This paper presents a design and development of an IoT-based system to real-time track elders' physical activities using accelerometer sensor data. The objective behind conducting such research is to overcome the lack of ability to monitor physical activities. Especially with the development of the socio-economic sector, the number of elders who live in isolated areas such as elderly homes have increased rapidly. In such a case with declining cognitive abilities, the healthcare of these elderly personalities becomes vulnerable. This research project fulfilled the necessity of a system to capture the vital details about those people. The Internet of Things (IoT) and cloud-based applications have become a significant part of the Information and Technology sector. Realtime monitoring is a concept tightly coupled with IoT cloud cloud-native application for this application is an excellent example of that.Further, the requirement of a low-cost system was fulfilled by using hardware components such as NodeMCU and accelerometer sensors. The designed and developed system is composed of a cost-effective wrist-worn device capable of capturing hand movement on three different arises. Hence, the detected signals are transmitted to a master node to process and recognize the activity according to the detected signal. Another significant aspect of the project is using machine learning techniques to recognize the four different activities such as walking, sitting, sleeping, and standing. The use of supervised machine learning techniques is evaluated to overcome the barriers of real-time activity recognition. Further different supervised machine learning algorithms were used and evaluated, which were extracted from existing literature. The project was conducted while accomplishing the machine learning life cycle stages, and it has significantly benefitted from generating highly accurate final results for the overall system. Further different supervised machine learning algorithms were used and evaluated, which were extracted from existing literature. The supervised machine learning algorithm Decision Tree Classifier used for this study. Using the Decision Classifier Tree algorithm succeeded in gaining more than 80% of model accuracy. Since the research topic comes under a classification type-oriented problem, the testing process of the model has been done using the confusion matrix for the trained model.

Quality of electricity with continuity is the reliability of the power system which is inversely proportional with the duration of power supply interruption. It depends on some expected or unexpected faults/failures on the systems, speed of protecting systems, preventive maintenance, and motivation of technical staffs. The detailed study of the distribution system is more crucial as its reliability is the concern of utility’s fame, service, customers’ satisfactions and reflects to the overall revenue. The relevant articles from the various sources has been collected and analyzed different reliability indices with their significance. Also, to realize the methodology related with reliability analysis, a comparative study among its different components has been carried out and the best techniques for maintaining system reliability are suggested.

In contemporary times startup is a very popular and growing entity, especially for the software industry as the technology field continues to grow. However, the failure rate of software startups is extremely high, and many reasons are behind this failure. There are many different variables behind the success or failure of a company. The purpose of this study is to mainly identify the possible reasons behind why the startups can’t reach the promise they are presumed to reach. To identify the reasons, company and employees of the company data were collected and analyzed using sentiment analysis and statistical correlation. Based on the analysis, some constructive suggestions were proposed accordingly. The discussion will enable the company employees and employers to bring out a transparent relation to ensure productive and satisfactory work environment. All the collected data for the study and the codes that were used to analyses the findings are attached below [28].

Visual impairment is where the person either can’t see or his vision has weakened to large extent. There is no alternative technique for visually impairment, but to some extent it can be trim down with devices, smart sticks and sensors. Although many techniques are there for helping out through electronic travelling aid, cost effective and minimum hardware solution was the expectation by impaired. The device which can identify and classify the object ahead of impaired person is needed so that person can be prevented from the accident. In this paper, a unified model of YOLO (You Only Look Once) is used for detection of object ahead of camera. The proposed model is based on phenomena of detecting small object and good detection speed of yolov3 makes system more robust. Once detected, labeled objects name is converted from text to speech, so that blind person can be alerted from colliding with obstacles. This paper is one step in the direction to help them by exactly classifying, detecting and localizing target object along with providing voice based guideline. The proposed model has proved accuracy in many real time scenes.

The research is concerned with the development of a mathematical model for estimating Intelligent Quotient of human above 65years. The model was optimized to know whether we have maximum or minimum human IQ level. However, the optimization result showed a saddled point which indicates that there is no minimum or maximum human IQ in life. This result implies that there is no particular IQ level that any human above 65years cannot attain when there is an enabling environment for adult education and other adult related trainings geared towards sharpening IQ skills of our retired and aged people. Similarly the IQ model was also validated with data from real life. And the outcome of the validation gave a correlation coefficient of 0.990405. This implies that our model is approximately 99% in agreement with real life data used, thus shows that the model is a standard measure for estimating IQ of human above 65years.