational intelligence in fog computing.

Recent advancements in cloud computing have heightened concerns about data control and privacy due to vulnerabilities in traditional encryption methods, which may not withstand internal attacks from cloud servers. To overcome these issues about the data privacy and control of transfer on cloud, a novel three-tier storage model incorporating fog computing method has been proposed. This framework leverages the advantages of cloud storage while enhancing data privacy.

Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma.

Unlabelled: Oral cancer can result from mutations in cells located in the lips or mouth. Diagnosing oral cavity squamous cell carcinoma (OCSCC) is particularly challenging, often occurring at advanced stages. To address this, computer-aided diagnosis methods are increasingly being used.

Prediction of Wear Rate of Glass-Filled PTFE Composites Based on Machine Learning Approaches.

Wear is induced when two surfaces are in relative motion. The wear phenomenon is mostly data-driven and affected by various parameters such as load, sliding velocity, sliding distance, interface temperature, surface roughness, etc. Hence, it is difficult to predict the wear rate of interacting surfaces from fundamental physics principles.

Variational Autoencoders for Network Lifetime Enhancement in Wireless Sensors.

Wireless sensor networks (WSNs) are structured for monitoring an area with distributed sensors and built-in batteries. However, most of their battery energy is consumed during the data transmission process. In recent years, several methodologies, like routing optimization, topology control, and sleep scheduling algorithms, have been introduced to improve the energy efficiency of WSNs.

Linker Encouraged Solid State Synthesis of MOF Derived Z-Scheme NiCoO/NiO/C toward Efficient Removal of Organic and Inorganic Pollutants from Water.

Here, we represent a solid-state route for the construction of MOF derived multifunctional Z-scheme NiCoO/NiO/C applied for the photocatalytic removal of methylene blue (dye) and tetracycline (drug) and the reduction of Cr(VI) (heavy metal). The developed solid-state method yielded a highly effective NiCoO/NiO/C catalyst by mechanically grinding independently produced Ni and Co-MOFs and subsequently pyrolyzing them. The use of different linkers in the Ni MOF (H-BTC linker) and Co-MOF (2-methylimidazole linker) proved to be effective in constructing the NiCoO/NiO/C composite, ensuring a nonaggregated distribution on a carbon framework.

Revolutionizing Grain and Particle Size Measurement in Metals: The Role of Sensor-Assisted Metallographic Image Analysis.

Metallographic image analysis is vital in the field of metal science due to its potential to automate the sensing process for grain and particle size estimation. To ensure the good quality and reliability of metal products, analysis of the integrity of metallic components is required. In contemporary manufacturing processes, microscopic analysis is a crucial step, mainly when complex systems like gearboxes, turbines, or engines are assembled using various components from multiple suppliers.

An optimized deep strategy for recognition and alleviation of DDoS attack in SD-IoT.

The attacks like distributed denial-of-service (DDoS) are termed as severe defence issues in data centres, and are considered real network threat. These types of attacks can produce huge disturbances in information technologies. In addition, it is a complex task to determine and fully alleviate DDoS attacks.

Mitigating urban heat island and enhancing indoor thermal comfort using terrace garden.

The United Nations advocates for sustainable urban planning and design, emphasizing green infrastructure initiatives to mitigate urban heat island effects and enhance the resilience and livability of cities globally. To address urban heat challenges, a study was conducted in Chennai, India, from April to June 2023. The study focused on assessing temperature dynamics on a building's terrace by comparing a well-maintained garden area with an exposed region.

The image dataset of Indian coins: A machine learning approach for Indian currency.

In an increasingly digital world, the significance of creating a Comprehensive Image Dataset of Contemporary Indian Coins (CIDCIC) cannot be overstated. This research presents a dataset comprising 6,672 images of 53 different classes of Indian coins, including denominations of 25 Paisa, 50 Paisa, 1 Rupee, 2 Rupee, 5 Rupee, 10 Rupee, and 20 Rupee. The images of coins with various shapes and sizes are taken from obverse and reverse sides in various environments and different backgrounds.

Facile construction of multifunctional xNiCoO/BiVO heterojunction with accelerated charge transfer for efficient photocatalytic treatment of Cr (VI), MB and TC under visible light.

The release of industrial effluents, comprising of organic dyes, antibiotics, and heavy metals poses substantial environmental and ecological threats. Among the different approaches, the utilization of heterogeneous photocatalysis based on semiconducting metal oxides is of paramount important to removal of organic ( MB dye and TC antibiotic) and inorganic pollutants ( Cr (VI) ) in wastewater. In this work, a new approach for creating type-II heterojunction photocatalysts named xNiCoO/BiVO or BNC is suggested.

Face mask wearing image dataset: A comprehensive benchmark for image-based face mask detection models.

The Face Mask Wearing Image Dataset is a comprehensive collection of images aimed at facilitating research in the domain of face mask detection and classification. This dataset consists of 24,916 images, carefully categorized into two main folders: "Correct" and "Incorrect" representing instances of face masks being worn properly and improperly, respectively. Each folder is further divided into four subfolders, each denoting a specific type of face mask - Bandana, Cotton, N95, and Surgical.

Sen-2 LULC: Land use land cover dataset for deep learning approaches.

Land Use Land Cover (LULC) classification is pivotal to sustainable environment and natural resource management. It is critical in planning, monitoring, and management programs at various local and national levels. Monitoring changes in LULC patterns over time is crucial for understanding evolving landscapes.

A comprehensive dataset of damaged banknotes in Indian currency (Rupees) for analysis and classification.

Detecting authentic and quality banknotes presents a significant challenge, particularly for individuals with low vision or visual impairments. Extensive research has been dedicated to achieving accurate banknote detection. It is crucial for clean banknotes to be readily detectable and accepted in daily transactions.

Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation.

In accordance with the inability of various hair artefacts subjected to dermoscopic medical images, undergoing illumination challenges that include chest-Xray featuring conditions of imaging acquisi-tion situations built with clinical segmentation. The study proposed a novel deep-convolutional neural network (CNN)-integrated methodology for applying medical image segmentation upon chest-Xray and dermoscopic clinical images. The study develops a novel technique of segmenting medical images merged with CNNs with an architectural comparison that incorporates neural networks of U-net and fully convolutional networks (FCN) schemas with loss functions associated with Jaccard distance and Binary-cross entropy under optimised stochastic gradient descent + Nesterov practices.

Dry fruit image dataset for machine learning applications.

Dry fruits are convenient and nutritious snacks that can provide numerous health benefits. They are packed with vitamins, minerals, and fibres, which can help improve overall health, lower cholesterol levels, and reduce the risk of heart disease. Due to their health benefits, dry fruits are an essential part of a healthy diet.

Cyber-Internet Security Framework to Conquer Energy-Related Attacks on the Internet of Things with Machine Learning Techniques.

The Internet of Things (IoT) ushers in a new era of communication that depends on a broad range of things and many types of communication technologies to share information. This new age of communication will be characterised by the following characteristics: Because all of the IoT's objects are connected to one another and because they function in environments that are not protected, it poses a significantly greater number of issues, constraints, and challenges than do traditional computing systems. This is due to the fact that traditional computing systems do not have as many interconnected components.

Detection of Malicious Cloud Bandwidth Consumption in Cloud Computing Using Machine Learning Techniques.

The Internet of Things, sometimes known as IoT, is a relatively new kind of Internet connectivity that connects physical objects to the Internet in a way that was not possible in the past. The Internet of Things is another name for this concept (IoT). The Internet of Things has a larger attack surface as a result of its hyperconnectivity and heterogeneity, both of which are characteristics of the IoT.

Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images.

The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor.

Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image.

Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person's chances of survival, an early diagnosis is a necessary prerequisite.

FEEDNet: a feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis.

Automated cell nuclei segmentation is vital for the histopathological diagnosis of cancer. However, nuclei segmentation from 'hematoxylin and eosin' (HE) stained 'whole slide images' (WSIs) remains a challenge due to noise-induced intensity variations and uneven staining. The goal of this paper is to propose a novel deep learning model for accurately segmenting the nuclei in HE-stained WSIs.

Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification.

COVID-19 has become a pandemic for the entire world, and it has significantly affected the world economy. The importance of early detection and treatment of the infection cannot be overstated. The traditional diagnosis techniques take more time in detecting the infection.

Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine.

Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome-wide protein interaction (PPI) data is growing as high-throughput experiments become more common.

Early Stroke Prediction Methods for Prevention of Strokes.

The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems. Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage. The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (TIA) before experiencing the actual attack of the stroke.

Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems.

Since the last decade, cloud-based electronic health records (EHRs) have gained significant attention to enable remote patient monitoring. The recent development of Healthcare 4.0 using the Internet of Things (IoT) components and cloud computing to access medical operations remotely has gained the researcher's attention from a smart city perspective.