No abstract

Background: Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection. Methods: A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset. Results: Four benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively. Conclusions: The performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain.

No abstract

Road accidents have become a major concern for safety today. The increase in the number of vehicles has significantly increased the real-time road traffic, leading to many fatal accidents. Over the years, researchers have been trying to find solutions to enhance vehicular security in order to prevent these accidents or at least reduce their impact. This paper aims to contribute in this domain by studying the reaction time of vehicles in low visibility conditions and proposing a solution which can be implemented in the real world to prevent accidents in such scenarios. A novel algorithm is proposed which utilizes a modified form of AODV routing protocol in VANETS Cloud to alert the vehicles whenever a leading vehicle in the same lane slows down. This study can help the drivers by providing them sufficient time to react before any occurrence of an accident. The drivers would have a better idea about the vehicles in front, allowing them to make proper and informed decisions while driving in low visibility conditions.

The number of smartphone users has increased from 3.6 billion in 2016 to 6.25 billion by 2021, which shows that mobile phone usage has increased dramatically over the past few years. This is due to the development of mobile computing applications like commerce, healthcare, e-learning, etc. The use of mobile devices has resulted in an exponential rise in the amount of data generated and as a result the amount of energy consumed has increased. This is where cloud computing plays a major role. Cloud computing has transformed traditional mobile computing. The new mobile cloud not only provides on-demand services but also data storage and increased energy efficiency. Through mobile computing based on cloud computing, mobile device functions can be virtualized, reducing power consumption. In this paper, the authors survey application and potential of mobile cloud computing and present the energy-efficient ways. Also, the paper discusses development opportunities of mobile cloud computing. The research also mentions some of the major challenges in current mobile computing technology.

The term “distributed denial of service” (DDoS) refers to one of the most common types of attacks. Sending a huge volume of data packets to the server machine is the target of a DDoS attack. This results in the majority of the consumption of network bandwidth and server, which ultimately leads to an issue with denial of service. In this paper, a majority vote-based ensemble of classifiers is utilized in the Sever technique, which results in improved accuracy and reduced computational overhead, when detecting attacks. For the experiment, the authors have used the CICDDOS2019 dataset. According to the findings of the experiment, a high level of accuracy of 99.98% was attained. In this paper, the classifiers use random forest, decision tree, and naïve bayes for majority voting classifiers, and from the results and performance, it can be seen that majority vote classifiers performed better.

Virtual worlds are progressing toward a holistic abstraction of the metaverse. While there is abundant literature and synthesis on virtual worlds and related constructs, the linkages between above scholarly work and the “metaverse” are scarce. This research study addresses this gap by focusing on three specific research pursuits: a comprehensive definition of the metaverse that subsumes virtual world literature and looks at the metaverse as a sociotechnical stack, exploring the design elements of the metaverse, and a synthesis of future research direction associated with metaverse. For achieving the above goals, a hybrid research methodology comprising bibliometric analysis and a rigorous qualitative analysis of case studies across four major metaverse players with varied end goals was employed. The interpretive qualitative analysis was further distilled by mapping the emergent themes to the theoretical lens of affordances. This work presents a novel framework of metaverse design, establishing theoretical linkages between the sociotechnical fabric and applications of the metaverse.

Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.