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Background: Temprano ANRS 12136 was a factorial 2 × 2 trial that assessed the benefits of early antiretroviral therapy (ART; ie, in patients who had not reached the CD4 cell count threshold used to recommend starting ART, as per the WHO guidelines that were the standard during the study period) and 6-month isoniazid preventive therapy (IPT) in HIV-infected adults in Côte d'Ivoire. Early ART and IPT were shown to independently reduce the risk of severe morbidity at 30 months. Here, we present the efficacy of IPT in reducing mortality from the long-term follow-up of Temprano. Methods: For Temprano, participants were randomly assigned to four groups (deferred ART, deferred ART plus IPT, early ART, or early ART plus IPT). Participants who completed the trial follow-up were invited to participate in a post-trial phase. The primary post-trial phase endpoint was death, as analysed by the intention-to-treat principle. We used Cox proportional models to compare all-cause mortality between the IPT and no IPT strategies from inclusion in Temprano to the end of the follow-up period. Findings: Between March 18, 2008, and Jan 5, 2015, 2056 patients (mean baseline CD4 count 477 cells per μL) were followed up for 9404 patient-years (Temprano 4757; post-trial phase 4647). The median follow-up time was 4·9 years (IQR 3·3–5·8). 86 deaths were recorded (Temprano 47 deaths; post-trial phase 39 deaths), of which 34 were in patients randomly assigned IPT (6-year probability 4·1%, 95% CI 2·9–5·7) and 52 were in those randomly assigned no IPT (6·9%, 5·1–9·2). The hazard ratio of death in patients who had IPT compared with those who did not have IPT was 0·63 (95% CI, 0·41 to 0·97) after adjusting for the ART strategy (early vs deferred), and 0·61 (0·39–0·94) after adjustment for the ART strategy, baseline CD4 cell count, and other key characteristics. There was no evidence for statistical interaction between IPT and ART (pinteraction=0·77) or between IPT and time (pinteraction=0·94) on mortality. Interpretation: In Côte d'Ivoire, where the incidence of tuberculosis was last reported as 159 per 100 000 people, 6 months of IPT has a durable protective effect in reducing mortality in HIV-infected people, even in people with high CD4 cell counts and who have started ART. Funding: National Research Agency on AIDS and Viral Hepatitis (ANRS).

The liver is a crucial organ in the human body and performs vital functions essential for overall health, including metabolism, immunity, digestion, detoxification, and vitamin storage. Detecting liver diseases at an early stage poses challenges due to the liver's ability to function adequately despite partial damage. Early detection is crucial as liver diseases have significant clinical and socio-economic impacts, affecting other organ systems and requiring timely intervention to improve patient survival rates. Classical diagnostic methods for liver disorders may not always produce better results, thus necessitating more advanced and accurate diagnostic systems. Intelligent systems like predictive modeling and decision support systems, have shown promising results in recent years in disease detection and are aiding medical practitioners. In this research, a neuro fuzzy-based system integrating neural networks and fuzzy logic (FL) was implemented. Based on the risk factors present in the dataset that was used to benchmark the algorithm, this system offered a classification accuracy of 97% which is comparable with existing systems in the literature. This study creates a neuro-fuzzy system for early liver disease identification, solving diagnostic issues and offering healthcare improvements. The proposed system is validated by presenting the simulation results.

Abstract Habanero plant diseases can significantly reduce crop yield and quality, making early detection and treatment crucial for farmers. In this study, we discuss the creation of a modified VGG16 (MVGG16) Deep Transfer Learning (DTL) model-based smartphone app for identifying habanero plant diseases. With the help of the smartphone application, growers can quickly diagnose the health of a habanero plant by taking a photo of one of its leaves. We trained the DTL model on a dataset of labelled images of healthy and infected habanero plants and evaluated its performance on a separate test dataset. The MVGG16 DTL algorithm had an accuracy, precision, f1-score, recall and AUC of 98.79%, 97.93%, 98.44%, 98.95 and 98.63%, respectively, on the testing dataset. The MVGG16 DTL model was then integrated into a smartphone app that enables users to upload photographs, get diagnosed, and explore a history of earlier diagnoses. We tested the software on a collection of photos of habanero plant leaves and discovered that it was highly accurate at spotting infected plants. The smartphone software can boost early identification and treatment of habanero plant diseases, resulting in higher crop output and higher-quality harvests.

One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer.

Full video Encryption has been established as the most suitable technique to guarantee the security of digital video data during transmission but cannot be widely adopted because of its large disk space and long processing time. Therefore, this paper aims at reducing the computational space complexity using Residue Number System (RNS). MPEG IV algorithm and encryption of pixel values using the traditional RNS moduli set were employed for the encryption while a modified reverse converter was used for decryption. JAVA was used for the implementation while an evaluation was performed to compare the disk size of the cipher and deciphered videos. The scheme outperformed the existing evaluated cryptosystems in terms of cipher size and the decrypted videos increased by 49% when compared with the original video. The space computational complexity problem in full video encryption was adequately reduced using RNS. It is recommended that RNS should be used for securing video transmission and storage.

This paper aims to improve the image scrambling and encryption effect in traditional two-dimensional discrete Arnold transform by introducing a new Residue number system (RNS) with three moduli and the New Arnold Transform. The study focuses on improving the classical discrete Arnold transform with quasi-affine properties, applying image scrambling and encryption research. The design of the method is explicit to three moduli set {2n, 2n+1+1, 2n+1-1}. These moduli set includes equalized and shapely moduli leading to the effective execution of the residue to binary converter. The study employs an arithmetic residue to the binary converter and an improved Arnold transformation algorithm. The encryption process uses MATLAB to accept a digital image input and subsequently convert the image into an RNS representation. The images are connected as a group. The resulting encrypted image uses the Arnold transformation algorithm. The encrypted image is used as input at decryption using the anti-Arnold (Reverse Arnold) transformation algorithm to convert the picture to the original RNS (original pixel value). Then the RNS was used to retransform the original RNS to its binary form. Security analysis tests, like histogram analysis, keyspace, key sensitivity, and correlation coefficient analysis, were administered on the encrypted image. Results show that the hybrid system can use the improved Arnold transform algorithm with better security and no constraint on image width and size.

To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.

The encryption of digital images has become essential since it is vulnerable to interception while being transmitted or stored. A new image encryption algorithm to address the security challenges of traditional image encryption algorithms is presented in this research. The proposed scheme transforms the pixel information of an original image by taking into consideration the pixel location such that two neighboring pixels are processed via two separate algorithms. The proposed scheme utilized the Gray code number system. The experimental results and comparison shows the encrypted images were different from the original images. Also, pixel histogram revealed that the distribution of the plain images and their decrypted images have the same pixel histogram distributions, which means that there is a high correlation between the original images and decrypted images. The scheme also offers strong resistance to statistical attacks.

Residue Number System (RNS) has become one of the most preferred solutions for the implementation of distributed and ubiquitous computing platforms such as cloud, wireless adhoc networks, applications which require tolerance against errors and scalable solutions in mission critical and next generation's applications. The implementation of RNS for different applications require different approaches depending on the areas of applications. Some applications of Residue Number System described in literature are reviewed so as to illustrate the various possibilities. Distinct features such as applications, architectures and implementations were considered. In this paper, we presented a comprehensive survey on the different areas of applications, architectures and implementations of RNS to various fields of Information Technology while also including new areas of applications hitherto not covered in previous surveys. The implementations issues with different types of applications such as moduli set, forward conversion, residue arithmetic units, reverse conversion and hardware design, were discussed. Lastly, we focus on the various challenges with the use of RNS and the different solutions that exist and also discuss the future trends in RNS. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents a methodology for an image cryptosystem to ameliorate the problems of many existing digital image cryptosystems. The cryptosystem is based on a gray code number system. Design/Methodology/Approach: The gray code number system design is used to transform the pixel information of an image. Images are first binarized and based on the location of a particular pixel value an algorithm is selected for its transformation. This is later followed by a gray code transformation and reconstruction of the image (cipher). Findings: Based on the mathematical analysis performed with the encryption and decryption designs, it is being envisaged that the proposed technique will efficiently encrypt digital images. Research Limitation: This paper only presents the methodology for image cryptosystem based on the gray code number system. The designed system is yet to be implemented and tested to confirm its efficiency. Originality/Value: In the literature, a lot has been done in the area of applying unconventional number system in image encoding but this research laid emphasis on the use of gray code number system in image security. Many recent journals were considered. [ABSTRACT FROM AUTHOR]

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described, and a method, called, diagonal based feature extraction is used for extracting the features of the handwritten alphabets. This project implements this methodology using BrainNet Library. Ten data sets, each containing 26 alphabets written by various people, are used for training the neural network and 130 different handwritten alphabetical characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system, if modified will be suitable for converting handwritten documents into structural text form and recognizing handwritten names. [ABSTRACT FROM AUTHOR]