One of the major health concerns for human society is skin cancer. When the pigments producing skin color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigments may appear similar in appearance. Hence, early detection of lesions (which form the base of skin cancer) is definitely critical and useful to completely cure the patients suffering from skin cancer. Significant progress has been made in developing automated tools for the diagnosis of skin cancer to assist dermatologists. The worldwide acceptance of artificial intelligence-supported tools has permitted usage of the enormous collection of images of lesions and benevolent sores approved by histopathology. This paper performs a comparative analysis of six different transfer learning nets for multi-class skin cancer classification by taking the HAM10000 dataset. We used replication of images of classes with low frequencies to counter the imbalance in the dataset. The transfer learning nets that were used in the analysis were VGG19, InceptionV3, InceptionResNetV2, ResNet50, Xception, and MobileNet. Results demonstrate that replication is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. It is inferred that Xception Net outperforms the rest of the transfer learning nets used for the study, with an accuracy of 90.48. It also has the highest recall, precision, and F-Measure values.

Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.

In order to achieve a sustainable mix design, this paper evaluates self-consolidating green concrete (SCGC) properties by experimental tests and then examines the design parameters with an artificial intelligence technique. In this regard, cement was partially replaced in different contents with granulated blast furnace slag (GBFS) powder, volcanic powder, fly ash, and micro-silica. Moreover, fresh and hardened properties tests were performed on the specimens. Finally, an adaptive neuro-fuzzy inference system (ANFIS) was developed to identify the influencing parameters on the compressive strength of the specimens. For this purpose, seven ANFIS models evaluated the input parameters separately, and in terms of optimization, twenty-one models were assigned to different combinations of inputs. Experimental results were reported and discussed completely, where furnace slag represented the most effect on the hardened properties in binary mixes, and volcanic powder played an effective role in slump retention among other cement replacements. However, the combination of micro-silica and volcanic powder as a ternary mix design successfully achieved the most improvement compared to other mix designs. Furthermore, ANFIS results showed that binder content has the highest governing parameters in terms of the strength of SCGC. Finally, when compared with other additive powders, the combination of micro-silica with volcanic powder provided the most strength, which has also been verified and reported by the test results.

Clustering in wireless sensor networks plays a vital role in solving energy and scalability issues. Although multiple deployment structures and cluster shapes have been implemented, they sometimes fail to produce the expected outcomes owing to different geographical area shapes. This paper proposes a clustering algorithm with a complex deployment structure called radial-shaped clustering (RSC). The deployment structure is divided into multiple virtual concentric rings, and each ring is further divided into sectors called clusters. The node closest to the midpoint of each sector is selected as the cluster head. Each sector’s data are aggregated and forwarded to the sink node through angular inclination routing. We experimented and compared the proposed RSC performance against that of the existing fan-shaped clustering algorithm. Experimental results reveal that RSC outperforms the existing algorithm in scalability and network lifetime for large-scale sensor deployments.

Mandible defects and its deformities are serious complications and its precise reconstruction is one of the most challenging tasks in oral maxillofacial surgery. The commercially available standard mandible implants are manually bended before surgery to custom fit the patient’s jaw. A slight mismatch in the plate and bone alignment may result in the implant failure. However, with the integration of computer-aided design, rapid prototyping, and advanced imaging systems (computed tomography or magnetic resonance imaging), it is possible to produce a customized mandible implant that can precisely fit the patient’s jaw. The aim of this article is to compare a new design of customized mandible implant (sinewave plate) and compare it with the commonly used straight implant design. The finite element–simulated results reveal that the commonly used straight reconstruction plates are more prone to loosening of the screws due to its higher strain concentration on the screw hole when compared to newly designed sinewave reconstruction plate. Moreover, the straight plate is more sensitive to the chewing load variations and develops almost 20% increase in the stresses when compared to sinewave plate. The study reveals that the sinewave reconstruction plate can significantly enhance the stability and safety of the mandible implant.