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

The integration of the machine learning techniques in healthcare can be of huge benefit aimed at curing illness of millions of people. A lot of effort has been taken by researchers to detect and provide early-stage insights into cancer diagnosis. In the machine learning research community, various algorithms-KNN, SVM, Decision Trees and Random Forest have been applied to calculate the presence or decisiveness of cancer in correspondence with the symptoms shown by the patients. This paper aims to analyze the symptoms of the different age groups Youth, Working Class and Elderly. Tree-based algorithms like Decision Trees, Random Forest and XGBoost have been used to identify the underlying data patterns in order to calculate relative feature importances. It has been concluded that Coughing of Blood, Clubbing of Finger Nails, Genetic Risk, Passive Smoking and Snoring are the factors that are responsible for lung cancer in all the age groups in most of the cases. © 2020 IEEE.

Engineering and Technology

Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020

Symptom Analysis using a Machine Learning approach for Early Stage Lung Cancer

Automatic age estimation from the face images is a growing research interest nowadays. Various literature works have contributed towards the age detection scheme, besides only a few have resulted in providing good performance. This is due to the influence of the external factors, such as environment, lifestyle, and various expressions present in the face image. This paper proposes a deep belief network with the crow optimization algorithm for the age detection purpose. The proposed Crow Deep Belief Network (CDBN) finds the age of the person in the image through the initial training with the face features. The features for the training of the proposed CDBN are provided by the scattering transform and the Active Appearance Model (AAM). The training of the CDBN with the features provides the optimal weights used for the age detection. The experimentation of the proposed CDBN is done by four standard databases, namely the IMDB database, the Adience database, the AFAD database, and the FG-NET database based on the metrics, such as Mean Absolute Error (MAE), Accuracy of error of one age category (AEO) and Accuracy of an Exact Match (AEM). Among them, the proposed model has the minimum MAE with a value of 2.186 for FG-NET database, and maximum AEO and AEM with the values of 0.972, and 0.971, respectively for IMDB database. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

World University root node

Fulltext

Journal of Signal Processing Systems

CDBN: Crow Deep Belief Network Based on Scattering and AAM Features for Age Estimation

In recent years, peer-to-peer (P2P) network has reached popularity in file sharing as it is a distributed and decentralized network architecture. As there is no centralized authority, there arise various attacks, which lead to insecurity in the network. Thus, the security issues of the P2P networks are to be considered with more care. This paper proposes an authentication and authorization approach, named fuzzy enabled advanced encryption standard (AES)-based multi-level authentication and authorization to offer security against various kinds of attacks that occur in the P2P networks. Here, the authentication is carried out with the security factors, namely location profile, one-time password, spatial information, session password, a hashing function, and so on. Initially, the user and the server are registered in the authentication process, and then, hashing functions and AES are used to perform multi-level authorization and authentication processes. Thus, the proposed scheme improves the security of the P2P network. Using the proposed system, the hit ratio obtained is 0.9, and the success rate is 0.7666.

Springer Evolutionary Intelligence

A novel authentication and authorization scheme in P2P networking using location-based privacy

An art of taking picture of oneself through selfie camera of smartphones has become ubiquitous. It has turned to be the effective way for expressing ones emotion nowadays. Researchers are analyzing selfie images for multiple aspects like personality, psychology, behavior. Authentication of such images taken under research is doubtful to be a real selfie image. Thus, researchers attempted identifying images as selfie automatically and segregating such images over the database. There are works that helps in detecting selfies through computer vision and deep learning methods. This research defines positive aspects of such investigations to elucidate on various aspects of analysis and highlights the different points of forethought for examining self portraits along with the future research direction. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

ICTIS 2020, Pride Plaza Hotel, Ahmedabad, India

A Survey on Recognizing and Significance of Self-Portrait Images

Air pollution is a growing threat towards society and various measures are being taken recently to control it. The problem of concern which remains is the efficient prediction of air pollution to work in the right direction for reducing the same. Since the AQI follows a periodic pattern, deep learning models can be used to effectively predict the future AQI values. LSTM being a prominent time series forecasting model can be integrated with a separate DNN model to effectively add the impact of weather, temperature and other factors that can affect the future AQI values. The paper also explores the impact of having a parallel DNN to the LSTM cell instead of using the cell alone. © 2020 IEEE.

2020 International Conference for Emerging Technology (INCET)

Air Quality Index forecasting using parallel Dense Neural Network and LSTM cell

According to the World Health organization, 5% of the world population, approximately 466 million people, is deaf and/or mute or has disabling hearing loss. There is often a wall of distinction between handicapped people and normal people. We communicate to share our thoughts but for a disabled person (mainly deaf and dumb), it becomes difficult to communicate. Inability to speak is considered to be a true form of disability. For such people, sign language or Braille is the only means of communication. Sign Language is a way of communication using hand gestures. However, it becomes difficult for them to communicate with others as most don't understand sign language. Hence, we aim at bridging this communication gap, between a deaf/mute person and others, by developing a system which acts as a mediator between both. We propose a hand gesture recognition system which works in 4 steps: 1. Generate a live stream of hand gestures using web-cam. 2. Form images from the video using video frames. 3. Preprocess these images. 4. Recognize sign language hand-gestures and convert into text/audio output. The system is implemented using the concepts of image processing and neural networks. We have tested the proposed models using Kaggle dataset, our dataset and a dataset formed after combining both. We propose to eliminate the ambiguity introduced in the results by inculcating variation in the background. Most of the models give similar accuracy of the test results for both plain and cluttered background. © 2020 IEEE.

2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020

Models for Hand Gesture Recognition using Deep Learning

The data aggregation highly reduces the energy consumption in the network and hence their is a requirement for involving aggregation of data from multiple nodes. These techniques are considered as remove the crucial for successful applications in Wireless Sensor Network (WSN). This paper proposes Group Search Optimization (GSO) algorithm with Neural Network (NN) for developing the novel query-based data aggregation model. The Querying Order (QO) model in the scheme uses Query Order that can be ranked on the basis of latency and throughput. The paper aims to build up the QO model with low latency and high throughput thereby enhancing existing data aggregation techniques. Thus it can assist the network administrator to get the knowledge regarding the best queries for enhancing the performance of the sink. Further, it compares the performance of conventional methods by analyzing the latency as well as throughput of the network. It reveals that the data aggregation performance of the proposed model outperforms the conventional models as it minimizes latency and maximizes throughput.

Ad-hoc & Sensor wireless network

Optimized Query Ordering Data Aggregation Model using neural Networks and Group Search Optimization in Wireless Sensor Network

Case Study: Use of AWS Lambda for Building a Serverless Chat Application

Anatomic stage of cancer is a key factor in cancer prognosis and provides vital information in almost all types of cancer. The anatomic stage used to represent stage of cancer which shows attributes of tumor - T, number of affected lymph node -N and distant metastasis - M. Experts retrieve the anatomic stage information from clinical and pathological reports and most of these are unstructured. The main aim of the research study is to propose generalized decision-making tool for most accurate anatomic stage extraction from medical reports of different health organizations. Most previous work considered single health organization for research; extracted only TNM value, did not identify anatomic stage from TNM value; used pathology reports only. This research addresses the extraction of anatomic stage from Breast Cancer medical reports using Natural Language Processing because most of these reports are unstructured and require text processing to extract stage information from them. To detect most accurate anatomic stage, TNM information will be extracted from medical reports using NLP, machine learning technique and rule-based methodology. Average T, N and M accuracy for the rural region is 98%, 94% and 99% respectively whereas for urban region, average accuracy is 95%, 88% and 98% respectively. The average stage accuracy for rural and urban region is 93% and 85% respectively. 87% average stage accuracy achieved in both regions. Anatomic stage extraction with high accuracy in different health organizations of different regions shows that the proposed study is promising and generalized for most accurate anatomic stage extraction. © 2020, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature.

Health and Technology

Anatomic stage extraction from medical reports of breast Cancer patients using natural language processing

Skin Cancer is the most common (accounting for 40% of cancer cases globally) and potentially life-threatening type of cancers. It was diagnosed in about 5.6 million individuals last year. Automated classification of skin lesions through images has been a challenge throughout the years because of fine variability in their appearance. Deep Learning techniques exhibit potential in tackling fine-margined image-based analysis and manage to provide accurate results. The three modelling stages include data collection and augmentation, model architecture and finally prediction into 7 different types of skin cancer namely actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi and vascular lesions. A Convolutional Neural Network was fabricated (using TensorFlow) obtaining an accuracy of 81.24%. Further Transfer learning Approach was implemented in PyTorch, which yielded accuracies of 96.40%, 98.20%, 98.70% and 99.04% respectively for Wide Resnet101, Resnet50, Densenet121 and VGG19 with batch normalization, which are all trained end-to-end from images directly, to proliferate the scalability of these models and curtail initial diagnostic costs. The aim of this research paper is to render non-invasive skin cancer screening a common norm, making it simpler. © 2020 IEEE.