Decentralized trust framework for smart cities: a blockchain-enabled cybersecurity and data integrity model.

The rapid evolution of smart cities has led to transformative advancements through the integration of IoT devices, sensors, and data-driven systems, yet has simultaneously exposed critical vulnerabilities in cybersecurity, data integrity, and trust management. This research proposes a Decentralized Trust Framework that leverages blockchain technology, AI-driven threat detection, and a Lightweight Adaptive Proof-of-Stake (LA-PoS) consensus mechanism to address these challenges. The framework integrates three key layers: a Blockchain Layer for decentralized trust and immutability, a Cybersecurity Layer employing cryptographic standards and AI-based anomaly detection, and a Data Integrity Protocol Layer for real-time synchronization and tamper-proof data validation.

AraTraditions10k bridging cultures with a comprehensive dataset for enhanced cross lingual image annotation retrieval and tagging.

The exponential growth of multimedia content in the digital age has necessitated the development of advanced cross-lingual systems capable of understanding and interpreting visual information across different languages. However, current efforts have predominantly been focused on monolingual tasks, leaving a substantial gap in cross-lingual multimedia analysis, particularly for non-English languages. To address this gap, AraTraditions10k, a comprehensive and culturally rich dataset, has been introduced to enhance cross-lingual image annotation, retrieval, and tagging, with a specific focus on Arabic and English languages.

DARNet: Deep Attention Module and Residual Block-Based Lung and Colon Cancer Diagnosis Network.

Accurate and efficient lung and colon cancer classification is vital for early detection and treatment planning. Traditional methods require manual effort and expert analysis, leading researchers to explore deep learning models. However, deep learning-based lung and colon cancer classification models face challenges such as generalization, overfitting, gradient vanishing, and hyperparameter tuning.

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, but they suffer from issues such as gradient vanishing, overfitting, and parameter tuning. To address these problems, the deep drug synergy prediction network, named as EDNet is proposed that leverages a modified triangular mutation-based differential evolution algorithm.

An enhanced diabetes prediction amidst COVID-19 using ensemble models.

In the contemporary landscape of healthcare, the early and accurate prediction of diabetes has garnered paramount importance, especially in the wake of the COVID-19 pandemic where individuals with diabetes exhibit increased vulnerability. This research embarked on a mission to enhance diabetes prediction by employing state-of-the-art machine learning techniques. Initial evaluations highlighted the Support Vector Machines (SVM) classifier as a promising candidate with an accuracy of 76.

Public mental health through social media in the post COVID-19 era.

Social media is a powerful communication tool and a reflection of our digital environment. Social media acted as an augmenter and influencer during and after COVID-19. Many of the people sharing social media posts were not actually aware of their mental health status.

3D indoor modeling and game theory based navigation for pre and post COVID-19 situation.

The COVID-19 pandemic has greatly affected human behavior, creating a need for individuals to be more cautious about health and safety protocols. People are becoming more aware of their surroundings and the importance of minimizing the risk of exposure to potential sources of infection. This shift in mindset is particularly important in indoor environments, especially hospitals, where there is a greater risk of virus transmission.

A deep transfer learning approach for COVID-19 detection and exploring a sense of belonging with Diabetes.

COVID-19 is an epidemic disease that results in death and significantly affects the older adult and those afflicted with chronic medical conditions. Diabetes medication and high blood glucose levels are significant predictors of COVID-19-related death or disease severity. Diabetic individuals, particularly those with preexisting comorbidities or geriatric patients, are at a higher risk of COVID-19 infection, including hospitalization, ICU admission, and death, than those without Diabetes.

Exploring transfer learning in chest radiographic images within the interplay between COVID-19 and diabetes.

The intricate relationship between COVID-19 and diabetes has garnered increasing attention within the medical community. Emerging evidence suggests that individuals with diabetes may experience heightened vulnerability to COVID-19 and, in some cases, develop diabetes as a post-complication following the viral infection. Additionally, it has been observed that patients taking cough medicine containing steroids may face an elevated risk of developing diabetes, further underscoring the complex interplay between these health factors.

DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images.

Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models.

Efficient Evolving Deep Ensemble Medical Image Captioning Network.

With the advancement in artificial intelligence (AI) based E-healthcare applications, the role of automated diagnosis of various diseases has increased at a rapid rate. However, most of the existing diagnosis models provide results in a binary fashion such as whether the patient is infected with a specific disease or not. But there are many cases where it is required to provide suitable explanatory information such as the patient being infected from a particular disease along with the infection rate.

Efficient Obstacle Detection and Tracking Using RGB-D Sensor Data in Dynamic Environments for Robotic Applications.

Obstacle detection is an essential task for the autonomous navigation by robots. The task becomes more complex in a dynamic and cluttered environment. In this context, the RGB-D camera sensor is one of the most common devices that provides a quick and reasonable estimation of the environment in the form of RGB and depth images.

Single-image reconstruction using novel super-resolution technique for large-scaled images.

A fast and novel method for single-image reconstruction using the super-resolution (SR) technique has been proposed in this paper. The working principle of the proposed scheme has been divided into three components. A low-resolution image is divided into several homogeneous or non-homogeneous regions in the first component.

Detection and Classification of Colorectal Polyp Using Deep Learning.

Colorectal Cancer (CRC) is the third most dangerous cancer in the world and also increasing day by day. So, timely and accurate diagnosis is required to save the life of patients. Cancer grows from polyps which can be either cancerous or noncancerous.

Influence of COVID-19 on the Tourism Industry in China: An Artificial Neural Networks Approach.

Prior to COVID-19, the tourism industry was one of the important sectors of the world economy. This study intends to measure the perception of Chinese tourists concerning the spread of COVID-19 in China. The crowding perception, xenophobia, and ethnocentrism are the measurement indicators of the study.

An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease.

Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similarities with other diseases such as drug rash.

IoT-based automated water pollution treatment using machine learning classifiers.

Water is one of the most vital sources for the survival of life. In the globe, the accessibility of water in safe and healthy ways is a major concern. The consumption of unsafe water may lead to health risks.

Enhanced Computational Model for Gravitational Search Optimized Echo State Neural Networks Based Oral Cancer Detection.

The Clinical Oncology of American Society report in 2016 predicted deaths are increased upto 9570 due to oral cancer. This cancer occurs due to abnormal tissue growth in the oral cavity. This cancer has limited symptoms, so, it has been difficult to recognize in the early stages.

Memetic Search Optimization Along with Genetic Scale Recurrent Neural Network for Predictive Rate of Implant Treatment.

Implant treatment is one of the most important surgical processes in teeth which reduces the difficulties in teeth by providing the interface between bone and jaw. The established implant treatment used to support the denture, bridge and teeth crown. Even though it supports many dental related activities, the successive measure of implant treatment is fail to manage because it fully depends on the patient's personal activities and health condition of mouth tissues.