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This paper presents a skin cancer classification model that combines a pre-trained Convolutional Neural Network (CNN) with a nature-inspired feature optimization algorithm. A custom dataset comprising both malignant and benign skin cancer microscopic illustrations is derived from the ISIC dataset of dermoscopic images. Several preprocessing steps are performed on the input pictures, such as histogram equalization, gamma correction, and white balance adjustment, to improve visibility, quality, and make color corrections. Deep feature extraction and pattern recognition are conducted on both enhanced and original dataset images using the pre-trained CNN model EfficientNetB0. As a result of fusing these features, the model can capture rich details from both dataset versions at the same time. Ant Colony Optimization (ACO), a nature-inspired feature selection algorithm is applied to perform model optimization by keeping the most relevant features and discarding the unnecessary ones. The optimized feature vector is then used with various SVM classifier kernels for the skin cancer classification task. The maximum achieved accuracy of the proposed model exceeded 98% through CB-SVM while maintaining an excellent prediction speed and reduced training time. [ABSTRACT FROM AUTHOR]

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques. [ABSTRACT FROM AUTHOR]

Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for extracting the alphabets, while diagonal-based extraction methods are implemented in online systems. Moreover, our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text recognition. To this end, we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline mediums. Subsequently, we carried out detailed experiments for character recognition, as detailed in the results. The proposed CNN model outperformed as compared to previously published approaches. [ABSTRACT FROM AUTHOR]

Today, due to the pandemic of COVID-19 the entire world is facing a serious health crisis. According to the World Health Organization (WHO), people in public places should wear a face mask to control the rapid transmission of COVID-19. The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places. Therefore, it is very difficult to manually monitor people in overcrowded areas. This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places, by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of COVID-19. This paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or unmasked. The proposed framework is built by fine-tuning the state-of-the-art deep learning model, Faster-RCNN, and has been validated on a publicly available dataset named Face Mask Dataset (FMD) and achieving the highest average precision (AP) of 81% and highest average Recall (AR) of 84%. This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked faces. Moreover, this work applies to real-time and can be implemented in any public service area. [ABSTRACT FROM AUTHOR]

Featured Application: Diabetes is a common chronic disorder defined by excessive glucose levels in the blood. A good diagnosis of diabetes may make a person's life better; otherwise, it can cause kidney failure, major heart damage, and damage to the blood vessels and nerves. As a result, diabetes classification and diagnosis are vital tasks. By using our proposed methodology, clinicians may obtain complete information about their patients using real-time monitoring. To gain new insights, they can combine historical information with current data, making it easier for them to perform more thorough and comprehensive treatments than before, and they will be able to provide proactive care, which will help to improve health outcomes and reduce hospital re-admissions. Diabetes is a long-term illness caused by the inefficient use of insulin generated by the pancreas. If diabetes is detected at an early stage, patients can live their lives healthier. Unlike previously used analytical approaches, deep learning does not need feature extraction. In order to support this viewpoint, we developed a real-time monitoring hybrid deep learning-based model to detect and predict Type 2 diabetes mellitus using the publicly available PIMA Indian diabetes database. This study contributes in four ways. First, we perform a comparative study of different deep learning models. Based on experimental findings, we next suggested merging two models, CNN-Bi-LSTM, to detect (and predict) Type 2 diabetes. These findings demonstrate that CNN-Bi-LSTM surpasses the other deep learning methods in terms of accuracy (98%), sensitivity (97%), and specificity (98%), and it is 1.1% better compared to other existing state-of-the-art algorithms. Hence, our proposed model helps clinicians obtain complete information about their patients using real-time monitoring and can check real-time statistics about their vitals. [ABSTRACT FROM AUTHOR]

To see if HHV-6 may be a cause of infertility, researchers looked at 18 men and 10 women who had unexplained critical fertility and had at least one prior pregnancy. HHV-6 DNA was discovered in both infertile and fertile peripheral blood mononuclear cells (PBMC) (12 and 14%, respectively); endometrial epithelial cells from 4/10 (40%) infertile women were positive for HHV-6 DNA; this viral DNA was not found in the endometrium of fertile women. When endometrial epithelial cells were cultivated, they produced viral early and late proteins, suggesting the existence of an infectious virus. Endometrial HHV-6 infection creates an aberrant NK cell and cytokine profile, resulting in a uterine domain that is not favorable to conception, according to the findings. To corroborate the findings, studies of extra fertile and barren women should be done. Semen samples were taken from 18 guys who visited the Government General Hospital Guntur's infertility department because they were having reproductive issues with their partners. Herpes virus DNA has been discovered in the sperm of symptomatic fertile and infertile male patients on rare instances. Furthermore, researchers must investigate the role of viral diseases in male infertility. [ABSTRACT FROM AUTHOR]

Image classification is a core field in the research area of image processing and computer vision in which vehicle classification is a critical domain. The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security, traffic analysis, and selfdriving and autonomous vehicles. The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional, and handcrafted means of solving image analysis problems. In this paper, a combination of pre-trained CNN GoogleNet and a nature-inspired problem optimization scheme, particle swarm optimization (PSO), was employed for autonomous vehicle classification. The model was trained on a vehicle image dataset obtained from Kaggle that has been suitably augmented. The trained model was classified using several classifiers; however, the Cubic SVM (CSVM) classifier was found to outperform the others in both time consumption and accuracy (94.8%). The results obtained from empirical evaluations and statistical tests reveal that the model itself has shown to outperform the other related models not only in terms of accuracy (94.8%) but also in terms of training time (82.7 s) and speed prediction (380 obs/sec). [ABSTRACT FROM AUTHOR]

For machine learning techniques to be used in early keratoconus diagnosis, researchers aimed to find and model representations of corneal biomechanical characteristics from exam images generated by the Corvis ST. Image segments were used to identify and convert anterior data into vectors for representation and representation of apparent posterior surfaces, apparent pachymetry, and the composition of apparent anterior data in images. Chained (batch images) and simplified with wavelet, the vectors were also arranged as 2D histograms for deep learning use in a neural network. An interval of 0.7843 to 1 and a significance level of 0.0157 were used in the scoring, with the classifications getting points for being as sensitive as they could be while also being as precise as they could be. In order to train and validate the used data from examination bases in Europe and Iraq, in grades I to IV, researchers looked at data from 686 healthy eyes and 406 keratoconus-afflicted eyes. With a score of 0.8247, sensitivity of 89.49%, and specificity of 92.09%, the European database found that apparent pachymetry from batch images applied with level 4 wavelet and processed quickly had the highest accuracy. This is a 2D histogram of apparent pachymetry with a score of 0.8361, which indicates that it is 88.58 percent sensitive and 94.389% specific. According to the findings, keratoconus can be diagnosed using biomechanical models. [ABSTRACT FROM AUTHOR]

Emotions are a fundamental part of human behavior and can be stimulated in numerous ways. In real-life, we come across different types of objects such as cake, crab, television, trees, etc., in our routine life, which may excite certain emotions. Likewise, object images that we see and share on different platforms are also capable of expressing or inducing human emotions. Inferring emotion tags from these object images has great significance as it can play a vital role in recommendation systems, image retrieval, human behavior analysis and, advertisement applications. The existing schemes for emotion tag perception are based on the visual features, like color and texture of an image, which are poorly affected by lightning conditions. The main objective of our proposed study is to address this problem by introducing a novel idea of inferring emotion tags from the images based on object-related features. In this aspect, we first created an emotion-tagged dataset from the publicly available object detection dataset (i.e., "Caltech-256") using subject evaluation from 212 users. Next, we used a convolutional neural network-based model to automatically extract the high-level features from object images for recognizing nine (09) emotion categories, such as amusement, awe, anger, boredom, contentment, disgust, excitement, fear, and sadness. Experimental results on our emotion-tagged dataset endorse the success of our proposed idea in terms of accuracy, precision, recall, specificity, and F1-score. Overall, the proposed scheme achieved an accuracy rate of approximately 85% and 79% using top-level and bottom-level emotion tagging, respectively. We also performed a gender-based analysis for inferring emotion tags and observed that male and female subjects have discernment in emotions perception concerning different object categories. [ABSTRACT FROM AUTHOR]