Development of Energy-Efficient Routing Protocol in Wireless Sensor Networks Using Optimal Gradient Routing with On Demand Neighborhood Information

Wireless sensor networks (WSNs) consist of a number of autonomous sensor nodes which have limited battery power and computation capabilities with sensing of various physical and environmental conditions. In recent days, WSNs adequately need effective mechanisms for data forwarding to enhance the energy efficiency in networks. In WSNs, the optimization of energy consumption is a crucial issue for real-time application. Network topology of WSNs also is changed dynamically by anonymous nodes. Routing protocols play a major role in WSNs for maintaining the routes and for ensuring reliable communication. In this paper, on demand acquisitions of neighborhood information are used to find the optimal routing paths that reduce the message exchange overhead. It optimizes the number of hops for packet forwarding to the sink node which gives a better solution for energy consumption and delay. The proposed protocol combines the on demand multihop information based multipath routing (OMLRP) and the gradient-based network for achieving the optimal path which reduces energy consumption of sensor nodes. The proposed routing protocol provides the least deadline miss ratio which is most suitable to real-time data delivery. Simulation results show that the proposed routing protocol has achieved good performance with respect to the reduction in energy efficiency and deadline miss ratio

An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique

The prostate cancer is a deadly form of cancer that assassinates a significant number of men due of its mediocre identification process. Images from people with cancer include important and intricate details that are difficult for conventional diagnostic methods to extract. This work establishes a novel Automated Prostate-cancer Prediction System (APPS) model for the goal of detecting and classifying prostate cancer utilizing MRI imaging sequences.  The supplied medical image is normalized using a Coherence Diffusion Filtering (CDFilter) approach for improved quality and contrast. The appropriate properties are also extracted from the normalized image using the morphological and texture feature extraction approach, which helps to increase the classifier's accuracy. In order to train the classifier, the most important properties are also selected utilizing the cutting-edge Dragon Fly Optimized Feature Selection (DFO-FS) algorithm. Using this method greatly improves the classifier's overall disease diagnosis performance in less time and with faster processing. More specifically, the provided MRI input data are used to categorize the prostate cancer-affected and healthy tissues using the new Convoluted Gated Axial Attention Learning Model (ConGA2L) based on the selected features. This study compares and validates the performance of the APPS model by looking at several aspects using publicly available prostate cancer data

Artificial intelligence for media ecological integration and knowledge management

Information Technology’s development increases day by day, making life easier in terms of work and progress. In these developments, knowledge management is becoming mandatory in all the developing sectors. However, the conventional model for growth analysis in organizations is tedious as data are maintained in ledgers, making the process time consuming. Media Ecology, a new trending technology, overcomes this drawback by being integrated with artificial intelligence. Various sectors implement this integrated technology. The marketing strategy of Huawei Technologies Co. Ltd. is analyzed in this research to examine the advantages of Media Ecology Technology in integration with artificial intelligence and a Knowledge Management Model. This combined model supports sensor technology by considering each medium, the data processing zone, and user location as nodes. A Q-R hybrid simulation methodology is implemented to analyze the data collected through Media Ecology. The proposed method is compared with the inventory model, and the results show that the proposed system provides increased profit to the organization. Paying complete attention to Artificial intelligence without the help of lightweight deep learning models is impossible. Thus, lightweight deep models have been introduced in most situations, such as healthcare management, maintenance systems, and controlling a few IoT devices. With the support of high-power consumption as computational energy, it adapts to lightweight devices such as mobile phones. One common expectation from the deep learning concept is to develop an optimal structure in case time management.Web of Science115art. no. 22