A bacterial or bone infection in the feet causes diabetic foot infection (DFI), which results in reddish skin in the wound and surrounding area. DFI is the most prevalent and dangerous type of diabetic mellitus. It will mainly occur in people with heart disease, renal illness, or eye disease. The clinical signs and symptoms of local inflammation are used to diagnose diabetic foot infection. In assessing diabetic foot ulcers, the infection has significant clinical implications in predicting the likelihood of amputation. In this work, a diabetic foot infection network (DFINET) is proposed to assess infection and no infection from diabetic foot ulcer images. A DFINET consists of 22 layers with a unique parallel convolution layer with ReLU, a normalization layer, and a fully connected layer with a dropout connection. Experiments have shown that the DFINET, when combined with this technique and improved image augmentation, should yield promising results in infection recognition, with an accuracy of 91.98%, and a Matthews correlation coefficient of 0.84 on binary classification. Such enhancements to existing methods shows that the suggested approach can assist medical experts in automated detection of DFI.

This article uses cutting‐edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty time‐consuming and often require too much labor. The number of experts capable of evaluating such structural damage is inadequate. As a result, computer vision‐based techniques for automatic damage detection have been developed. This paper’s civil infrastructure damages are classified into four damages of roads common in Indian highways and the concrete deterioration in the bridges. The convolutional neural network has become a standard tool for organizing and recognizing images. In this paper, an ensemble of three CNN models is proposed, and two are transfer learning‐based models. The proposed ensemble transfer learning model provided a validation accuracy of 87.1%.

Cancer ranks as one of the primary contributors to morbidity and mortality worldwide, standing as the second leading causeof death on a global scale. According to data from the National Cancer Registry Program of the Indian Council of Medical Research, over1300 individuals in India lose their lives daily as a result of cancer-related causes. Gastric cancer is among the top five most prevalent cancersglobally, after cancer in the lung, breast, colorectum, and prostate, highlighting the importance of accurate classification for effectivetreatment strategies. In this study, a novel approach utilizing a Quadratic Convolutional Neural Network combined with Extreme Learningand Fine-Tuning technique, a deep learning architecture specifically designed to capture intricate patterns and features within medicalimaging data. Fine tuning technique is used to enhance the model's generalization capability and adaptability to diverse datasets. Throughextensive experimentation and validation on a comprehensive dataset comprising gastric cancer images, the proposed approach achievesan impressive accuracy of 94%. The findings indicate the efficacy of the proposed approach for classifying gastric cancer. With its highaccuracy and robust performance, the developed QCNN model holds promise for assisting clinicians in accurate diagnosis and prognosis ofgastric cancer patients, ultimately contributing to improved patient outcomes and personalized treatment strategies.

In a heterogeneous distributed computing environment, developing a fault tolerance mechanism is a key research issue. Most of the existing fault tolerance approaches for distributed computing environment are post-active. These post-active approaches, predominantly involve the heartbeat strategy for fault detection and the checkpointing mechanism for fault recovery. In this proposed work, a proactive Health Aware Fault Tolerant (HAFT) scheduler using the Cox Proportional Hazard survival probability model is developed. The survival probability of the resource is estimated using resource failure data analytics and termed as health coefficient of the resource. For the job distribution classes jclass1, jclass2, and jclass3, the average improvement for makespan in HAFT algorithm over the compared algorithms are 44, 59.6, and 26.4%. In a heterogeneous environment, the job failure rate of the HAFT scheduler is ranging between 15 and 20% and it is stable for all the three jclasses. In a homogenous environment, the job failure rate of HAFT algorithm in comparison to IRP, REP and MJSP algorithm is considerably reduced by 58.6, 26.4, and 11.6%, respectively. For a failure probability higher than 0.4, the resource efficiency of HAFT algorithm on an average is 26% more than MJSP and 53% more than REP.