An Efficient Routing Algorithm for Optimizing Energy Conservation and Improved Scalability in WSN

Wireless Sensor Network (WSN) is a familiar technique to develop various real-world applications through sensor nodes on wireless networks. Energy efficiency and scalability are the important challenges in WSN, along withsome other parameters such as reliability, availability, and security. The nodes’ path and communication network have been decided based on routing techniques among the network nodes. Classical routing algorithms have improved the efficiency and accuracy of WSN nodes, but it works only in the limited range. The number of nodes is increased in WSN, and security of data transmitted has a lot of limitations in routing. This problem has been considered for a long time, but the level of improvement is only within a short range, not for larger networks and high-volume nodes. This problem has been overcome by an efficient routing algorithm SEERA (Scalable Energy Efficient Routing Algorithm). Familiar routing protocol techniques such as LEACH (Low Energy Adaptive Clustering Hierarchy), Multi-LEACH, CELL-LEACH when compared with this algorithm shows that SEERA provides better security, energy efficiency and scalability. Also, the proposed algorithm uses hybrid MAC protocols along with the clusters communicated in Intra mode on the network. This research paper summarizes the results of SEERA withmore scalable nodes on WSN and also improved their energy efficiency among all nodes

Product Recommendation System using Scalable Alternating Least Square Algorithm and Collaborative Filtering using Apache Spark in E-Commerce

Recommender System is tremendously used in numerous spaces, such as e-commerce and entertainment to enhance businesses by increasing the chance of sales. Earlier researches have focused more on traditional Machine Learning (ML) and Artificial Intelligence (AI)-based approaches. Developing a scalable recommender system has been challenging concerning high availability and fault tolerance. The traditional collaborative filtering approach used with the recommender system also faces challenges due to the absence of explicit product ratings by the customer and the cold start problem. We have proposed a scalable Alternating least square (ALS) and collaborative filtering-based approach for the recommender system. The experimental results of the proposed hybrid approach show improved performance as compared with the traditional approach

Cloud-based smart water quality monitoring system using IoT sensors and machine learning

Low water quality is a major concern in urban as well as rural areas. Consumption of contaminated water leads to several health hazards. Early water quality detection can prevent most of such health-related issues. Parameters such as conductivity, pH, nitrate, biochemical oxygen demand, fecal coliform are significant parameters in deciding the quality of water. These parameters which are collected from groundwater samples at different places are highly correlated to each other. Therefore, machine learning algorithms are used for classification. The data collected from sensors are further analyzed using a cloud-based environment Ubidots to support distributed computing. The cloud environment is connected to display units and mobile devices. To predict the quality of water it is necessary to check the values associated with the quality attributes and for that reason, a decision tree classification model is used. The dataset is broken into subsets that have decision nodes and leaf nodes to decide classifications. The IoT based sensors are deployed in the water tank to measure the quality parameters which are further sent to the cloud. The proposed framework predicts the water quality and assesses the performance of the decision tree classifier. Decision Tree is used to infer decision rules based on various parameters read through sensors

Sentiment Analysis for E-Commerce Products Using Natural Language Processing

Sentiment analysis is one of the ways to evaluate the attitude of consumers towards products and services. E-commerce businesses have grown to a larger level in recent years. Customers' opinions and preferences are collected to analyze them further to boost online businesses. Collecting real-time structured and unstructured data and performing sentiment analysis on them are challenging and need to be addressed. We have used PySpark, and resilient distributed dataset (RDD) based sentiment analysis using Spark NLP to address scalability and availability issues in sentiment analysis on the e-commerce platform. We have also used FLASK-based Restful APIs and Scrapy for web scrapping to collect useful data from an e-commerce site. Our findings indicate that the proposed method of Natural Language Processing (NLP) for e-commerce products in real-time has enhanced efficiency in terms of scalability, availability, and faster data collectio