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Human health are to be monitored for glucose level, blood pressure level and intimated at the right time for taking the necessary pills or food. Defense forces, Diabetes victim and victim with heart attack must be given alert on their BP level and accordingly they can react for taking the appropriate pill or food. A victim may go low in his /her BP level while sleeping more vulnerable to this kind of situation which requires salient tracking and alerting system. Smart polymers combined with Biomimetic Elastomers forms the Injectable Polymer hydrogels. Moreover, many IoT devices have been discovered. A study throws light on a fact that most of continuous tracking observations, human skin suffers a bone disorder called Osteoporosis which ultimately leads to deformation of bones. But hydration loss must be reducible for a bi tissue to be discovered; thereby bypassing the drawbacks caused due to standalone IoT sensors. Injectable Polymer hydrogels is already used for plants and animals for avoiding damage to them during research experiments. The polymers must be molded to the required consistency for synthesizing wearable sensors eliminating health hazards. The conventional IoT system is substituted with the biotechnological solution to enhance continuous monitoring and alert system for victim and elderly aged people. Injectable Polymer hydrogels an artificial skin protein constitutes the composition of nutrients and proteins available in the form of powder can be used for manufacturing the wearable devices to maintain the conductivity range in such a way that it does not affect the human skin. It is proposed to synthesize the artificial skin gel in a relative state that it produces accurate results with sensor data transfer also. The solution proposed here provides the safe usage of irremovable monitoring and tracking of health status.

Estimating the location of a movable object is highly necessary for providing context-aware services in an indoor environment. As Global Positioning System (GPS) is not appropriate for indoor positioning, Wireless Local Area Network (WLAN) seems to be the choice due to its ubiquitous nature. The localization task based on wireless signals involves several challenges. This paper proposes a cost-effective Wi-Fi-based location estimation and navigation architecture which employs the existing IEEE 802.11 infrastructure for facilitating indoor positioning, providing business solutions, monitoring health care and guiding navigation. A statistical regression model is built on the recorded Received Signal Strength (RSS) dataset using Multi-output Least Square Support Vector Machine (M-LS-SVM) regression which infers the locality of a mobile device. The information from the radio map helps in improving the performance. The proposed M-LS-SVM technique is compared with various regression models for different kernels.

The scheduling algorithms for IEEE 802.16 standard are designed with the predominant goals of throughput optimization, ensuring fairness and Quality of Service (QoS) provisioning. In this work, enhancements are proposed to the existing Weighted Fair Queuing (WFQ) and Deficit Weighted Round Robin (DWRR) scheduling algorithms to efficiently utilize the unused units. In WFQ, additional units may be assigned to a queue, thus reducing the service rate. Instead in Enhanced WFQ (EWFQ), multiple queues are served in a round by effectively utilizing the unexploited units. In DWRR, a queue is not serviced if the size of the packet at the front of the queue exceeds the available quantum. Enhanced DWRR (EDWRR) checks for packets with sizes less than the Deficit Counter (DC), sorts the queue and services a smaller packet in the current round. Further, if the queue that is currently served becomes empty, the DC is transferred to the ensuing active queue instead of making it zero. This helps in servicing more number of packets in a round. The proposed scheduling schemes are proficient in servicing specific traffic flows.

In this contemporary world, almost every business and companies deploy machine learning methods for taking exemplary decisions. Predictive Customer analytics supports to accomplish of momentous insights from customer data. This trend is more distinct in the telecommunication industry. The most challenging issue for telecom service providers is the increased churn rate of customers. In Existing works, combining various classification algorithms to design hybrid algorithms as well as ensembles have reported best results compared to single classifiers. But, selecting classifiers for creating an effective ensemble combination is challenging and are still in investigation. This work presents a various type of ensembles such as Bagging, Boosting, Stacking and Voting to combine with different Base classifiers in a systemic way. Experiments were conducted with benchmark Telecom customer churn UCI dataset. It is inferred that; Ensemble learners outperform single classifiers due to its strong classifying ability. The models designed were affirmed using standard measures such as AUC, Recall, F-Score, TP-rate, Precision, FP-Rate and Overall Accuracy. This way of combining multiple classifiers as an ensemble achieves the highest accuracy of 97.2% from bagged and boosted-ANN.

Customer churn is an important issue and major concern for many companies. This trend is more noticeable in Telecom field. Telecom operators requires an essential proactive method to prevent customer churn. The existing works fails to adopt best feature selection for designing model. This works contributes on developing churn prediction model, which helps telecom operators to identify the customers who are about to churn. The significance for the recall evaluation measure, which actually solves the real-time business problem is highlighted. The prominent goal of this churn analysis is to perform binary classification with customer records and figure out who are likely to cancelled in the future. Fifteen machine learning methods with different parameters are employed. The performance of the model is evaluated by various measures like Accuracy, Recall, Precision, F-score, ROC and AUC. Our aim in this work is to produce highest recall value which has a direct impact on real-world business problems. Based on experimental analysis, we observed that Decision Tree model 3 outperforms all other models.