In the Business world, the customer satisfaction is vital. In order to improve the sales and patterned the satisfaction of customer, the preferences of the customer, quality and quantity of the product is analyzed. Various parameters are used in business aiming to improve the sales and customer satisfaction. The Agglomerative clustering, K-Means Clustering and Density Based Spatial Clustering (DBSCAN) techniques are used in this study. In the Density based clustering techniques used to fragment the customers based on the customer preference and behavior. The Intra-cluster variance is minimized with K-Means clustering. By employing the three algorithms, the customers are categorized based on who share similar interests by extracting and analyzing patterns from the customer data at hand. The evaluation reveals that K-means Clustering and Agglomerative Clustering achieves the highest silhouette score when conducting customer segmentation, surpassing other methods. The comparative analysis conducted in this study on feature-based customer segmentation provides organizations with valuable insights for implementing effective strategies in customer segmentation.

A wireless communication system is providing secured, reliable, and quality communications. The evolution of the network starts with first generation to fifth generation. Each generation have a set of standards to utilize the networks in any applications. In underwater communication, lot of data missed due to very low bandwidth, intermittent channel, and hardware problems. The sensor nodes are deployed in underwater in a long distance. The coverage area is restricted due to the bandwidth level of the networks. Compared to 4G networks, the 6G networks are to be useful for underwater and underground communication and result in decrease the data loss intern minimize the utilization of energy level. In the TAO Project, the sensors are deployed from the distance of 1.5–500 m and 10 years of data collected from the project. The root mean square error analysis depicts the lot of data loss which is occurred for each year for a particular distance. This loss of data is identified due to very low-level bandwidth and loss of intermittent channel requirements. Hence, the loss of data may be avoided by updating the generation of networks.

Brain is one of the most complex organs in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low-grade and malignant tumors or high-grade. Benign tumors are non-cancerous tumor and they do not spread to other tissues or organs. Malignant tumors are cancerous tissue and they can easily spread to other tissues or organs. Proposed system is to differentiate between normal brain and tumor brain (benign or malign). Also, the proposed system predicts brain tumor from MRI image classification system is based on extracting useful MRI features for diagnosing the medical MRI images. The benefits of using SVM is nevertheless of the image brightness or rotation of the MRI image, it also provides huge number of strong features that can be automatically prepared well to be suitable for MRI classification. Support Vector Machine (SVM) algorithm is used to predict the diseases accurately from MRI (Magnetic Resonance Imaging) scan images. SVM algorithm is the used for the purpose of classifying the image datasets and to predict the disease by itself for those matching the images to enhance a comprehensive set of quantitative measurements among several influential on various brain image databases.

Climate change, Global warming and Environmental degradation have got a very close relationship and relevance to oceanographic changes. The parameters such as temperature, currents at the surface and deep-sea, salinity and marine biological disturbances of the ocean contribute a lot to the changes of its environmental behavior. This in turn leads to unpredictable contingencies and often disastrous effects on the entire society and surrounding. That purpose is met with by the adoption of efficient data aggregation algorithms for Underwater Wireless Sensor Networks (UWSNs). Many researchers had employed UWSNs system to gather data on the factors of the ever changing sea environment. Invariably they all faced one unique problem of managing the energy source in conserving and controlling the utilization of the energy. It is absolutely essential to prolong and enhance the life-span of the network. The Segment Based Cluster with Data Aggregation (SBCDA) for Underwater Wireless Sensor Networks reduce the redundancy, overhead in the Cluster Head, packet lost, consumption of energy thereby the life-span of the network is increased.

Environmental fluctuations are continuous and provide opportunities for further exploration, including the study of overground, as well as underground and submarine, strata. Underwater wireless sensor networks (UWSNs) facilitate the study of ocean-based submarine and marine parameters details and data. Hardware plays a major role in monitoring marine parameters; however, protecting the hardware deployed in water can be difficult. To extend the lifespan of the hardware, the inputs, processing and output cycles may be reduced, thus minimising the consumption of energy and increasing the lifespan of the devices. In the present study, time series similarity check (TSSC) algorithm is applied to the real-time sensed data to identify repeated and duplicated occurrences of data for reduction, and thus improve energy consumption. Hierarchical classification of ANOVA approach (HCAA) applies ANOVA (analysis of variance) statistical analysis model to calculate error analysis for realtime sensed data. To avoid repeated occurrences, the scheduled time to read measurements may be extended, thereby reducing the energy consumption of the node. The shorter time interval of observations leads to a higher error rate with lesser accuracy. TSSC and HCAA data aggregation models help to minimise the error rate and improve accuracy.

A critical factor of underwater sensor networks (UWSN) is to maintain energy consumption at minimum, as immediate battery replacement is difficult. This is achieved by reducing duplication of data with similarity functions. The construction of optimal clustering is to avoid data loss. In this article, similarity function-based data aggregation with a Semaphore process is applied to UWSN to retain the energy level at an advantage. Sensor nodes (SNs) are clustered in a Date Palm Tree approach. The Minkowski Distance model is used in Data Aggregation Nodes (DANs) to check similar measures of readings collected from cluster members. The Semaphore concept is executed in all DANs and cluster heads (CHs) to enhance network life and regulate excessive exploitation of energy levels of the SN, DANs, and CHs. The message queue (MQ) can be used to allow the packets transferred from the DANs to the cluster heads (CHs). The proposed algorithm SBDA with similarity measures would result in better link quality, reduction in redundancy, data delay, and would control the consumption of energy.

Environmental changes in the world occurred for various reasons involving a lot of factors and paved the way for exploration in Underwater and Underground during the last decades. Most of the temporal readings of underwater and underground layers are collected by the sensor system to investigate the environmental changes. Sensors deployed in underwater consume more energy for collecting and communicating the readings to the Information Center compared with the Terrestrial Wireless Sensor Network. A huge number of redundant measurements sensed by the node consumes energy, as well as extra storage space, which is used for saving the readings. Hence, to minimize the conservation of energy and storage space, the RSiM model is proposed and it evaluates the temporal readings and predicts similar data and storage space in the repository for two time intervals. Therefore, this analysis has proved that the utilization of space for storing dissimilar data is increased and the energy used for forwarding dissimilar data to the Information Center is minimized.