Badminton star, Rushendra Thirupathi, a first-year BBA student, clinched the silver medal in the Men’s Singles category at the Yonex-Sunrise 11th Telangana State Senior Badminton Championship 2025. Hailing from the state of Telangana, Rushendra’s dedication, discipline and relentless efforts have forged his name in the charts of successful champions in the field of Badminton.

Previously, Rushendra secured gold at the Yonex-Sunrise 46th Junior National Badminton Championships 2023 at Bengaluru and a bronze medal in the NMDC Telangana International Challenge in 2024. After a six-month break, Rushendra returned to the courts with zeal and passion and triumphed through the championship, competing with 142 athletes, placing second after a fierce final match.

After a well-awaited victory, Rushendra expressed his gratitude to his coaches, trainers, and team, who supported him during his time off. He said, “I am sincerely thankful to my coach, mentors, and team from the Gopichand Academy and SRM University-AP, who believed in me and my skills. Their guidance and support empowered me to overcome all difficulties and excel in the championship finals.”

Director of Sports, Mr Anup Singh Suryavanshi, applauded his remarkable achievement and congratulated the silver medallist. He commented, “This outstanding accolade is a testament to his fighting spirit and the well-rounded training system and facilities offered at SRM University-AP. We empower our athletes to rise, compete, and conquer national and international tournaments.”

Prof Ch. Satish Kumar, Pro-Vice Chancellor, appreciated the student. He quoted, “Achievements like this make the university proud and motivate the trainers and coaching team to put in their extra efforts to prepare students for such events.”

Prof. Manoj K Arora, Vice Chancellor, also expressed his appreciation for Rushendra, stating that the accomplishments of the rising sports star reflect the university’s spirit—ambitious and unstoppable. He commented, “We take immense pride in the ecosystem SRM AP has built to nurture excellence in sports and support our athletes to be champions of national pride.” The university continues to nurture athletes in various fields, offering divergent training and world-class facilities to generate sporting aces of the country.

SRM University-AP hosted a memorable get-together, the Alumni-Meet and Greet in Hyderabad for its beloved alumni on June 29, 2025. The Meet and Greet welcomed 150 alumni from Hyderabad and neighbouring locations, who assembled for a wonderful evening of rekindling memories, reconnecting with peers and establishing new connections. Organised under the aegis of The Directorate of Alumni Relations, the event was presided by Prof. Ch Satish Kumar, Pro-Vice-Chancellor, Dr Satish Anamalamudi, Assistant Director – Alumni Relations, Dr Vinayak Kalluri, Dean – Academic Affairs, Mr Siddharth Tripathi, Director – Entrepreneurship & Innovation, and Mr Payeli Indra Kiran Kumar, President of the Alumni Association SRM AP.

Prof. Ch Satish Kumar welcomed all alumni and remarked that the Meet aimed to build an emotional bridge between the generations, batches, and sections. He opined that this network would help the alumni grow, prosper, and progress professionally and personally and commended Dr Satish and SRM AP for taking the initiative to build this bridge through the Alumni Meet and Greet.

Mr Indra Kiran Kumar enlightened the alumni on the various channels through which they could engage and contribute to their alma mater. He encouraged them to be mentors by sharing their experience and knowledge with the students, to be part of the various academic and non-academic councils of the university, and to join hands with the faculty and students to work on joint projects or begin a new business venture. He also emphasised the slogan “Come. Connect. Contribute” advocated by Vice Chancellor Prof. Manoj K Arora.

The meet-and-greet offered alumni an opportunity to meet their fellow alumni and connect with their alma mater, which has guided them in their formative years of higher education and helped them choose their career path. The event also conducted some team-building activities and prizes for the winners.

The Hyderabad chapter of the Alumni Meet and Greet was a resounding success. The Alumni Relations’ initiative to organise several smaller meetings in various locations of the country made it easier and more accessible for the alumni to get together, witnessing a broad and active participation. The event organisers also encouraged the alumni to visit the campus occasionally and be part of the journey to excellence.

Industry-meet

The Department of Mechanical Engineering organised a focused Industrial Meet on June 21, 2025, titled “Demystifying Fracture Mechanics for Industry: Safe Design, Analysis and Operation of Pressure Vessels, Piping & Pipelines & Green Hydrogen Storage and Transportation Equipment.” This specialised workshop was designed for professionals from well-established Refinery, Petrochemical and Oil & Gas, sectors and the emerging Green Hydrogen industry.

The industry meet commenced with a brief keynote address by Dr Dipak K Chandiramani, Independent Consultant (Mumbai) & Chair, ASME PVP Division, India, on the topic “Quality Assurance in Fabrication: Perspectives from Industry Standards.” His talk laid a strong foundational understanding, setting the stage for the central theme of the meet. visited the laboratories of the Department of Mechanical Engineering and discussed opportunities for commercialising the available facilities. He expressed interest in promoting our industrial training programs on ASME’s Master Class – Advanced Learning Programs webpage and invited faculty members to participate in the upcoming ASME code development committee meetings.

Dr Gurumurthy Kagita, Professor of Practice, Department of Mechanical Engineering, SRM AP, the main resource person for the event, shared his extensive industry experience relevant to the theme of the meet and explained the practical applications of fracture mechanics in the refinery, petrochemical, and oil & gas sectors, as well as in the emerging green hydrogen industry.

The meet began with an introduction to the key concepts of fracture mechanics, presented in a simple and accessible manner for industry professionals. Emphasis was placed on visual learning through graphics and animations rather than traditional classroom-style lectures. In the latter part of the session, the focus shifted to applying fracture mechanics in alignment with various international industry codes and standards. Several case studies were presented using in-house-developed software modules, including Brittle Fracture Screening, MPT envelopes for hydro-processing reactors, etc.

Industry-meet

The meeting saw external participants from professionals across various industries, faculty members from the Department of Mechanical Engineering, and students, including both B.Tech. and Ph.D. scholars.

Several industry participants expressed strong interest in future collaboration with SRM University. Multiple organisations, including Anup Engineering, Jindal Renewable Pvt. Ltd., and Ratnamani Metals and Tubes Ltd., expressed interest in signing Memoranda of Understanding (MoUs) with SRM University for future collaboration in advanced engineering services. Several companies also expressed willingness to offer internships and placement opportunities to SRM AP students.

Industry representatives showed keen interest in collaborative research and consultancy projects, particularly in areas such as hydrogen pipeline design, digital twin tools, and numerical assessment of hydrogen-compatible materials.

These outcomes collectively highlight strong potential for long-term industry-academia partnerships and increased opportunities for students in training, research, and employment.

What happens when you can use natural waste like orange peels and rice husks to extract valuable metals from old batteries, electric vehicles, etc.? Exploring innovative and eco-friendly methods to recover useful metals, Dr Deblina Dutta, Assistant Professor, Department of Environmental Science and Engineering, and her research scholars, Ms Syed Suffia Iqbal and Mr Pranav Prashant Dagwar, have published a paper titled “Recovery of metals from spent lithium-ion batteries using agricultural waste, applications and circularity framework” in the prestigious Q1 journal Separation and Purification Technology having an impact factor of 8.2.

Abstract

This research explores an innovative, sustainable method to recover valuable metals (Li, Co, Ni, Mn) from spent lithium-ion batteries (LIBs) using agricultural waste such as rice husk, orange peels, tea waste, and sugarcane molasses. These organic wastes serve as eco-friendly, cost-effective leaching agents. The study compares conventional and biomass-based recovery methods, evaluates environmental impacts, and aligns the approach with circular economy principles to promote green technology and resource optimization.

Practical Implementation/ Social Implications of the Research

  • Eco-friendly Recycling: Offers an alternative to hazardous chemical recycling by using biodegradable, non-toxic agricultural waste.
  • Waste Valorisation: Transforms agricultural waste into a valuable resource, reducing landfill and open burning.
  • Resource Recovery: Helps recover critical battery metals, reducing dependence on mining.
  • Circular Economy: Supports sustainability goals by promoting a closed-loop recycling system.
  • Policy Relevance: Aligns with SDGs such as clean energy (SDG 7), sustainable industries (SDG 9), and responsible consumption (SDG 12).

Collaborations

  1. Ain Shams University, Egypt
  2. Central Metallurgical R&D Institute (CMRDI), Egypt

Future Research Plans

  • Scale up biomass-based metal recovery to pilot or industrial level.
  • Develop hybrid leaching methods combining microbes and green solvents.
  • Expand to rare earth element (REE) recovery from other waste streams.

Link to the article

Fig. 2. Conventional and technologically advanced process for recycling LIBs.

Fig. 9. Sustainability and circular economic applications in LIBs management.

In the digital era, enhancing wireless connectivity is vital for systems’ accurate and efficient functioning. Working on a smart system to catalyse seamless wireless connectivity, Dr Dimpal Janu, Assistant Professor from the Department of Electronics and Communication Engineering, has published a paper “MASSFormer: Mobility-Aware Spectrum Sensing using Transformer-Driven Tiered Structure” in the Q1 journal IEEE Communications Letters. She has developed a novel cooperative spectrum sensing method based on MASSFormer that uses artificial intelligence, specifically a transformer model, to decide when to use a communication channel without causing interference.

Abstract

We develop a novel mobility-aware transformer-driven tiered structure (MASSFormer) based cooperative spectrum sensing method that effectively models the spatio-temporal dynamics of user movements. Unlike existing methods, our method considers a dynamic scenario involving mobile primary users (PUs) and secondary users (SUs) and addresses the complexities introduced by user mobility. The transformer architecture utilises an attention mechanism, allowing the proposed method to model the temporal dynamics of user mobility by effectively capturing long-range dependencies. The proposed method first computes tokens from the sequence of covariance matrices (CMs) for each SU. It processes them in parallel using the SU-transformer to learn the spatio-temporal features at the SU-level. Subsequently, the collaborative transformer learns the group-level PU state from all SU-level feature representations. The main goal of predicting the PU states at each SU-level and group-level is to improve detection performance even more. The proposed method is tested under imperfect reporting channel scenarios to show robustness. The efficacy of our method is validated with simulation results that demonstrate its higher performance compared to existing methods in terms of detection probability Pd, sensing error, and classification accuracy (CA).

Practical Implementation of Research

Smart cities & IoT Networks:

  • MASSFormer enables intelligent spectrum sharing among a multitude of IoT devices in urban environments.
  • It improves communication reliability in crowded and mobile settings, such as vehicular networks, smart traffic systems, or surveillance setups.

Social Implications of Research

MASSFormer is built to handle the mobility of users, which is essential in rural, remote, or developing areas where users often access the internet via mobile networks rather than fixed infrastructure. By enabling more efficient use of existing spectrum, the method helps extend internet access to remote and underserved regions, supporting education, healthcare, and economic development.

Future Research Plans

  1. Planning to develop hybrid models that blend classical detection with neural inference for improved interpretability and reliability.
  2. Use of federated learning so that SUs can collaboratively train models without sharing raw data.

Link to the article

The proactive approach of Green Chemistry is driven by the cardinal rule of prevention rather than remediation of pollution. Analysing various eco-friendly ways to reduce waste, improve recycling, and help industries and farmers benefit from organic waste, Dr Debajyoti Kundu, Assistant Professor from the Department of Environmental Science and Engineering and his scholar Mr Arun Bharati, has published a paper titled “Anaerobic digestion-derived digestate valorization: Green chemistry innovations for resource recovery and reutilization”, in the Q1 journal Green Chemistry having an impact factor of 9.3.

Their research looked into eco-friendly ways to turn the bi-product of anaerobic digestion – digestate into valuable products like organic fertilisers, chemicals, and even ingredients for making plastics. The team also explores how to recover nutrients like nitrogen and phosphorus from it.

Abstract

Anaerobic digestion (AD) is a sustainable technology that converts organic waste into biogas, producing digestate as a by-product. This review investigates innovative strategies for digestate valorization through green chemistry approaches, emphasising its transformation into valuable resources such as biochar, bio-based polymers, and high-value chemicals like volatile fatty acids and humic substances. Additionally, the study explores nutrient recovery techniques like ammonia stripping and struvite precipitation. Through techno-economic and life cycle assessment perspectives, the work promotes digestate reutilization within a circular bioeconomy to enhance environmental sustainability and support net-zero goals.

Practical Implementation/ Social Implications of the Research

The research offers practical solutions for managing the large volumes of digestate generated in biogas plants. By converting digestate into biofertilisers, biochar, and industrial chemicals, we reduce the environmental burden of waste disposal and create economic opportunities for rural and urban stakeholders. These innovations support sustainable agriculture, reduce reliance on synthetic fertilisers, and promote clean technology, aligning with national and global sustainability goals, including SDGs and the circular economy.

Collaborations

The study was collaborative between SRM University–AP, SRMIST, Vignan’s Foundation, University of North Bengal, University of Burdwan, CSIR–NEERI, Thapar Institute, and Virginia Tech (USA).

Future Research Plans

The research lab focuses on the eco-friendly valorization of diverse organic wastes using green chemistry and sustainable bioprocessing approaches. We aim to develop scalable technologies for producing high-value biochemicals, enzymes, and biomaterials. These efforts are aligned with key UN SDGs.

Link to the article

Dr Prasun Goswami, Assistant Professor from the Department of Environmental Science and Engineering, published a paper titled “Microplastics: Hidden drivers of antimicrobial resistance in aquatic systems”, in the Q1 journal, NanoImpact. His research reveals a concerning connection between microplastics and antimicrobial resistance in oceans. The study uncovers how microplastics in our oceans can harbour antibiotic-resistant pathogens, posing significant threats to marine ecosystems and human health. The paper not only sheds light on the topic but also proposes essential steps to better understand and manage the emerging threat.

Abstract

Tiny plastic particles, called microplastics, are commonly found in oceans, rivers, and lakes. These particles quickly gather layers of bacteria and other microbes, forming what scientists call the “plastisphere.” This plastisphere can carry harmful bacteria, including those that are resistant to antibiotics. Together, these plastic-based communities and the genes they carry make up what’s now being called the “Plastiome.” This review looks at how microplastics interact with bacteria and antibiotic resistance in water environments. It highlights how these plastics can collect and spread dangerous germs and genes that make infections harder to treat. The result is a growing health risk not just for marine life, but also for people. The review also points out areas where more research is needed and suggests ways to better understand and manage the spread of antibiotic resistance through plastic pollution in water.

Practical Implementation/ Social Implications of the Research

Understanding the Plastiome—the microbial life thriving on microplastics—is not just a scientific curiosity; it has real-world consequences. As these plastic particles travel through our oceans, they act as floating hubs for antibiotic-resistant bacteria, which can potentially enter the food chain via seafood or contaminate drinking water sources. The research highlights the urgent need for improved waste management, plastic use reduction, and policy frameworks to monitor microplastic pollution and its microbial cargo. By identifying how microplastics help spread antimicrobial resistance (AMR), the study can help inform public health strategies, guide marine conservation policies, and support international efforts to tackle both plastic pollution and the growing AMR crisis. In essence, tackling the Plastiome is not just about saving the oceans; it’s about protecting ecosystems, public health, and the future.

Collaborations

This work was conducted in collaboration with the National Institute of Animal Health, National Agriculture and Food Research Organization (NIAH-NARO), Tsukuba, Japan.

Future Research Plans

As part of the ongoing research, Dr Prasun explores how different plastic polymers interact with microbial communities and antibiotic resistance (AMR) genes in aquatic environments. Not all plastics behave the same—some may provide a more favourable surface for harmful microbes or facilitate the spread of resistance genes more efficiently. By understanding these polymer-specific interactions, he aims to identify which types of plastics pose the greatest environmental and public health risks. This research has important implications for designing safer materials, guiding environmental regulations, and developing strategies to curb the spread of AMR through plastic pollution in marine and freshwater ecosystems.</p

Link to the Article

In the growing need for clean energy solutions, Dr Sabyasachi Chakrabortty, Associate Professor, Department of Chemistry, Dr Uday Kumar Ghorui (Post Doctoral Scholar) and Mr Gokul Sivaguru (PhD scholar) have filed and published the invention of “Electrode material” with Application Number: “202441075507” in the Patent Office Journal, on developing a low-cost, eco-friendly electrode material using a simple hydrothermal process. The research team has developed a pioneering Ternary Transition Metal Oxide (TTMO) nanocomposite electrode for the hydrogen evolution reaction (HER). Their work focuses on creating a sustainable alternative to fossil fuel-based hydrogen production methods, which currently generate significant CO₂ emissions.

Abstract

This disclosure focuses on developing a low-cost, earth-abundant Ternary Transition Metal Oxide (TTMO) nanocomposite electrode for efficient, clean hydrogen production, addressing the depletion of fossil fuels and the CO₂ emissions from current methods like methane reforming and coal gasification. Using a simple hydrothermal process, the TTMO electrode demonstrates excellent electrochemical HER performance, with low overpotential and 100-hour stability, despite challenges in cost, infrastructure, and safety for hydrogen energy generation.

Practical Implementation/ Social Impact of the Research

Practical Implementation

  • The research develops a cost-effective TTMO nanocomposite electrode using a scalable hydrothermal method
  • Enables efficient hydrogen production with low overpotential
  • Offers a practical, sustainable alternative to fossil-fuel-based methods
  • Achieves 100 hours of stable hydrogen generation while minimizing CO₂ emissions
  • Enables efficient hydrogen production with low overpotential

Social Implications

  • Addresses the critical need for clean, renewable energy sources
  • Provides a sustainable solution for hydrogen production without carbon emissions
  • Makes green hydrogen technology more accessible through cost-effective materials
  • Contributes to global efforts in reducing dependence on fossil fuels

Future Research Plans

  • Optimising the TTMO nanocomposite’s composition and synthesis to boost HER efficiency
  • Improving stability for industrial-scale hydrogen production
  • Exploring integration into real-world energy systems
  • Investigating other earth-abundant materials to advance affordable, green hydrogen technologies

Dr Vemula Dinesh Reddy, Assistant Professor, Department of Computer Science and Engineering, has been granted a patent for his invention “A System And A Method for Fog-Based Animal Intrusion Detection” with the Application No: 202341026013, in the Indian Patent Official Journal. The invention acts as a groundbreaking fog computing-based system designed for real-time detection of animal intrusions in sensitive areas using smart sensors for instant alerts.

Abstract

This research introduces an intelligent system using fog computing to detect animal intrusions in sensitive or protected zones such as farmlands, highways, and forest borders. The system enables real-time data processing closer to the site of intrusion, offering faster detection and reduced dependency on centralised cloud systems. Furthermore, we proposed the Quantum-Inspired optimisation technique called Quantum Evolutionary Algorithm.

Practical Implementation/ Social Implications of the Research

Through this invention, we can:

  • Prevent crop destruction and reduce human-wildlife conflict.
  • Enhance safety on highways where animal crossings are common.
  • Support forest conservation efforts by enabling non-intrusive monitoring.
  • Reduce latency and bandwidth costs by processing data locally (via fog computing).

Future Research Plans

  • Integrating AI-based species classification to identify specific animals.
  • Creating a scalable mesh network for larger geographic coverage.
  • Enhancing energy efficiency through solar-powered edge nodes.
  • Extending the system to include drone-based visual surveillance.

The digital age is flooded with multimedia content ranging from articles and podcasts to videos and images, spanning multiple languages. The challenge isn’t just accessing information but understanding and summarising it efficiently. Addressing this need, a pioneering patent titled “A System and Method for Multimodal Multilingual Input Summarization Using Quantum Motivated Processors” (Application Number 202341005519) has been granted to Dr Ashu Abdul, Assistant Professor in the Department of Computer Science and Engineering, and Mr Phanidra Kumar S, PhD Scholar, as published in the Indian Patent Office Journal. This innovative system converts all kinds of media like text, images, audio, and video into descriptive text, then leverages quantum-inspired algorithms to extract and stitch together the most relevant sentences and visuals, thereby crafting a perfect summary.

Abstract

This research details a system and method for summarizing multimodal and multilingual input data by leveraging quantum-motivated processors. The system is designed to handle input documents comprising text, audio, image, and video data, potentially in multiple languages. A pre-processing engine extracts textual descriptions from all these modalities (using deep learning, CNN, VAF, Python), merging them into a unified text corpus. A quantum enabler module assigns initial probabilities and encodes sentences from this corpus into binary states (0s or 1s), reflecting a quantum measurement concept (using randint). A selection module, utilizing an objective/fitness function incorporating factors like term frequency, sentence length, pronoun presence, coverage (QCSS-based similarity), and title relevance (Sentence-to-Title QCSS), calculates a fitness score for each encoded sentence and shortlists relevant ones using a “radiant function”. This module also handles duplicate removal based on QCSS. A rearrangement module scores and reorders the shortlisted sentences based on metadata (like publishing date) and scores (like ROUGE). A summary generation module produces a textual summary. Concurrently, an image selector engine selects a relevant image from the input data, primarily based on the image’s textual description and the generated summary, often using QCSS (Quantum Cosine Similarity Score). Finally, an output engine merges the textual summary with the selected image to create a multioutput (MO) summary. The system may also include a machine translation engine to translate non-English extracted descriptions into English before summarization, if needed. The approach employs quantum measurement and adaptive quantum rotation gates within an evolutionary framework (suggesting a Quantum Genetic Algorithm approach, referred to as MSQMGA) to find optimal summary sentences, demonstrating improved performance and efficiency compared to traditional Genetic Algorithms.

Practical Implementation of the Research

The system’s design outlines a modular architecture suitable for software or hardware implementation, involving distinct processing engines (Input, Pre-processing, Quantum Enabler, Selection, Rearrangement, Summary Generation, Image Selector, Output, and potentially Machine Translation). Key technical details include:

  • Pre-processing: Use of Python, deep learning models (VAF, CNN) for extracting textual descriptions from audio/video/image data.
  • Quantum Enabler/Selection: Assignment of initial probability (1/√2), encoding via a randomized quantum measurement model (randint(0,1) <= alpha_i), fitness function incorporating multiple weighted factors (fs = [0.75 * ((w1) * C * + w2 * pn * Ts) + 0.25 * S1] * Tf), QCSS for similarity checks (summary-to-document, sentence-to-title, intra-sentence, image selection), shortlisting via a radiant function, duplicate removal via QCSS.
  • Rearrangement: Sorting shortlisted sentences based on metadata like publishing date and ROUGE score.
  • Image Selection: Deep learning models like QTL-based CNN-LSTM, thresholding (e.g., 0.85).
  • Multilingual Handling: Explicit mention of a Machine Translation Engine (122) to translate non-English extracted text into English
  • Performance: Claims of achieving ROUGE-1 scores (e.g., 0.78) and QCSS scores (e.g., 92% for image ID), and being “quite faster” compared to traditional GA approaches.
  • Datasets: Evaluation conducted using DUC 2005, DUC 2007, Indian Express datasets for text summarization, and Flickr 8k, Flickr 30k, Indian Express datasets for image description (ID).

These specifics suggest practical implementation could involve developing software modules that utilize libraries for deep learning (e.g., TensorFlow, PyTorch with CNN, LSTM components), natural language processing (e.g., NLTK, spacy for tokenization, POS, lemmatization), and potentially frameworks for simulating or interfacing with quantum-inspired algorithms. The “real-time applications” aspect implies design considerations for efficiency and processing speed. Potential deployments include news aggregation platforms, content management systems, competitive intelligence dashboards, cross-cultural communication tools, or applications for analysing vast archives of mixed-media data.

Social Impact

Beyond basic information access, this technology has the potential to foster greater understanding and reduce bias by providing summarized content across linguistic and cultural divides. It could empower individuals and organisations to consume and analyse global information landscapes more effectively. For educators, it could facilitate the creation of multimodal learning materials from diverse sources. For researchers, it could accelerate literature review across different fields and languages. However, it also raises potential implications related to the source and neutrality of the summarisation models themselves – whose perspective is encoded, and how might summaries differ based on training data or algorithmic biases? Ethical considerations around information representation and potential manipulation of summaries would be important as such technologies become more widely adopted.

Future Research Plans

Although the patent doesn’t explicitly list a roadmap, the detailed description and stated advantages imply several potential future research directions and refinements based on the current work:

  1. Algorithmic Refinement: Further optimizing the “quantum-motivated” genetic algorithm (MSQMGA) framework, including the fitness function weights (w1, w2 are mentioned as trainable parameters), the “radiant function” for shortlisting, and the quantum measurement mapping.
  2. Modality Integration: Enhancing the pre-processing and integration of information from different modalities, potentially exploring more sophisticated methods for cross-modal semantic understanding beyond extracting textual descriptions.
  3. Cross-Lingual Capabilities: Improving the multilingual summarization accuracy, potentially integrating more advanced machine translation techniques directly within the summarization process or extending the quantum-motivated selection mechanism to handle multi-language sentence comparisons natively.
  4. Quantum Hardware Exploration: Investigating the feasibility and performance benefits of implementing parts of the system, particularly the quantum enabler and selection modules, on actual quantum computing hardware as it matures, moving beyond the current “quantum-motivated” (inspired/simulated) approach.
  5. Scalability and Real-time Performance: Further developing the system to handle even larger volumes of multimodal, multilingual data efficiently for true real-time applications.
  6. Evaluation and Benchmarking: Expanding testing on a wider range of diverse datasets and benchmarking against more varied state-of-the-art multimodal and multilingual summarization techniques.
  7. Summarization Quality: Focusing on subjective quality metrics of the generated summaries, such as coherence, readability, and conciseness, in addition to objective metrics like ROUGE
  8. Image Selection Enhancement: Refining the image selection process, potentially considering factors beyond just textual description and summary similarity, such as image quality, saliency, and contextual relevance within the broader multimodal input.