In a significant advancement for neurodevelopmental research, Dr Nitul Dutta, Associate Professor in the Department of Computer Science and Engineering, along with PhD scholar Ms Surya Samantha Beri and BTech student Mr Nallamothu Sai Karthik, have successfully filed and published a patent titled “A System for Autism Spectrum Disorder Detection.” The application, numbered 202441053505, has been officially documented in the Patent Office Journal.

The innovative system aims to enhance the early detection of Autism Spectrum Disorder (ASD), providing a more efficient and accessible method for diagnosis. By integrating advanced algorithms and machine learning techniques, the system promises to analyse behavioural data effectively, allowing for timely interventions and support for individuals on the spectrum. Dr Dutta emphasised the importance of early detection, stating, “The earlier we can identify ASD, the better the outcomes for individuals and their families. Our system is designed to make this process more accurate and user-friendly.”

Ms Beri and Mr Karthik contributed significantly to the research, which reflects a collaborative effort between academia and technology. Their work not only demonstrates the potential for technological solutions in healthcare but also highlights the critical role of interdisciplinary approaches in addressing complex challenges.

This patent represents a crucial step forward in the field of autism research and is expected to pave the way for further innovations aimed at improving the lives of those affected by ASD.

Abstract of the Research

The system for autism spectrum disease detection incorporates a server with a hybrid application comprising several key modules: a capturing module receiving images from image-capturing devices , a data collection module gathering a dataset of images from multiple capturing devices, and a pre-processing module standardising and normalising images to generate a standardised dataset. Additionally, a feature extraction module collaborates with the pre-processing module to identify autism-indicative features in standardised images, preparing labelled standardised images stored in the data collection module.

Furthermore, a data segmentation module segments standardised images into training and testing data, including a training module for real-time training of a convolutional neural network model and a testing module to evaluate the convolutional neural network model’s accuracy in detecting autism based on testing data.

Explanation of the Research in Layperson’s Terms

The background information herein below relates to the present disclosure but is not necessarily prior art. Autism spectrum disorder (ASD) is a neurological or developmental disorder that profoundly impacts communication skills, social interaction, and cognitive abilities in individuals. Those with Autism Spectrum Disorder (ASD) often exhibit challenges in social interaction, limited eye contact, difficulty understanding social cues, and impaired language skills. Additionally, repetitive behaviours and sensory sensitivities are common characteristics.

The disorder arises from developmental changes in brain structure and can have various causes, including genetic factors, familial history of autism spectrum disorder (ASD), advanced parental age, or low birth weight. The prevalence of autism spectrum disorder (ASD), as reported by the World Health Organization (WHO), stands at one in every 160 children. Early detection and intervention are crucial for managing autism spectrum disorder (ASD) effectively, as interventions such as medical and neurological examinations, cognitive and language assessments, and frequent observations, including blood and hearing tests, can significantly improve outcomes. Detecting Autism Spectrum Disorder (ASD) in children below the age of 10 is comparatively easier than in adults, underscoring the importance of early diagnosis to facilitate timely interventions.

Current diagnostic processes for autism spectrum disorder (ASD) often present significant challenges, particularly for young children, due to their limited ability to communicate and cooperate during assessments. Traditional diagnostic methods rely heavily on structured interviews, behavioural observations, and standardised tests, which can be daunting and stressful for children, leading to inaccurate results. Moreover, these procedures are time-consuming and often require multiple visits to specialised clinics or healthcare facilities, causing inconvenience and financial strain for families. Also, the cost associated with autism spectrum disorder (ASD) diagnosis can be prohibitive for many families.

Therefore, there is a pressing need for more accessible, less intrusive, and cost-effective methods for detecting autism spectrum disorder (ASD) in its early stages to ensure timely and effective intervention. Therefore, there is a need for a system for autism spectrum disorder detection that alleviates the drawbacks.

Practical and Social Implications Associated with the Research

Current diagnostic procedures for Autism Spectrum Disorder (ASD) face considerable technical challenges, particularly concerning young children’s limited ability to engage in conventional assessment methods. These methods typically rely on structured interviews, behavioural observations, and standardised tests, all of which can be arduous and distressing for children with Autism Spectrum Disorder (ASD), potentially leading to unreliable outcomes. Furthermore, these procedures are resource-intensive, requiring multiple visits to specialised clinics or healthcare facilities, thereby causing logistical challenges and financial burdens for families. Current diagnostic processes for Autism Spectrum Disorder (ASD) often present significant challenges, particularly for young children, due to their limited ability to communicate and cooperate during assessments. Traditional diagnostic methods rely heavily on structured interviews, behavioural observations, and standardised tests, which can be daunting and stressful for children, leading to inaccurate results. Moreover, these procedures are time-consuming and often require multiple visits to specialised clinics or healthcare facilities, causing inconvenience and financial strain for families. Also, the cost associated with Autism Spectrum Disorder (ASD) diagnosis can be prohibitive for many families. Therefore, there is a pressing need for more accessible, less intrusive, and cost-effective methods for detecting Autism Spectrum Disorder (ASD) in its early stages to ensure timely and effective intervention.

Collaborations

This research was done in collaboration with Professor George, Brunel University, London, United Kingdom

Future Research Plans

In the future, we will also try to diagnose the disorder by speech therapy using Natural Language Processing and integrate it with real-time industry in health care, which can be used by many doctors in their respective practices

In a significant advancement for cloud computing technologies, Dr Kakumani K C Deepthi and Dr Prasanthi Boyapati, Assistant Professors in the Department of Computer Science and Engineering, alongside B Tech student Ms Yarra Khyathisree, have successfully filed and published a patent titled “SYSTEM AND METHOD FOR AUTOMATIC LOAD BALANCING FOR BANK OF CLOUD SERVERS.” The patent, registered with Application Number 202441057273, was officially published in the Patent Office Journal.

As cloud computing continues to expand, effective load balancing has become critical for optimizing distributed environments. Load balancing is essential for distributing data and services across a scalable network of nodes, ensuring that no single node becomes overwhelmed. This is particularly important as data storage needs in cloud environments grow exponentially.

The newly patented system aims to enhance load balancing and job scheduling, addressing the increasing demand for efficient services. The article highlights various notification algorithms designed to improve these processes, comparing the latest methods to boost performance and user satisfaction.
This innovation marks a promising step forward in cloud computing technology, paving the way for more robust and efficient systems to meet the evolving needs of users and organizations alike.

Abstract of the Research

Load balancing is crucial for the efficient operation of distributed environments, especially with the rapid growth of cloud computing and increasing customer demands for more services and positive outcomes. Cloud load balancing involves transparently sharing data and delivering services through a scalable network of nodes. Due to the open and distributed nature of cloud computing, the amount of data storage grows rapidly, making load balancing a critical issue. Managing load information in such a vast system is costly. A major challenge in cloud computing is distributing dynamic workloads across multiple nodes to prevent any single node from becoming overwhelmed. Numerous algorithms have been proposed to effectively allocate customer requests to available cloud nodes. These methods aim to enhance the overall performance of the cloud and provide users with more satisfying and efficient services. This article reviews various notification algorithms to address cloud computing load balancing and job scheduling issues, comparing the latest methods in the field.

Practical Implementation or the Social Implications Associated with the Research

In this patent, the common load-balancing algorithms in cloud computing include:
• Round Robin
• Least Connection
• Randomized
• Load Balancing Challenges in Cloud Computing
• Automated Service Provisioning
• Virtual Machine Migration
• Energy Management
• Stored Data Management

Future Research Plans

To implement automatic load balancing for not only banks but also some other applications where cloud servers can be designed by ensuring optimal resource utilization, performance, and reliability.

Link to the article

In a significant achievement for the Department of Computer Science and Engineering, Dr Mahesh Kumar Morampudi, Assistant Professor, along with B.Tech. student Ms. Nunna Lakshmi Manasa, has been granted a patent for their groundbreaking invention titled “System and method for generating synthetic data based on variational autoencoder.” The patent, with Application Number: 202241049545, was officially recognised in the Indian Patent Office.

This innovative system leverages the capabilities of variational autoencoders to generate synthetic data, which has vast applications in various fields, including machine learning, data privacy, and simulation. The ability to create high-quality synthetic datasets can significantly enhance research and development processes, providing researchers and practitioners with valuable tools for analysis and experimentation.

The recognition of this patent not only highlights the innovative spirit within the department but also underscores the collaborative efforts between faculty and students in advancing technology and contributing to the field of computer science.

Abstract of the Research

Diabetic retinopathy (DR) is a diabetes-related eye condition that occurs when high blood sugar levels cause damage to the blood vessels in the retina, the light-sensitive tissue at the back of the eye. Over time, these damaged vessels can leak blood or other fluids, leading to vision impairment.

DR typically progresses through stages, starting with mild non-proliferative retinopathy, where small bulges form in the blood vessels, to proliferative retinopathy, the most severe stage, where new abnormal blood vessels grow on the retina and in the vitreous humor, potentially leading to blindness. Early detection and management are crucial to prevent significant vision loss, often involving regular eye exams, blood sugar control, and treatments like laser therapy or surgery.

Synthetic data generation for DR is an emerging approach to augment limited clinical datasets, enhancing the training of machine learning models for diagnosis and prognosis. The present disclosure envisages a system for generating synthetic data based on a variational autoencoder (VAE). This work explores the use of a VAE combined with deep learning for the detection and classification of DR. VAEs, known for their ability to learn compact and meaningful representations of complex data, are employed to generate latent features from retinal images, effectively capturing the subtle variations and anomalies indicative of DR. These latent features are then fed into a deep learning classifier, which is trained to categorise the severity of DR into various stages, ranging from no DR to proliferative DR.

Research in Layperson’s Terms

Imagine your eye is like a camera, and the retina at the back of your eye is the film that captures the pictures you see. DR is a condition that affects this “film” when someone has diabetes for a long time. High blood sugar levels can damage the tiny blood vessels in the retina, leading to vision problems and, in severe cases, blindness.

Our research is about creating a computer program that can help doctors detect and classify this eye condition more accurately. We use a special kind of technology called a VAE, which is like a smart artist that learns to understand and recreate detailed images of the retina. This “artist” can pick up on the tiny changes and patterns in the retina that might be missed by the human eye. Once the VAE has learned these details, it passes them on to another program, which is really good at sorting things into categories. This second program, a deep learning classifier, uses the information from the VAE to decide how severe diabetic retinopathy is—whether it’s mild, moderate, or severe.

By combining these two technologies, our system can help doctors detect diabetic retinopathy earlier and more accurately, which is crucial for preventing vision loss in people with diabetes.

Title of the Patent in the Citation Format
Inventor Name(s):
Dr Mahesh Kumar Morampudi
Nunna Lakshmi Manasa
“System and method for generating synthetic data based on variational auto encoder” with Application Number: ” 202241049545.” Date of Patent Grant. 29/07/2024

Practical Implementation:
The practical implementation of our research involves integrating the VAE and deep learning classifier into a software tool that can be used by eye care professionals. Here’s how it might work in a real-world setting:

1. Data Collection: Retinal images are collected from patients during routine eye exams. These images are fed into the system, which has been trained using a large dataset of retinal images, including those showing different stages of DR.
2. Feature Extraction: The VAE processes these images, learning to capture and condense the important features that indicate the presence and severity of DR. This step is crucial because it allows the system to focus on the subtle details that might signify early stages of the disease.
3. Classification: The deep learning classifier then takes these features and classifies the severity of DR. It provides a diagnosis, categorising the condition as no DR, mild, moderate, or severe. The system could also flag cases that need urgent attention, helping prioritise patient care.
4. Clinical Decision Support: The results are presented to the healthcare provider through an intuitive interface. This tool could be used in clinics, especially in areas with limited access to specialised eye care, allowing general practitioners or technicians to screen for DR and refer patients for further treatment if needed.

Social Implications:

1. Increased Access to Early Detection: By automating the detection of diabetic retinopathy, this technology can be deployed in remote or underserved areas where access to specialists is limited. Early detection is key to preventing vision loss, and this tool can make screening more accessible, especially in communities with high rates of diabetes.
2. Reduced Healthcare Costs: Early detection and treatment of diabetic retinopathy can prevent the progression to more severe stages, reducing the need for expensive treatments like surgery or long-term care for blindness. This could lower healthcare costs for both patients and the healthcare system.
3. Empowering Healthcare Providers: The tool can support general healthcare providers in making more accurate diagnoses, reducing the burden on specialists and allowing them to focus on more complex cases. This democratizes eye care, making it possible for more people to get the care they need without long delays.
4. Improved Patient Outcomes: With more accurate and timely diagnosis, patients can receive treatment sooner, leading to better health outcomes and preserving vision. This can significantly enhance the quality of life for individuals with diabetes, who might otherwise suffer from preventable blindness.
5. Ethical Considerations: While technology offers many benefits, it also raises ethical questions about the use of AI in healthcare, such as ensuring that the system is fair, transparent, and does not introduce biases. Continuous monitoring and updates would be necessary to ensure the system remains accurate and equitable.

Future Research Plans

1. Improving Model Robustness and Generalization: Enhance the VAE and deep learning classifier’s ability to generalize across diverse populations and imaging conditions.
2. Incorporating Multimodal Data: Integrate additional data sources, such as patient medical history, blood sugar levels, and genetic information, to improve diagnostic accuracy.
3. Real-Time Implementation and Mobile Integration: Adapt the system for real-time analysis and deploy it on mobile devices.
4. Collaboration with Clinical Trials: Validate the AI system’s effectiveness through collaboration with clinical trials.

In a significant contribution to the field of food marketing and consumption, Ms P Naga Sravanthi, an Assistant Professor Ad hoc in the Department of Computer Science and Engineering, has recently published a book chapter titled “Blockchain-Based Food Influencer Verification.” The chapter is part of the book “Innovative Trends Shaping Food Marketing and Consumption,” shedding light on the emerging use of blockchain technology in authenticating food influencers.

In the rapidly evolving world of food marketing, trust and authenticity have become more critical than ever. The book chapter delves into how blockchain technology can revolutionise the way food influencers are verified and trusted by their audiences. Blockchain technology, known for its transparency and security features, has been making waves across various industries, and its application in the food sector is no exception.

Ms Naga Sravanthi’s work focuses on utilising blockchain to verify the credibility and authenticity of food influencers, a vital aspect in today’s digital era, where influencer marketing plays a significant role in consumer decision-making. The book chapter explores how blockchain can address issues of trust and reliability in food marketing by providing a secure and immutable record of influencers’ endorsements and partnerships. This innovative approach not only benefits consumers by ensuring they receive genuine recommendations but also helps food brands collaborate with legitimate and reputable influencers.

The publication of this book chapter underscores SRM University-AP’s commitment to fostering cutting-edge research and innovation in diverse fields, positioning its faculty members as thought leaders in their respective domains. Ms Naga Sravanthi’s work serves as a testament to the university’s dedication to academic excellence and pushing the boundaries of knowledge in emerging technologies.

Brief Description of the Book Chapter:
This chapter explores the integration of blockchain technology into the verification process of food influencers, providing a transparent and tamper-proof method to authenticate their influence and endorsements. By leveraging blockchain, the chapter outlines how the food industry can ensure that influencers are genuine, their endorsements are credible, and their impact is accurately measured. The chapter also delves into case studies and practical applications, demonstrating how this technology can reshape the landscape of influencer marketing in the food sector.

Significance of the Book Chapter
The significance of this chapter lies in its exploration of a cutting-edge application of blockchain technology in a field that deeply influences consumer behaviour in food marketing. As the digital space becomes more saturated with influencers, the need for a reliable verification system becomes paramount. This chapter is particularly meaningful because it addresses the growing concern of authenticity in influencer marketing, a crucial factor in maintaining consumer trust and brand integrity. For those passionate about the intersection of technology, marketing, and food, this chapter offers insights into how blockchain can be a game-changer in ensuring that the voices consumers trust is, indeed, trustworthy.

Target Audience

The book is primarily targeted at professionals in the food industry, including marketers, brand managers, and digital strategists, who are keen on understanding the latest trends shaping consumer behaviour and marketing practices. Additionally, this chapter will be valuable to academics and students in the fields of marketing, food science, and technology, as well as tech enthusiasts interested in the practical applications of blockchain.

Startups and entrepreneurs looking to innovate within the food sector will also find this book to be a vital resource, providing both theoretical insights and practical examples of how technology can redefine industry standards.In essence, “Innovative Trends Shaping Food Marketing and Consumption” and its chapter on blockchain-based influencer verification is an essential read for anyone looking to stay ahead of the curve in the dynamic world of food marketing.

In an impressive achievement, Dr Ashok Kumar Pradhan, Associate Professor of the Department of Computer Science and Engineering, along with PhD Scholar Ms Ghanta Swetha and BTech CSE students Mr Bandi Sai Harshith, Mr Estamsetty Srikanth, Mr Guduri Venkata Sai Kumar, and Mr Atmakuri Pavan Kumar, has successfully published a patent titled “A SYSTEM AND METHOD FOR AUTOMATED PLANT DISEASE DETECTION.” The application has been officially recognised with Application Number: 202441052706, as recorded in the Patent Office Journal.

This innovative system aims to revolutionise the agricultural sector by providing an automated mechanism for detecting diseases in plants. Thus, it enhances crop management and ensures healthier yields. The team’s dedication to advancing technology and improving agricultural practices showcases the potential of computer science in solving real-world problems.

The patent not only reflects the hard work and collaboration among the faculty and students but also signifies a step forward in the integration of technology with agriculture. As the world faces challenges related to food security, such innovations play a critical role in safeguarding plant health and agricultural productivity.

Congratulations to Dr Pradhan, Ms Swetha, and the student inventors on this significant milestone in their academic and professional endeavours!

Abstract of the Research:
The innovation that is being presented in this project is a software-based system that is intended to help farmers by offering an automated way to identify plant diseases. This innovation combines a number of technological elements:

Convolutional Neural Networks (CNNs): These networks are used to recognise and categorise plant diseases from images of both healthy and sick plants with high accuracy. This is leveraging transfer learning with pre-trained CNN architectures to improve performance even with sparsely annotated data.

MobileNetV2 Architecture: Designed with the specific purpose of classifying agricultural diseases in mind, this model is effective and lightweight, making it ideal for use in resource- constrained settings such as farms.

Weather API Integration: This helps farmers make decisions about crop management and disease prevention by giving them access to real-time weather data.

AI-Powered Website: Acts as a user interface for farmers to communicate with the system, submit plant photos, post queries, and get weather and diagnostic updates.

Chatbot: Utilizes Recurrent Neural Networks (RNN) and Natural Language Processing (NLP) to respond to user inquiries and provide guidance on crop management and disease prevention.

All things considered, this innovation is a system that integrates various software components, artificial intelligence, and data integration to produce a complete tool for raising agricultural productivity and managing diseases.

Research in Layperson’s Terms

1. Automated Plant Disease Prediction: Current approaches frequently use traditional diagnostic procedures and manual inspection, which can be laborious and prone to human mistakes. The suggested method uses CNNs to automatically and precisely recognise images, increasing the efficacy and precision of plant disease diagnosis.
2. This project assists farmers in making decisions on crop management and disease prevention by providing them with access to current weather information.
3. Farmers can query various farming issues and receive responses. This is done by leveraging RNNs and NLP.
4. Use of Transfer Learning: The system uses transfer learning to use pre-trained CNN architectures, which enables it to function well even with a small amount of annotated data. Compared to typical machine learning models, which frequently need big datasets and intensive training, this is a major improvement.
Integration with Smart Agriculture Systems: – This method combines disease prediction with smart agriculture systems, allowing for real-time monitoring and decision-making, in contrast to independent diagnostic instruments.

Practical Implementation or the Social Implications Associated
1. Field Diagnosis by Farmers: – Farmers can snap photos of their crops in the fields and instantly receive a disease diagnosis and treatment suggestions by using the platform.

2. Agricultural Extension Services: Using the system, agricultural extension agents may help farmers more effectively by offering guidance and support.

3. Agricultural Research: – Researchers can investigate plant diseases and create novel remedies and management techniques by utilizing the extensive annotated picture library and diagnostic tools.

4. Commercial Farming Operations: – By incorporating the system into their precision agriculture techniques, large-scale farming operations can maximize crop health management and operational effectiveness.

5. Policy Formation and Governance: – Governmental organizations can monitor plant disease outbreaks and create regional or national plans for disease control and prevention using aggregated data from the platform.

Future research plans

We may use privacy and security enhancement tools and techniques to make the data more secure

The link to the article

In a recent development, Dr Sanjay Kumar, Assistant Professor in the Department of Computer Science and Engineering, along with a team comprising N Ujwala, J Yashwanth Sri Ram, J Lakshmi Prasanna Kumar, and K Heman Rai Choudhary, have successfully filed and published an innovative invention in the Patent Office Journal titled “SYSTEM AND METHOD FOR DISCRETE WAVELET TRANSFORM (DWT)-BASED SECURE IMAGE WATERMARKING” with the application number “202441045387”.

This invention holds great promise in the field of digital image security and watermarking. The patented system and method are based on the discrete wavelet transform (DWT), a widely used signal processing technique, to embed secure watermarks into digital images. The application of DWT ensures that the watermarking process is robust and secure, making it suitable for a wide range of applications where image integrity and authenticity need to be ensured.

Dr Sanjay Kumar’s expertise as an Assistant Professor, coupled with the team’s skills and dedication, has resulted in the successful development and patenting of this cutting-edge technology. The “SYSTEM AND METHOD FOR DISCRETE WAVELET TRANSFORM (DWT)-BASED SECURE IMAGE WATERMARKING” promises to be a significant addition to the field of digital image security and watermarking, offering enhanced protection against unauthorised tampering and misuse of digital images. It is expected that this invention will garner attention from industry professionals, researchers, and policymakers, paving the way for its integration into diverse digital imaging systems.

Abstract

Our research introduces a robust image watermarking technique that combines Discrete Wavelet Transform (DWT) and chaotic map-based encryption. The method analyzes high-frequency sub-bands derived from DWT applied to the blue channel of an RGB image, selecting the block with the highest energy for embedding a grayscale watermark encrypted with the Henon Map. The alpha blending technique is used to integrate the encrypted watermark, ensuring both imperceptibility and robustness. The method achieves an average PSNR of 43.7211 dB and SSIM of 0.9950. The watermark can be extracted by analyzing patterns in the high-frequency component, even after various attacks, using inverse DWT and Henon Map for decryption.

Explanation of the Patent in Layperson’s Terms
Our research focuses on protecting digital images by embedding a hidden watermark that is hard to remove. We use a mathematical method called the Discrete Wavelet Transform (DWT) to break down an image into different parts and find the best place to hide the watermark. The watermark is further secured by encrypting it with a technique called the Henon Map. Our method ensures that the watermark remains invisible to the naked eye while being resistant to tampering. This means the watermark can be detected and recovered even if the image is altered.

Practical Implementation and the Social Implications

The primary application of our research is in protecting the ownership and integrity of digital images. This technique can be used by photographers, artists, and digital content creators to ensure their work is not copied or altered without permission. By embedding a secure, invisible watermark, they can prove ownership and detect unauthorised use. Additionally, this method can be applied in sensitive fields such as medical imaging and legal documents where tamper detection is crucial.

Collaborations

This research was conducted by the Visual Information Processing Lab at the Department of Computer Science and Engineering, SRM University AP, Guntur, India. The team comprised Nadella Ujwala, Sanjay Kumar, Jayyavarapu Yaswanth Sri Ram, Jetti Lakshmi Prasanna Kumar, and Katragadda Heman Rai Choudhary.

Future Research Plans

Our future research will focus on enhancing the watermarking technique’s robustness against more sophisticated attacks, exploring real-time applications in video watermarking, and developing methods to embed multiple watermarks in a single image. We also aim to reduce the computational complexity to make the algorithm more efficient for practical applications.