Selecting a focus area and topic for conducting your research and writing thesis/dissertation can be a problematic process-given constant transformation of academic landscape. This is the reason our team has investigated strategies, and come up with the best ones that you can utilize to select the most suited topic for yourself and ensure perfect trajectory to academic success. Our experts collaborated to categorically define six steps that can set you in the right direction. To read more about the process kindly check "Starting Research and Selecting Topic" section in our knowledge base.
We sent out invitation to 134 PhDs on-board with us, to submit the most valuable research topics in CS and IT for the year 2020. Our QA Team received more than thousand topics, which were then thoroughly discussed in expert groups to funnel out a list for our valuable readers. These topics are based mainly on the recent trends in awarded grants and national agendas, as well as potential focus areas that are expected to rise exponentially in next five years. So, please pay close attention to the topics, our team has also defined basic introduction and a strategic overview for each topic in the list, which can be provided to you upon request; kindly feel free to contact us in that regard using the contact provided at the end.
We have divided the list of topics into further focus areas to make the selection easier for you; however, the topics are interdisciplinary and in many ways the focus areas are overlapping. These topics are on high priority by reputable institutes. We strongly recommend to use this list as a source of inspiration. Copying the topics, as it is, is not recommended; although, you can change them to add your own flavor.
In addition, we provide some valuable resources with each focus area that may allow you to dig deeper and shape your understanding of the research topics.
Starting with Machine Learning and AI, a series of posts will be published for the best topics in the following focus areas:
Machine Learning and AI
Computer and Network Security
Big Data and IoT
Information Systems (Cloud and Database Management)
Health IT and Bioinformatics
Robotics
Visual Computing (AR/VR/CGI)
Software Theory (OS and Architecture)
Machine Learning and AI
Topics:
Neural Generative Models and Representation Learning for Information Retrieval
Controversy Identification Using Machine Learning: Time Dependent Probabilistic Modelling of Controversy Formation based on Social Network Analysis
Automated Product Categorization using Multi-class classification on Data from Amazon
Multi Sensor Fusion for Simultaneous Localization and Mapping on Autonomous Vehicles
Identification of Fake Reviews using Network Analysis and Modeling for E-commerce websites
Approaches for Modeling Data in Multiple Modalities using representation-learning
Predictive, inferential, and mechanistic modeling of cellular-decision making
Reinforcement Learning for enhancing dependability of large distributed control systems: An approach based on advanced simulation structures
Dynamic Scheduling using predictive analytics of Multi Cloud Environments
Rule-based reasoning for knowledge authoring and categorization
Testing deep learning models for Biomedical Imaging: An intelligent image regeneration system
Analysis of the impact of Artificial Intelligence on Distributed Energy Technology using time series analysis
Using deep learning on visual data to predict subjective attributes
An analysis of Hierarchical image classification in CNNs
Using Machine Learning for predicting AQI values based on Satellite Images
Analysis of Landscape images for climate classification: A neural network based approach
Distracted Driver identification: An analysis of most appropriate feature classification and ML algorithms
Predicting Currency exchange rate for recognizing social arbitrage based on News Media
Using Machine Learning models for Credit Card Fraud Detection
Analysis of Economic Networks to Identify Industries: Using Network Characteristics for Node Labeling
Predicting Chaotic systems: An analysis for current Machine Learning Techniques
Using Machine Learning to Model Student Learning in Mobile Apps
Analysis of football match data to predict goals: ANN based approach
Framework for automating feature engineering for deep Q-learning on Markov decision processes: Using NLP for MDP Embeddings
Machine Learning model for risk of Breast Cancer Relapse based on Copy Number
Using DNA Microarray Data for identification of Leukemia Patients: A new classification approach
A comparison study of multinomial classification methods, SVM, Naive Bayes, Logistic Regression and Random Forests, to predict drug-drug interaction severity values from the adverse drug reactions in the FDA’s database
A framework for gradient boosting model predicting CVD risks using multiple EHRs
Social Mdeia Trolls identification using ML: Naive Bayes, Logistic Regression, Kernel SVM, Random Forest, and LSTM neural networks to identify political trolls across social media
A classification framework for Climate Change stance: Using labeled and unlabeled data from Twitter
Collision Avoidance for Urban Air Mobility Vehicles using Markov Decision Processes
Machine Learning on Biochemical Small Datasets: Strategies for Pursuing Predictive Analyses of Human Voltage Gated Sodium Ion Channel (hNaVs) Inhibitors
Optimization model for Antibiotic Treatment using Microculture Results dataset
How accurate is weather data for predicting solar power generation?A new feature engineering approach using National Solar Radiation Database (NSRDB)
Testing Random Network Distillation Theory & Reinforcement Learning for Transfer Learning
Learning With High-Level Attributes: An experiment with fine-grained classification on the Caltech Birds Dataset
Cardiovascular Health prediction using Adaptive Network-Based Fuzzy Inference System (ANFIS)
Biomedical Image Analysis and Reconstruction using Convolutional Neural Networks (CNN)
Using prediction algorithm on acceleration and gyroscopic data of digital pen for character classification: A framework for handwriting identification
Predictive analysis on work visa approval data from the US state department
Transfer Learning to fine-grained visual categorization (FGVC) for Tree Leaf Identification
Labeling Characters as Good or Evil using Sentiment Analysis approach in Cloud Enabled Machine Learning
Prediction of weight-loss based on calorie intake using MYFITNESSPAL DATASET
Price prediction model for the AirBnB offerings based on location
Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) models on exchange traded fund close price data to predict future prices.
Machine Learning Model for Tennis Match prediction using prior outcomes and player characteristics
Deep Learning to Collaborative Filtering Model: A novel approach for predictor system
Supervised Learning on Cloud Scale Networks for predicting Link Failure and Localization
An experiment using Deep Neural Networks for tuning of an Aircraft Pitch PID controller
A framework for detecting fake reviews using Yelp Data
SVM classifier and a modified convolutional neural network (CNN) based on Google Inception V3 to diagnose skin images as benign or malignant
Predictive analysis on used car prices
A framework for Yelp Recommendation System using XGboost
A critical review of reinforcement learning algorithms: Defining the way forward
Learning Generative Models using Transformations
New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications
Prediction system for Diagnosing Schizophrenia: A framework for clinical decision support
Biomedical Entity Recognition
A review into Energy Demand Forecast systems: A novel framework using cloud based AI for real time prediction
Text Classification: A review and way forward
Resources:
For basic understanding of Machine Learning, take this course for free at Coursera. The course is comprehensive, and one of the best MOOCs till date on any subject.
"Machine Learning" offered by Stanford
In order to get some expert insights into each component of machine learning, alongside some practical approaches, take the "Deep Learning Specialization" offered by Deeplearning.ai.
In order to start with some practical implementation from the get-go, google's offering of "Machine Learning with TensorFlow on GCP" is the best way to go. It will provide you hands-on step-by-step guides on implementing Machine Learning models without any cost or hassle.
On same line as that of the GCP specialization, a much easier and quick way to start is by using Microsoft Azure ML Studio, which provide you with already constructed models and algorithms to play with and implement. Its fun, its easy and its highly valuable: "Implementing Predictive Analytics" and "Predcitive Analytics for IoT".
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