Dynamic modern healthcare systems rely heavily on the contributions of computer scientists. The diagnosis process is a team effort involving many people: patients, their families, healthcare providers, researchers, and policymakers. Computer technology plays a crucial role in supporting this effort by providing a number of essential services to all of these groups. In the early stages of many diseases, a diagnosis can be made automatically using a computer-aided system, with some degree of certainty. This paper presents a hybrid optimal deep learning-based model for tuberculosis disease recognition using MRI images. Several deep learning models are combined to extract the most relevant features from MRI images. In particular, we establish a combination between vision transformer (ViTs) and Efficient-Net models in order to maximize classification accuracy. We conducted experiments to investigate the accuracy of the proposed model using the Shenzhen and Montgomery data set, and found that it yielded substantially more accurate and better results than the state of-the-art works.

Background: Alzheimer’s disease (AD) is a progressive neurological disorder that significantly affects middle-aged and elderly adults, leading to cognitive deterioration and hindering daily activities. Notwithstanding progress, conventional diagnostic techniques continue to be susceptible to inaccuracies and inefficiencies. Timely and precise diagnosis is essential for early intervention. Methods: We present an enhanced hybrid deep learning framework that amalgamates the EfficientNetV2B3 with Inception-ResNetV2 models. The models were integrated using an adaptive weight selection process informed by the Cuckoo Search optimization algorithm. The procedure commences with the pre-processing of neuroimaging data to guarantee quality and uniformity. Features are subsequently retrieved from the neuroimaging data by utilizing the EfficientNetV2B3 and Inception-ResNetV2 models. The Cuckoo Search algorithm allocates weights to various models dynamically, contingent upon their efficacy in particular diagnostic tasks. The framework achieves balanced usage of the distinct characteristics of both models through the iterative optimization of the weight configuration. This method improves classification accuracy, especially for early-stage Alzheimer’s disease. A thorough assessment was conducted on extensive neuroimaging datasets to verify the framework’s efficacy. Results: The framework attained a Scott’s Pi agreement score of 0.9907, indicating exceptional diagnostic accuracy and dependability, especially in identifying the early stages of Alzheimer’s disease. The results show its superiority over current state-of-the-art techniques.Conclusions: The results indicate the substantial potential of the proposed framework as a reliable and scalable instrument for the identification of Alzheimer’s disease. This method effectively mitigates the shortcomings of conventional diagnostic techniques and current deep learning algorithms by utilizing the complementing capabilities of EfficientNetV2B3 and Inception-ResNetV2 by using an optimized weight selection mechanism. The adaptive characteristics of the Cuckoo Search optimization facilitate its application across many diagnostic circumstances, hence extending its utility to a wider array of neuroimaging datasets. The capacity to accurately identify early-stage Alzheimer’s disease is essential for facilitating prompt therapies, which are crucial for decelerating disease development and enhancing patient outcomes.

Over the last few years, the amount of available information has increased exponentially in all professional fields, including the medical field. Modern-day patients have access to a wealth of medical information, whether it be from brochures, newspapers, television campaigns, or internet documents. To facilitate and accelerate the search for medical information, more precise systems have been implemented, such as visual question-and-answer systems. A visual question-and-answer system is designed to provide direct and precise answers to questions asked in natural language. In this context, we propose an optimal deep neural network model based on an adaptive optimization algorithm, which takes medical images and natural language questions as input, then provides precise answers as output. Our model starts by classifying medical questions following an embedding phase. We then use a deep learning model for visual and textual feature extraction and emergence. In this paper, we aim to maximize the accuracy rate and minimize the number of epochs in order to accelerate the process. This is a multi-objective optimization problem. The selection of deep learning model parameters, such as epoch number and batch size, is an essential step in improving the model, thus, we use an adaptive genetic algorithm to determine the optimal deep learning parameters. Finally, we propose a dense layer for answer retrieval. To evaluate our model, we used the ImageCLEF 2019 VQA data set. Our model outperforms existing visual question-and-answer systems and offers a significantly higher retrieval accuracy rate.

Vehicular Ad-hoc Networks (VANETs) indeed have significant potential to enhance transportation efficiency, accident prevention, and overall comfort. They enable communication between vehicles and road infrastructure, facilitating the exchange of entertainment and traffic information. This capability can lead to safer and more efficient transportation systems while improving the overall experience for drivers and passengers. The challenging and critical security requirements in VANETs, such as access management, authentication, and privacy protection, are essential for achieving the objectives of vehicle-to-infrastructure (V2I) and intervehicle (I2V) communications. These security measures ensure the integrity, confidentiality, and availability of communication channels, thereby enhancing transportation efficiency, accident prevention, and overall comfort within VANETs. In this study, we investigate the concerns of secure access control, authentication, and privacy protection in VANETs. In response to these challenges, we propose automobile authentication methods. This protocol is built on a toll-based certificate management system utilizing RFID tags and readers deployed on automobiles and toll booths. Additionally, we introduce a vehicular mobility management method and a VANET node attachment strategy that considers quality of service (QoS) requirements. This comprehensive approach addresses key security and operational aspects of VANETs.

Dyslexia is a form of specialized learning disability that can lead to academic dissatisfaction. Detection is essential yet difficult, especially for languages like Arabic and Spanish that have straightforward orthographies. As seen by the abundance of work given in a wide range of scientific journals, the detection of dyslexia continues to be a key pursuit for the research efforts in a variety of domains. It appears that machine learning and other statistical approaches are quite useful for these tasks, such as those used in the classification of medical datasets. In this paper, we combined principle machine learning models for dyslexia detection. However, a lot of work remains to be done by hand in order to properly categorize the enormous dataset employed during training. Recent research have shown that machine learning models are effective, proving their ability to lessen the workload associated with feature engineering. However, applying the machine learning model directly to the classification task yields no statistically significant gain in performance. For better machine learning prediction, we introduce an optimal ensemble learning model based on a genetic algorithms. Dyslexia feature extraction is first, followed by a hybridization of algorithms for machine learning in step tow of the model. After that, we use an adaptive genetic algorithm for select the most optimal weight to combine different machine learning models. We conducted trials on the Dyslexia dataset to show that our proposed technique significantly increases accuracy.