By combining Particle Swarm Optimisation (PSO) and Fuzzy Logic, the team at SRM AP has discovered a hybrid smart system for accurate localization of autonomous aerial vehicles (AAVs). Dr Awadesh Dixit, Dr Firoj Gazi, Dr Muzakkir Hussain Md, Assistant Professors and MTech student Ms Naga Nandini Devi from the Department of Computer Science and Engineering, have published their research article “OALHMT: Optimised AAV Localization Using Hybrid Metaheuristic Techniques” in the IEEE Journal of Indoor and Seamless Positioning and Navigation, that discloses the making and implementation of this smart system that is precise and works better than current methods for positioning and navigation.

Abstract

Accurate localization remains a critical challenge for autonomous aerial vehicles (AAVs) due to their high-speed, multidirectional movement. Precise positioning is essential for efficient resource management and task reallocation during flight. This research proposes a bioinspired hybrid model combining Particle Swarm Optimization (PSO) with fuzzy logic to enhance AAV localization accuracy. PSO effectively explores the search space to identify optimal positions, while fuzzy logic fine-tunes the solution using real-time environmental inputs such as signal strength, sensor noise, and GPS inaccuracies. This synergistic approach leverages PSO’s global optimization with the contextual adaptability of fuzzy systems, delivering robust and precise localization under dynamic and noisy flight conditions. Comparative evaluations with existing methods demonstrate the model’s superior accuracy and responsiveness in real-time AAV operations.

Practical Implementation/Social Implication of the Research

Proposed hybrid optimization for Indoor localization through swarm UAVs, localization methods for autonomous aerial vehicles (AAVs) typically rely on the global positioning system (GPS), which is effective in open environments. GPS provides accurate real-time positional data, making it widely used for outdoor AAV localization.

The team will continue to focus on developing swarm-based V2X communication and coordination, and reduced interference, secure communication for enhance UAV networks performance, advance AI-related applications.

Link to the article

Overall abstract diagram of the paper

The Department of Computer Science and Engineering at SRM University-AP has yet again produced a scientific breakthrough. Associate Professor Dr Ashok Kumar Pradhan, along with his research scholars Mr Raheem Qudus and Ms Swetha Ghanta, published a patent on post-stroke rehabilitation using Virtual Reality and Artificial Intelligence.

Brief Abstract of the Research

Stroke is one of the most significant health problems that aid disability and mortality in functionality of human health, While already there are varies approach, Traditionally and Technical methods developed to help patient overcome these challenges, common system to treat and help patients recovers from this are, using Local therapy approach, using EEG (electroencephalogram) and or using the BCI (Brain Computer Interface). However, not all patients find this approach applicable, suitable and affordable. The aim of this research is to perform Post-Stroke Rehabilitation recovery training and testing on patient without the need for Brain computer Interface (BCI) and Electroencephalogram (EEG), our proposed method provides similar embodiment and rehabilitation capabilities to those perceived from existing therapeutic techniques such as Constraint induced movement therapy (CIMT), Occupational therapy, mirror therapy and authenticating fear. Our Research approach resolution is to develop a virtual reality (VR) based system for post-stroke rehabilitation using Meta Quest (Headset) and Unity game engine for simulation of the virtual environment. Our emerging methods in integrating Generative-AI to provide adaptive therapy recommendations based on patient movements and engagement levels. While we implement the use of a real-time compensatory movement detection system without EEG, relying on AI-driven motion analysis.

Explanation in layperson’s terms:

After a stroke, patients often struggle to regain movement or perform simple tasks. Existing treatments rely on traditional and invasive signal equipment, such as wearing of electroencephalogram(EEG), and also require a professional expert to track and interpret the progress of the patient, while relying on real-world objects. Our invention solves those existing limitations through the integration of Virtual Reality (VR) and Artificial Intelligence (AI). We create a virtual world and object where stroke patients wear a VR headset and perform exercises in a simulated environment, while patients are able to feel a real-world immersive experience through movement practice, with a real-time communication guide of AI to aid users’ improvement.

Practical Implementation and Social Implications:

• Implementation: Clinics or homes can use our VR system to supplement therapy, reducing costs and therapist workload.
• Social Impact: Makes rehab accessible to low-income patients, avoids the stigma of bulky medical devices, and could reduce global stroke-related disability burdens.
• Accessible Therapy: Makes rehabilitation possible from home, especially in rural areas or underserved communities.
• Personalised Healing: The AI ensures therapy matches the patient’s ability in real time by providing real-time support during the session.
• Mental Health & Cognitive Benefit: Helps with memory, attention, and mood rehabilitation using immersive tasks.

Collaborations:

      SRM University-AP, Amaravati (Assignee)

Future Research Plans:

• Expand the discovery into an adaptation framework for elderly care, PTSD treatment, and education for neurodivergent learners.
• Pilot clinical testing with actual stroke patients in collaboration with hospitals.
• Expand the system to support speech therapy and cognitive recovery tools.
• Integrating an AI-Agent for real-time Non-player character medical support during the immersive session.

Assistant Professor Dr Hemantha Kumar Kalluri from the Department of Computer Science and Engineering and post-doctoral fellow, Dr Premkumar Borugadda have published a research paper titled, A Comprehensive Analysis of Artificial Intelligence, Machine Learning, Deep Learning and Computer Vision in Food Science. This significant research explores how Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) are making food processing smarter and more reliable.

Here’s a brief on their findings and the social and practical implementations of their work.

A Brief Abstract

Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society. It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest, storage, preparation, and consumption. This current paper seeks to demystify the importance of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) in ensuring food safety and quality. By stressing the importance of these technologies, the audience will feel reassured and confident in their potential. These are very handy for such problems, giving assurance over food safety. CV is incredibly noble in today’s generation because it improves food processing quality and positively impacts firms and researchers. Thus, at the present production stage, rich in image processing and computer visioning is incorporated into all facets of food production. In this field, DL and ML are implemented to identify the type of food in addition to quality. Concerning data and result-oriented perceptions, one has found similarities regarding various approaches. As a result, the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered. It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further. Also, deep learning is accurately integrated with identifying the quality and safety of foods in the market. This paper describes the current practices and concerns of ML, DL, and probable trends for its future development.

Explanation of the Research in Layperson’s Terms

The research explores how Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) are making food processing smarter and more reliable.

AI and ML in Food Processing

• AI-powered systems can predict food spoilage, detect harmful contaminants, and ensure food is stored at the right temperature.
• ML models learn from past data to improve food quality, making processing more efficient and reducing waste.

 Computer Vision (CV) for Food Inspection

• Cameras powered by AI can analyse food products and detect defects, ensuring only high-quality food reaches consumers.
• CV helps in sorting fruits and vegetables based on size, colour, and ripeness, reducing human error and speeding up production.
• It is also used to check food packaging for defects and prevent contamination.

 Deep Learning for Better Food Safety

• Deep learning, a more advanced type of AI, helps identify patterns in food quality data.
• It is used to detect food adulteration (unwanted substances added to food), ensuring that the food we consume is pure and safe.
• DL models can also classify different types of food and analyse their nutritional content.

Future of Smart Food Processing

• As AI and ML continue to evolve, food production will become more automated, reducing human labour while increasing efficiency.
• These technologies will help minimise food waste, improve supply chains, and provide personalised nutrition recommendations.
• By integrating AI with real-time monitoring, food safety can be ensured on a global scale.

Practical Implementation and Social Implications

The research on Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) in Food Science has significant real-world applications and social implications:

Practical Implementation

Our research has directed the researchers to develop applications in various stages of the food industry, from agriculture to food processing, quality control, and distribution. Here are some key practical implementations:

1. Smart Food Inspection and Quality Control

1.1 Computer Vision (CV) & Deep Learning (DL) for Defect Detection

• AI-powered cameras can detect defects in fruits, vegetables, and packaged food (e.g., detecting bruises in apples and sorting out contaminated grains).
• AI enhances automated food grading and sorting, reducing human error.

1.2 AI for Food Adulteration Detection

• AI models analyse food samples for contaminants, chemical adulterants, and spoilage (e.g., identifying milk adulteration using spectral data).
• Deep learning algorithms predict shelf life based on packaging conditions.

  2. AI in Food Safety and Hygiene Monitoring

2.1 AI-based Sensors for Real-time Food Safety Checks

• AI-driven sensors monitor temperature, humidity, and hygiene conditions in food storage units and supermarkets.
• ML-based forecasting predicts food spoilage before it happens, reducing foodborne diseases.

This research bridges the gap between technology and food security, ensuring that AI and ML can revolutionise the way food is produced, processed, and consumed. These technologies enhance quality control, reduce food waste, ensure hygiene, and support sustainable agriculture, leading to a healthier, safer, and more efficient global food system.

Future Research Plans.

AI-Powered Automated Food Sorting & Grading

• Use Computer Vision & Deep Learning to automate sorting of grains, fruits, and vegetables based on size, ripeness, and defects.
• Implement Deep Learning models for real-time sorting in food processing units.