MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

School of Computer Engineering & Technology

Cognitive computing is an intelligent system that converses with and mimics the human being in a natural form by learning at scale, reasoning with purpose. Cognitive computing is the third era of computing and now cognitive computing has attracted considerable attention in both academia and industry. Machines and humans’ intelligence gets combined to solve the most complex problems of the world. Complicated problems can be solved by computing framework without intervention of humans. Natural language processing with emotion analysis, artificial intelligence (AI), machine learning, neural networks are building blocks of cognitive computing process to tackle problems as the way human beings do. Nowadays, advance technologies adapt cognitive computing in many areas to assist human experts in smart decision making for the betterment of businesses. Current technology expectations are to make human life better and to help them work in better ways. Nowadays, there is explosive data growth, business conditions are also changing rapidly so intelligent, hassle-free, and enhanced interactions amongst human beings and technology can be effectively addressed by cognitive systems. AI is in use in many apps like the Alexa: Amazon voice assistant, Netflix and Amazon algorithms which recommend the next to watch or buy. Some examples of personal assistants that uses cognitive computing are Alexa, Siri, Google assistant, and Cortana. Advancement of technology and its adoption in the public and private sectors will greatly affect the future of cognitive computing due to technology evolutionary paths and trends. Cognitive systems must be adaptive, interactive, iterative, and stateful and contextual in commercial and widespread applications. Some of the applications that can use cognitive computing to gain benefits from this type of technology are cognitive computing for changing business values, Financial and Investment firms, Healthcare and veterinary medicine, Travel and Tourism, Health and wellness, Education and learning, Agriculture, Communication and network technology. © 2021 Elsevier Inc. All rights reserved.

World University root node

Engineering and Technology

Cognitive Computing for Human-Robot Interaction: Principles and Practices

Introduction to cognitive computing and its various applications

In the current era where ample data is generated every second and available to every individual in the form of information, it is easy to gain knowledge from this information but a systematic and methodical approach is required when it comes to skill development. Online and on campus programs help in gaining knowledge but the skills to apply this knowledge needs to be developed gradually through appropriate teaching pedagogy which helps in practicing higher order thinking skills as proposed by Bloom and well known as Bloom’s Taxonomy. In conventional teaching learning approach adopted for online and in-person classroom sessions, the transmission of knowledge takes place most of the time where learner’s involvement may be quite less. So from the perspective of improvement in learner’s involvement in teaching learning process, active learning methods play a significant role and hence practiced widely in recent times. Variety of methods are recommended by scientists and Education experts to improve upon assimilation of knowledge for problem solving. Depending upon the nature of the problem the learner either needs to use one of the lower order thinking skills like apply or higher order thinking skills like analyse, evaluate and create as defined by Bloom. The study presented in this paper discusses case studies of practicing active learning techniques and developing problem solving skills through these techniques. The consolidated feedback analysis for the active learning techniques practiced show that the 96% learners have agreed for clarity of understanding, 92.72% learners have shown confidence in application of concepts and 97.27% participants have shown improved satisfaction in learning. Qualitative testimonies suggests the promising strength of PBL and effectiveness of it in improvement of problem solving skills. The study encourages the effective use of active learning techniques for skill development which is need of time. © 2021, Rajarambapu Institute Of Technology. All rights reserved.

Journal of Engineering Education Transformations

Role of Active Learning Techniques in Development of Problem Solving Skills

The past few decades have witnessed an increase in dietary ailments, majorly caused due to unhealthy food habits. According to experts, mobile-based diet monitoring and assessment systems can capture real-time images of various food items as well as analyze the nutritional content present in it and can be very convenient to use and assist in improving food habits. This can help people lead a healthier lifestyle. This proposed model provides an innovative system that can automatically estimate various food attributes like the nutrients and ingredients by classifying the food image that is given as input. The approach involves the use of different types of deep learning models for accurate food item identification. Apart from image recognition and analysis, the food ingredients, nutrients and attributes are obtained and estimated by extracting words which are semantically related from a large collection of text, accumulated over the Internet. Experimentation has been performed with the Food-101 dataset. The proposed system assists the user to obtain the nutritional value of the food item in real-time which is effective and simple to use. The proposed system also provides supporting features such as food logging, calorie tracking and healthy recipe recommendations for self-monitoring of the user. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

A Model for Automated Food Logging Through Food Recognition and Attribute Estimation Using Deep Learning

Image Captioning is one of the emerging topics of research in the field of AI. It uses a combination of Computer Vision (CV) and Natural Language Processing (NLP) to derive features from the image, use this information to identify objects, actions, their relationships, and generate a description for the image. It is most important concept in artificial intelligence applied in the fields like aid to the blind, self-driving cars, and many more. This paper we demonstrates a concise state of art image captioning and its method for caption generation using deep learning concepts. We also determine the approach for image caption generation using Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) model in deep learning framework. Using this approach system intelligent enough to create sentences for images. It uses the encoder-decoder architecture, where CNN is used for image vector generation and LSTM is used for the generation of a logical sentence using the NLP concepts. Finally, we evaluate the proposed system experimental analysis with numerous existing systems and show the effeteness of system. © 2021 IEEE.

2021 International Conference on Emerging Smart Computing and Informatics, ESCI 2021

Image captioning methods and metrics

Social media has been an unavoidable part of our life over the years. As it is getting popular, the number of social media bots are also increasing. Social media bots are the artificial agents who imitate as a human on social media. They are intended to like, retweet the posts which eventually can tamper with the genuineness of the trend. They can also be a menace to democracy as they can falsely influence people. Social media bots can be used for cyberbullying, terrorist activities, gaining fame, spreading wrong information, restricting freedom of speech, spamming. To detect social media bots on Twitter, we utilized metadata of Twitter profiles and applied a unique feature selection method, and also explored the power of ensemble learning to make a robust classifier. © 2021 IEEE.

Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021

Enhanced Twitter bot detection using ensemble machine learning

Indian climate which primarily consists of four seasons and the majority of agriculture in India is practised in monsoon which is rainfed agriculture. The lack of irrigation makes India a potent place for the experiment of efficient water irrigation. Internet of Things (IoT) have emerged has ubiquitous technology which is being used in many fields. Machine Learning has proven to be helpful in data-intensive tasks. IoT and machine learning would prove helpful in agriculture. This paper proposes a system based on the research of machine learning and IoT to increase the efficiency in irrigation. With the help of data gathered through sensors and using machine learning algorithm the amount of water the needed for irrigation is calculated and used for irrigation. © 2021, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Smart Irrigation Using Decision Tree

The maintaining of the Quality of Service(QoS) parameter in wireless sensor networks (WSN) is becoming a challenging task. The limitations are like achieving QoS parameters in a distributed network, constraints in sensing parameters, natural disasters and battery life of sensor nodes need to overcome. Here in this paper few of the QoS parameters are analysed to increase the lifetime of sensor networks by partitioning a network into various sectors. A novel optimized sectoring scheme is proposed for optimal election of sector head (SH) for a given network. A Cat Swarm Optimization based Energy-Efficient Reliable sectoring Scheme with prediction algorithms (P-CSO-EERSS) is also proposed in this paper. The use of prediction algorithms is for early detection of node lifetime to improve the overall lifespan of a network. Along with this QoS parameters such as packet delivery ratio and energy consumption are analysed and compared with existing clustering algorithms. According to implementation results, it has been shown that the prediction based CSO-EERSS algorithm returns better QoS parameters as compare to existing cluster schemes.