Natural Language Processing for Sentiment Analysis

Aim:

The aim of this research is to analyze the area of sentiment analysis using natural language processing (NLP) by creating innovative methods that increase the precision and effectiveness of linguistic sentiment classification.

Research Objectives:

• To examine the most recent NLP techniques available for sentiment analysis.
• To establish and evaluate new NLP models and algorithms for sentiment analysis.
• To examine useful implementations such as social media monitering for enhanced sentiment analysis methods.

Exploring Generative Adversarial Networks (GANs) for Semi-Supervised Medical Image Segmentation

Aim:

The aim of this study is to analyze how Generative Adversarial Networks (GANs) can be used to improve semi-supervised medical picture segmentation, with a particular emphasis on raising the precision and effectiveness of segmentation algorithms in medical image analysis.

Research Objectives:

• To create a semi-supervised GAN-based framework specifically for medical image segmentation.
• To compare the performance of the proposed framework’s segmentation accuracy to that of conventional techniques and fully supervised methods.
• To enhance methods for decreasing the requirement for a large amount of labeled data in medical image segmentation jobs.

Use of Artificial Intelligence in Financial Fraud Detection

Aim:

The aim of this research is to determine whether artificial intelligence (AI) can be used to improve financial fraud detection process, which will increase the security and reliability of financial systems.

Research Objectives:

• To evaluate financial fraud’s Current situation and the effects it is having on the financial sector.
• To evaluate the current AI methods and tools used to identify financial crime.
• To evaluate how well AI-based algorithms identify different kinds of financial fraud.
• To identify obstacles and restrictions related to the use of AI in the identification of financial fraud.

Exploring the Impact of Blockchain Technology on Supply Chain Management

Aim:

The aim of this study is to thoroughly investigate and assess the effects of blockchain technology on supply chain management, with a particular emphasis on the advantages, disadvantages, and adoption variables that affect its incorporation into various supply chain activities.

Research Objectives:

• To evaluate how blockchain technology might improve supply chain traceability and transparency.
• To examine how blockchain might boost supply chain operations’ efficiency and potentially save costs.
• To examine how blockchain can protect supply chain data in terms of security and data integrity.
• To determine what issues and restrictions are preventing blockchain technology from being widely used in supply chain management.

Implementation of cyber security in the prospects of (IoT)

Aim:

The aim of this research is to evaluate the challenges and opportunities associated with the implementation of cyber security in the prospects of IoT.

Research Objectives:

• To assess the efficacy of current IoT cybersecurity security protocols and concerns.
• To suggest cutting-edge strategies and instruments for reducing IoT cybersecurity threats.
• To evaluate how cybersecurity affects the uptake and application of IoT.

Human-Computer Interaction in Virtual Reality

Aim:

The aim of this research is to examine how Human-Computer Interaction (HCI) is developing in Virtual Reality (VR) environments, emphasizing user experiences, obstacles, and new developments.

Research Objectives:

• To evaluate the development of Virtual hardware and software as well as the situation of HCI at the moment.
• To examine user opinions and contentment about HCI in Virtual.
• To identify obstacles and difficulties with HCI in Virtual.
• To examine cutting-edge HCI methods and how they affect user involvement.

Exploring Data Privacy concerns and solutions in Cloud Computing

Aim:

The aim of this study is to examine how cloud computing data privacy is changing and to evaluate its implications, challenges, and current situation.

Research Objectives:

• To evaluate how well-aligned the current data privacy laws are with cloud computing operations.
• To evaluate the efficiency of authentication and encryption mechanisms in protecting data within cloud environments.
• To examine how new technologies like blockchain and homomorphic encryption affect the privacy of cloud data.
• To determine the best approaches and suggestions for improving cloud computing data privacy.

Enhancing Traffic Management through Autonomous Vehicles

Aim:

The aim of the study is to examine how autonomous vehicles (AVs) might improve traffic control systems.

Research Objectives:

• To examine how the public feels about and accepts the use of AVs for traffic control.
• To analyze how AVs will affect reducing congestion, improving traffic flow, and improving road safety.
• To examine the difficulties and legal framework related to the use of AV.
• To make suggestions for governmental decision-makers and city planners to improve the use of AVs in traffic management.
• To examine how the public feels about and accepts the use of AVs for traffic control..

Big Data Analytics for Predictive Maintenance

Aim:

The aim of this research is to examine advanced big data analytics methods for predictive maintenance in industrial environments, with an emphasis on enhancing maintenance procedures, cutting downtime, and increasing operational effectiveness.

Research Objectives:

• To examine earlier research on predictive maintenance and big data analytics in industrial settings.
• To identify the most important data sources and techniques for gathering data for predictive maintenance.
• To evaluate the models’ ability to forecast equipment breakdowns and maintenance requirements.

Identifying Influential Figures Through Social Network Analysis

Aim:

The purpose of this study is to use social network analysis (SNA) to identify key players in online communities and networks and to explain how they affect social dynamics and the spread of information.

Research Objectives:

• To examine the body of research on impact detection techniques and social network analysis.
• To develop and put into use cutting-edge algorithms for locating important nodes in social networks.
• To evaluate the presented methodologies’ effectiveness using actual social network data.
• To evaluate influence detection’s uses and implications for a range of fields, including public health, marketing, and online communities.

Advanced Quantum Computing Algorithms for Solving Complex Problems in a Quantum Information Processing Environment

Aim:

The aim of this research is to explore knowledge of the latest quantum computing methods, including their potential uses in resolving intricate computational issues.

Research Objectives:

• To examine current quantum algorithms to understand their theoretical underpinnings and real-world applications, such as Grover’s and Shor’s algorithms.
• To enhance efficiency and scalability by developing and refining quantum algorithms for certain computing tasks.
• To examine how quantum computing methods are being used in the real world in domains including machine learning, cryptography, and optimisation.
• To evaluate the difficulties and constraints posed by quantum algorithms and suggest ways to overcome them.