Early diagnosis of skin cancer remains a pressing challenge in dermatological and oncological practice. AI-driven learning models have emerged as powerful tools for automating the classification of skin lesions by using dermoscopic images. This study introduces a novel hybrid deep learning model, Enhanced Vision Transformer (EViT) with Dens169, for the accurate classification of dermoscopic skin lesion images. The proposed architecture integrates EViT with DenseNet169 to leverage both global context and fine-grained local features. The EViT Encoder component includes six attention-based encoder blocks empowered by a multihead self-attention (MHSA) mechanism and Layer Normalization, enabling efficient global spatial understanding. To preserve the local spatial continuity lost during patch segmentation, we introduced a Spatial Detail Enhancement Block (SDEB) comprising three parallel convolutional layers, followed by a fusion layer. These layers reconstruct the edge, boundary, and texture details, which are critical for lesion detection. The DenseNet169 backbone, modified to suit dermoscopic data, extracts local features that complement global attention features. The outputs from EViT and DenseNet169 were flattened and fused via element-wise addition, followed by a Multilayer Perceptron (MLP) and a softmax layer for final classification across seven skin lesion categories. The results on the ISIC 2018 dataset demonstrate that the proposed hybrid model achieves superior performance, with an accuracy of 97.1%, a sensitivity of 90.8%, a specificity of 99.29%, and an AUC of 95.17%, outperforming existing state-of-the-art models. The hybrid EViT-Dens169 model provides a robust solution for early skin cancer detection by efficiently fusing the global and local features.

Towards tackling the phenomenon of textual information overload that is exponentially pumping with redundancy over the Internet, this paper investigates a solution depending on the Automatic Text Summarization (ATS) method. The idea of ATS is to assist, e.g., online readers, in getting a simplified version of texts for preserving their time/effort required to skim a given large body of text. However, ATS is deemed as one of the most complex NLP applications, particularly for the Arabic language that has not been intelligently developed like the other Indo-European languages. Thus, we present an extractive-based summarizer (ArDBertSum) for text written in Arabic, relying on the DistilBERT model. Besides, we propose a domain-specific sentence-clauses segmentater (SCSAR) to support our ArDBertSum in further shortening long/complex sentences. The results of our experiments illustrate that our ArDBertSum yields the best performance, compared with non-heuristic Arabic summarizers, in producing an acceptable quality of candidate summaries. These experiments have been conducted on EASC-dataset (along with our proposed dataset) to report on (1) a statistical evaluation utilizing ROUGE metrics and (2) a specific human-based evaluation. The human evaluation results revealed promising perceptions; however, further works are needed to ameliorate the coherence and punctuation of the automatic summaries.

Artificial Intelligence (AI) is expanding with colossal applications in various sectors. In the healthcare sector, it is booming to make life simpler with utmost accuracy by predicting, diagnosing and up to care with the help of Machine Learning (ML) and Deep Learning (DL) applications. Modern computer algorithms have attained accuracy levels comparable to those of human specialists in medical sciences, although computers often do jobs more quickly than people do. It is also expected that there will not be a mandate for humans to be present for the jobs that machines can do, and it is gaining the highest peak because of good trained artificial models in the medical field. ML enhances the therapeutic process and improves health by encouraging more patient participation. ML may get more accurate patient data when used with the Internet of Medical Things (IoMT) and automate message notifications that prompt patients to respond at certain times. The motivation behind this article is to make a comprehensive review of the on-going implementation of ML in medical science, what challenges it is facing now, and how it can be simplified for future researchers to contribute better to medical sciences while applying it to the practitioners' jobs easier. In this review, we have extensively mined the data and brought up systematised applications of AI in healthcare, what challenges have been faced by the experts, and what ethical responsibilities are liable to them while taking the data. We also tabulated which algorithms will be helpful for what kind of data and disease conditions will be useful for future researchers and developers. This article will provide a better insight into AI and ML for the beginner to the advanced developer and researcher to understand the concepts from the basics.

The COVID-19 pandemic has been a disastrous event that has elevated several psychological issues such as depression given abrupt social changes and lack of employment. At the same time, social scientists and psychologists have gained significant interest in understanding the way people express emotions and sentiments at the time of pandemics. During the rise in COVID-19 cases with stricter lockdowns, people expressed their sentiments on social media. This offers a deep understanding of human psychology during catastrophic events. By exploiting user-generated content on social media such as Twitter, people’s thoughts and sentiments can be examined, which aids in introducing health intervention policies and awareness campaigns. The recent developments of natural language processing (NLP) and deep learning (DL) models have exposed noteworthy performance in sentiment analysis. With this in mind, this paper presents a new sunflower optimization with deep-learning-driven sentiment analysis and classification (SFODLD-SAC) on COVID-19 tweets. The presented SFODLD-SAC model focuses on the identification of people’s sentiments during the COVID-19 pandemic. To accomplish this, the SFODLD-SAC model initially preprocesses the tweets in distinct ways such as stemming, removal of stopwords, usernames, link punctuations, and numerals. In addition, the TF-IDF model is applied for the useful extraction of features from the preprocessed data. Moreover, the cascaded recurrent neural network (CRNN) model is employed to analyze and classify sentiments. Finally, the SFO algorithm is utilized to optimally adjust the hyperparameters involved in the CRNN model. The design of the SFODLD-SAC technique with the inclusion of an SFO algorithm-based hyperparameter optimizer for analyzing people’s sentiments on COVID-19 shows the novelty of this study. The simulation analysis of the SFODLD-SAC model is performed using a benchmark dataset from the Kaggle repository. Extensive, comparative results report the promising performance of the SFODLD-SAC model over recent state-of-the-art models with maximum accuracy of 99.65%.

In the notion of communication system resource provision specifically, beam-forming is a concept of proficient utilization of the power of transmission. Network densification and massive MIMO allows us to control the power efficiency and can be effectively distributed among different users by reducing cost. We presented a practical scenario for the performance of massive MIMO and multi-small cell system to analyze the overall performance of the system. Our work is based on the resource allocation with optimal structural constraints to maintain the cost effectiveness while considering economic implications. The base stations located far away from the users receive attenuated signals and give rise to path loss, whereas the problems of inter cell interference also arise due to transmission from a base station to others cells. The performance of the cellular system can be enhanced with the combination of massive Mimo and small cells, where we simulate and also provide an analysis on practical system with optimal and low complexity beam-forming. The proposed scenario illustrates a structure with an optimal linear transmit beamforming regarding an efficient number of parameters to not lose optimality, which is extendable to designate any specific cellular network in consideration. Our approach exploited schemes with low complexity that are facilitating in complete solution formation, and tested them in various and all possible cases and scenarios.

Lung and colon (LC) cancers are among the most deadly types of cancer, often resulting in death. However, the chances of survival significantly increase with the early detection of these cancers. This study addresses the challenge of early detection of LC tumors, which is crucial for effective treatment. LC cancer is one of the most common tumors that require early detection. However, these tumors appear similar in their early stages, making it challenging for doctors to differentiate between them. To overcome this challenge, we propose an AI-based diagnostic model for detecting LC tumors. Artificial intelligence (AI) techniques have been employed to address this challenge. This study proposes two histopathological image analysis strategies for the early detection of LC cancer. The first strategy involves the diagnosis of LC cancer using decision tree (DT) and random forest (RF) networks with CNN model features, namely ResNet50, DenseNet169, and MobileNet, based on geometric active contour (GAC) and ant colony optimization (ACO) algorithms. The second strategy involves the diagnosis of LC cancer using DT and RF classifiers with the features of combined CNN, namely ResNet50-DenseNet169, DenseNet169-MobileNet, DenseNet169-MobileNet, and ResNet50-DenseNet169-MobileNet, based on the GAC and ACO algorithms. All strategies have demonstrated good results for early stage LC cancer detection. The RF network, which utilized the combined features extracted from the ResNet50- MobileNet-DenseNet169 models, demonstrated notable performance with an AUC of 99.7%, sensitivity of 99.72%, accuracy of 99.8%, precision of 99.76%, and specificity of 99.78%.