Volume IX, Issue III

Title– A CONCEPTUAL DESIGN APPROACH FOR WOMEN SAFETY THROUGH BETTER COMMUNICATION DESIGN

Author– Dolly Daga, Haribrat Saikia, Sandipan Bhattacharjee and Bhaskar Saha

Abstract

In this digital world, where everything is just a click away, and people adapting to the new technologies have enlarged speedily and hence, digital tools can be used proficiently for individual security or various other protection purposes. The heinous case that outraged the entire nation have wakened us to go for the safety issues and so a host of new apps have been developed to provide security systems to women via their smart watches. This paper presents Suraksha, an Android Application for the Safety of Women installed on a smart watch, this app can be activated automatically by the use of the sensors inbuilt in a smart watch or by a single click, whenever need arises. As soon as any causality occurs, identified by the changes registered by smart watch sensors the application generates an alarm can be snoozed within 10 seconds, identifies the location of place through GPS and sends a message comprising this location URL to the registered contacts and also a notification to the nearby users of the application for help. The unique feature of this application is that it is designed for smart watches and to make the sensors useful for women safety, sending the message to the registered contacts continuously for every five minutes until the ” stop button in the application is clicked and also a generation alarm of 10 seconds before sending to stop sending messages for help if wanted to. Constant site tracking info via SMS supports to catch the location of the victim quickly and can be rescued securely. Smart watches Sensors are used with the application to make the Emergency Alert Automated and rescue the Victim by the help of Digital Technology.

Index Term– Women Safety, Application, Smart Watch, Design process, User Interface, User Experience, Conceptual Design.

DOI- 10.30696/IJEEA.IX.III.2021.01-11.

Reference to this paper should be made as follows:  Dolly Daga, Haribrat Saikia, Sandipan Bhattacharjee and Bhaskar Saha, (2021), “A CONCEPTUAL DESIGN APPROACH FOR WOMEN SAFETY THROUGH BETTER COMMUNICATION DESIGN” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 3, pp. 01-11.

 

Title– FOOD-IMAGE CLASSIFICATION USING NEURAL NETWORK MODEL

Author– Alex M. Goh and Xiaoyu L. Yann

Abstract

New digital invented technologies are changing almost all the industries in the world. Food and beverage industries are one of them. Hotels, resorts and restaurants are using their good-looking images to attracts the customers. In the other side, customers are unaware about the originality of the images. To classify food images, machine learning may be a good solution. In this proposed work we have used a convolutional neural network for food-image classifications. As a convolutional neural network removes spatial features from images, so it is very efficient for food-image classifications. This proposed model may help restaurants owners for advertisement of their food to people looking for the same type of food they offer. Additionally, this model may be used for the food a distribution system. We have developed a neural network model to classify the food- image. We have also used the transfer learning technique with Inception V3.

Index Term– Machine Learning, Food-image, Data Augmentation, Convolutional Neural Network, Transfer learning, Inception-v3.

DOI- 10.30696/IJEEA.IX.III.2021.12-22.

Reference to this paper should be made as follows:  Alex M. Goh and Xiaoyu L. Yann, (2021), “FOOD-IMAGE CLASSIFICATION USING NEURAL NETWORK MODEL” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 3, pp. 12-22.

 

Title– BLOOD SUGAR DETECTION USING DIFFERENT MACHINE LEARNING TECHNIQUES

Author– Jeevan Kumar, Rajesh Kumar Tiwari and Vijay Pandey

Abstract

Blood sugar, or glucose, is the main sugar found in your blood. It comes from the food you eat, and is your body’s main source of energy. Your blood carries glucose to all of your body’s cells to use for energy. The presence of glucose helps ensure that this primary source of nutrition for infants is palatable and acceptable. There are several machine learning algorithms in order to predict blood-sugar. Blood-sugar prediction is a challenging task. The proposed algorithm may help doctors for decision making. This will also help for knowing about health and next treatment of the patient. The predictive analytics in healthcare is discussed and six different machine learning algorithms are used. Comparison and discussion of the accuracies and performances of the proposed algorithms is mentioned in this paper.

Index Term– Machine learning, big data, blood-sugar, predictive analytics, health care, deep-learning, regression, classification.

DOI- 10.30696/IJEEA.IX.III.2021.23-33.

Reference to this paper should be made as follows:  Jeevan Kumar, Rajesh Kumar Tiwari and Vijay Pandey, (2021), “BLOOD SUGAR DETECTION USING DIFFERENT MACHINE LEARNING TECHNIQUES” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 3, pp. 23-33.

 

Title– F-ALERT: EARLY FIRE DETECTION USING MACHINE LEARNING TECHNIQUES

Author– Nisarg Gupta, Prachi Deshpande, Jefferson Diaz, Siddharth Jangam, and Archana Shirke

Abstract

Natural disasters have been causing havoc since time immemorial. Forest and rural fires are one of the main causes of environmental degradation. Wireless Sensor Networks (WSN) have been fruitful in monitoring areas remotely and detecting environmental changes. By incorporation of Data Mining and Machine Learning techniques, we can build a system for early detection of fire disasters. WSNs based on Internet of Things (IoT) helps us in remote monitoring over the internet and prediction of an event as Fire/No Fire. With multi-criteria detection, multiple attributes of a forest fire are sensed by different sensing units. The temporal data from the sensors is collected and various machine learning techniques are used to analyze the patterns of data and use them to develop classification and prediction models. Model construction is done based on available data whereas model updating and prediction is in the real-time scenario. According to the data fed from sensors onset of fire can be detected and so the warning can be raised and sent to the authorities. Early detection and prediction of fire hazards help in improving firefighting resource management and reducing the damage. Preventing wildfires will be helpful in protection of natural as well as the human habitat. It helps in addressing a wider spectrum of problems, such as situational awareness and real-time threat assessment using diverse streams of data.

Index Term– visually impaired people, recognize currency bills, the mobile phone, partial images, accuracy rate.

DOI- 10.30696/IJEEA.IX.III.2021.34-43.

Reference to this paper should be made as follows:  Nisarg Gupta, Prachi Deshpande, Jefferson Diaz, Siddharth Jangam, and Archana Shirke, (2021), “ F-ALERT: EARLY FIRE DETECTION USING MACHINE LEARNING TECHNIQUES” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 3, pp. 34-43.