Skin cancer develops due to the unusual growth of skin cells. Early detection is critical for the recognition of multiclass pigmented skin lesions (PSLs). At an early stage, the manual work by ophthalmologists takes time to recognize the PSLs. Therefore, several “computer-aided diagnosis (CAD)” systems are developed by using image processing, machine learning (ML), and deep learning (DL) techniques. Deep-CNN models outperformed traditional ML approaches in extracting complex features from PSLs. In this study, a special transfer learning (TL)-based CNN model is suggested for the diagnosis of seven classes of PSLs. A novel approach (Light-Dermo) is developed that is based on a lightweight CNN model and applies the channelwise attention (CA) mechanism with a focus on computational efficiency. The ShuffleNet architecture is chosen as the backbone, and squeeze-and-excitation (SE) blocks are incorporated as the technique to enhance the original ShuffleNet architecture. Initially, an accessible dataset with 14,000 images of PSLs from seven classes is used to validate the Light-Dermo model. To increase the size of the dataset and control its imbalance, we have applied data augmentation techniques to seven classes of PSLs. By applying this technique, we collected 28,000 images from the HAM10000, ISIS-2019, and ISIC-2020 datasets. The outcomes of the experiments show that the suggested approach outperforms compared techniques in many cases. The most accurately trained model has an accuracy of 99.14%, a specificity of 98.20%, a sensitivity of 97.45%, and an F1-score of 98.1%, with fewer parameters compared to state-of-the-art DL models. The experimental results show that Light-Dermo assists the dermatologist in the better diagnosis of PSLs. The Light-Dermo code is available to the public on GitHub so that researchers can use it and improve it.

Automatic tracking and quantification of exercises not only helps in motivating people but also contributes towards improving health conditions. Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. Excellent trackers are available for aerobic exercises but, in contrast, tracking free weight exercises is still performed manually. This study presents the details of our data acquisition effort using a single chest-mounted tri-axial accelerometer, followed by a novel method for the recognition of a wide range of gym-based free weight exercises. Exercises are recognized using LSTM neural networks and the reported results confirm the feasibility of the proposed approach. We train and test several LSTM-based gym exercise recognition models. More specifically, in one set of experiments, we experiment with separate models, one for each muscle group. In another experiment, we develop a universal model for all exercises. We believe that the promising results will potentially contribute to the vision of an automated system for comprehensive monitoring and analysis of gym-based exercises and create a new experience for exercising by freeing the exerciser from manual record-keeping.

Emerging technologies such as Internet of Things (IoT) and blockchain have affected the digital transformation. Blockchain, on the one hand, was initially developed for the purpose of financial trading due to its robustness especially for fault tolerance and cryptographic security in addition to its decentralized architecture. IoT, on the other hand, is an open interconnected network of smart devices able to communicate simultaneously. This arises a challenge in privacy and security, specifically for the data being exchanged. To overcome this, studies have focused on the blockchain to resolve the security and privacy issues of IoT. Indeed, limited studies have proposed to assess blockchain’s viability for IoT and the associated challenges. In this paper, a conceptual model has proposed to identify the crucial factors affecting the adoption of blockchain in IoT. The model consists of four dimensions of factors that we assume will affect the adoption of the two technologies. The dimensions are: attitude-related factors, social influence related factors, data-related factors, and security-related factors. This model is validated through a survey that was distributed between professionals in blockchain and IoT. The findings show a significant impact of data-related factors on the adoption of blockchain in IoT and the intention to use them. The model can play an important role in the development of strategies, standards, and performance assessment.

The impact of online social networks on information exchange between humans has revealed the need to study the mechanisms of information diffusion. Multiple prior works have considered empirical studies and introduced new diffusion models to understand the dynamics of the diffusion process. However, the complexity of network structures and user interactions make it challenging to model the diffusion mechanisms of online social networks and to accurately predict diffusion. In this paper, we propose an information diffusion prediction model based on a physical radiation energy transfer mechanism. The aim of this model is to predict the diffusion graph of a certain contagion throughout an interest-based community. This non-parametric model can accommodate the dynamicity of online social networks because it can receive different input diffusion parameters at different diffusion contagions. With our RADiation DIFFusion (RADDIFF) model, we precisely capture the information diffusion process from both temporal and spatial dimensions and measure the level of influence initiated by certain influencers for each diffusion process. To our knowledge, this model is the first in this domain that exploits the prediction of information networks based on a physical radiation mechanism. We conduct an extensive analysis using an experiment that includes two well-known prediction diffusion models, the linear influence model (LIM) and NETINF. The results show that RADDIFF effectively outperforms both the LIM and NETINF in terms of accuracy and the quality of forecast.

Due to multitudes factors like rapid change in technology, customer needs, and business trends, the software organizations are facing pressure to deliver quality software on time. To address this concern, the software industry is continually looking the solution to improve processing timeline. Thus, the Development and Operations (DevOps) has gained a wide popularity in recent era, and several organizations are adopting it, to leverage its perceived benefits. However, companies are facing several problems while executing the DevOps practices. The objective of this work is to identify the DevOps success factors that will help in DevOps process improvement. To accomplish this research firstly, a systematic literature review is conducted to identify the factors having positive influence on DevOps. Secondly, success factors were mapped with DevOps principles, i.e., culture, automation, measurement, and sharing. Thirdly, the identified success factors and their mapping were further verified with industry experts via questionnaire survey. In the last step, the PROMETHEE-II method has been adopted to prioritize and investigate logical relationship of success factors concerning their criticality for DevOps process. This study’s outcomes portray the taxonomy of the success factors, which help the experts design the new strategies that are effective for DevOps process improvement.