CCIR: The Next Frontier in Mobile Network Evolution - Integrating Communication and Computing for Enhanced Services
The mobile RAN is the bridge and enabler between end users and application services, and fortunately, the computational capacity of base stations in RANs[1] has experienced tremendous growth accompanied by increasingly stringent service requirements from emerging applications such as AR/VR. These two trends imply an unprecedented opportunity that the abundant computing resources in base stations could be leveraged to host latency-sensitive applications if being managed properly, thereby giving rise to a new vision named Communication and Computing Integrated RAN (CCIR), where not only communication but also computing services are delivered by RANs in a coherent way[2]. In fact, CCIR implies an even more radical departure of designing future mobile networks-going beyond regarding the role of RANs as connecting links. This article provides an elaboration on the fundamental design philosophies and principles of CCIR, the logical architecture. We will explore further on key tech-nologies toward realizing our vision. Specifically, we provide a thorough view on what CCIR really means - it involves different integration granularities between communication and computing functions; meanwhile it keeps evolving until approaching real-time joint scheduling and holistic resource management based on one unified infrastructure. To assess the feasibility and advantages brought by CCIR, several field experiments were carried out where computing resources are migrated within different base stations. In conclusion, CCIR is a prospective evolution direction for future mobile networks dealing with communication-computation-converged requirements in new service use scenarios[3]. Using current infrastructure more intelligently and efficiently will facilitate better user experience, greener mobile networks as well as serve as better platform for future advancements in wireless technology. Grenze Scientific Society, 2025.
Raj, E. Babu; Padmalal, S.; Ram Mohan, N.R.; Shaji, B.
16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;1;pp.4318-4324
Restricted Access; Hardcopy may be available in the library
https://www.scopus.com/pages/publications/105034172713?origin=resultslist
Raj E.B., Department of Computer Science and Engineering, CHRIST University, Bengaluru, India; Padmalal S., Department of Computer Science and Engineering, Mangalam College of Engineering, Kottayam, Kerala, Ettumanoor, India; Ram Mohan N.R., Department of Computer Science and Engineering, Royal College of Engineering and Technology, Akkaikavu, Kerala, Thrissur, India; Shaji B., Department of Computer Science and Engineering, Nehru College of Engineering and Research Centre, Pampady, Kerala, Thrissur, India
Enhanced Spam Detection in Short Message Service using Hybrid Techniques
Convolutional Neural Network (CNN); Machine Learning; Nae Bayes; SMS Spam
Receiving unwanted text messages, or SMS spam, costs consumers time and money and poses a security concern. To address this issue, we can deploy a system that recognizes and automatically filters out undesirable messages. This method, a testament to the advancement in technology, employs machine learning algorithms that gain knowledge from a pool of communications classified as spam or not. Managing various message contents and languages is one of the system's unique challenges. Notwithstanding these challenges, the approach may be effective in reducing unsolicited communications, improving the security of people's mobile devices and saving them time and money. To address this issue, a variety of machine learning approaches have been employed, ranging from more modern deep learning methods like Convolutional Neural Networks (CNNs) to more traditional ones like Naive Bayes. It is common practice to assess the effectiveness of SMS spam classifiers using measures like as F1-score, precision, and recall. All things considered, SMS spam classification is crucial for protecting the security and privacy of mobile phone users and has useful applications in everyday situations. Grenze Scientific Society, 2025.
Raj, E. Babu; Barona, R.; Mary, N. Ansgar; George, Shiju
16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;1;pp.4203-4209
https://www.scopus.com/pages/publications/105034072843?origin=resultslist
Raj E.B., Christ University, Bengaluru, Bengaluru, India; Barona R., St. Xavier's Catholic College of Engineering, IT, Chunkankadai, India; Mary N.A., St. Xavier's Catholic College of Engineering, IT, Chunkankadai, India; George S., Christ University, Bengaluru, Bengaluru, India
Automated Waste Segregation using Raspberry Pi and Deep Learning
automation; computer vision; Convolutional Neural Network (CNN); environmental sustainability; machine learning; Real-time waste detection; smart waste management; TrashNet dataset; Waste segregation
With rapid urbanization and increasing waste generation, efficient waste segregation has become a critical challenge for sustainable waste management. Traditional waste disposal methods rely heavily on manual sorting, which is inefficient, labor-intensive, and prone to errors, leading to improper recycling and environmental hazards. To address these problems, a clever waste segregation method is presented in this research. It automatically sorts waste into four categoriesglass, metal, plastic, and paper/cardboardusing computer vision and machine learning. A 720p webcam is used to collect images in real time, and the system is powered by a Raspberry Pi 4B with 4GB of RAM. A Convolutional Neural Network (CNN) model that was developed using the TrashNet dataset forms its basis. The model can correctly identify the waste in the photos due to an optimized training method that incorporates data augmentation, regularization strategies, and early stopping to prevent overfitting. An SG90 servo motor controls the lid, ensuring the garbage is placed in the appropriate compartment, while an MG996R servo motor swings the bin into place after the waste has been classified. The bin and lid go back to their initial places once the garbage has been dumped, preparing the system for usage again. Here, we are able to combine automated mobility, automated categorization, and real-time waste detection with embedded technologies, machine learning, and automation to separate waste with the least amount of human intervention. Furthermore, the system's scalability and adaptability make it appropriate for smart city initiatives, urban trash management, and wider industrial application. Consequently, this technology helps to tackle intelligent waste management problems, which facilitates the emergence of a sustainable and eco-friendly future. The system achieved a testing accuracy of 88.1%, showcasing its effectiveness and reliability. Grenze Scientific Society, 2025.
Khan, Fazal; Priya, Sharon Roji C.; Burhanuddin, Ibrahim; Kurian, Joshua P.K.
16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;2;pp.8620-8625
https://www.scopus.com/pages/publications/105034833401?origin=resultslist
Khan F., Department of Computer Science and Engineering, Christ University, Bangalore, India; Priya S.R.C., Department of Computer Science and Engineering, Christ University, Bangalore, India; Burhanuddin I., Department of Computer Science and Engineering, Christ University, Bangalore, India; Kurian J.P.K., Department of Computer Science and Engineering, Christ University, Bangalore, India
Leveraging Hybrid Dual-Level Contextual Attention and Spiking Neural Networks for Effective Hepatic Malignancy Diagnosis
Dual-Level Contextual Attention; Hepatic Malignancy; Liver Cancer; Medical Data Processing; Medical Imaging; Spiking Neural Networks
s Abstract Liver cancer remains the leading cause of cancer-related mortality, prompt-ing advanced diagnostic techniques for early detection and accurate classification in the health sector worldwide. Multifaceted deep learning methods have shown significant potential in medical imaging, but challenges exist in capturing intricate contextual information. In our research, we propose a novel hybrid framework that integrates Dual-Level Contextual Attention (DLCA) with Spiking Neural Networks (SNNs) to enhance the diagnosis of liver cancer. The proposed framework uses a DLCA mechanism that effectively extracts both local and global contextual features within the medical images and aids in precise lesion differentiation. The SNNs module supports computational efficiency and robust pattern recognition, enabling precise identification of subtle cancerous patterns by reducing redundant activations while preserving critical diagnostic information. Experimental evaluations on publically available datasets demonstrate the effectiveness of our work, showcasing its reliability in clinical applications. Moreover, the model offers a direction for future AI-assisted diagnostic tools in medical imaging and oncology. Grenze Scientific Society, 2025.
George, Shiju; Joseph, Asha; Raj, E. Babu; Shiju, Angel
16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;2;pp.14252-14258
https://www.scopus.com/pages/publications/105034865412?origin=resultslist
George S., Christ University, Karnataka, Bengaluru, India; Joseph A., New Horizon College of Engineering, Karnataka, Bengaluru, India; Raj E.B., Christ University, Karnataka, Bengaluru, India; Shiju A., Christ University, Karnataka, Bengaluru, India
Real-Time Implementation of Deep Learning Model for Polyp Classification and Segmentation in Medical Imaging
colonoscopy; Deep learning models; medical imaging; polyp classification; polyp detection; polyp segmentation; preliminary tests
Real-time deep learning models for polyp identification and segmentation in medical imaging. Recognising the limits of current database systems for real-time applications, the research focusses on creating a deep learning model capable of recognising crucial picture components to aid in precise polyp categorisation. The suggested methodology is intended for realtime, practical healthcare and diagnostic applications that need quick polyp detection via preliminary colonoscopy testing. Performance investigation demonstrates that ResNet50 and EfficientNet B2 outperform other models, implying that they are suitable for real-world application and optimal outcomes. 2025 Bharati Vidyapeeth, New Delhi.
Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development, INDIACom 2025;
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.23919/INDIACom66777.2025.11115652 https://www.scopus.com/pages/publications/105016230225?origin=resultslist
Singhal P., Maharishi University of Information Technology, Department of Computer Science and Engineering, Lucknow, India, School of Sciences, Christ (Deemed to be University), Department of Computer Science, Delhi-NCR, India; Yadav R.K., Maharishi University of Information Technology, Department of Computer Science and Engineering, Lucknow, India
Leveraging ML Based Technique for Mobile Sales Forecasting
Artificial Intelligence; Decision Tree; Forecasting; Machine Learning; Mobile
The mobile phone industry is very competitive, so mobile sales forecasting is now imperative for businesses to forecast demand and order inventory in advance to plan strategically. This research focuses on the higher accuracy of mobile sales prediction and studies several machine learning models like Brand, Ratings, RAM, ROM, Battery- Power, pixel- height- and width, and targets alongside Camera Details as an alternate set to association rule mining. A real-time dataset that covers real-world mobile phone sales data has been collected and had its features pre-processed to fill in missing values and do the definite column encoding. Dataset were tested to understand the model performance of several predictive models, such as Decision Trees, Support Vector Machine (SVM), and ensemble methods (Random Forest and Gradient Boosting). The performance of each model was measured by accuracy, precision, recall, and F1-score. To address the issue of class in the sales categories (Low, Medium, High), stratified sampling and Synthetic Minority Over-sampling Technique (SMOTE) techniques were used. The results showed the predictive solid abilities of all the models in forecasting sales for different segments, with ensemble models performing better than individual classifiers in terms of prediction accuracy and robustness. This approach was further strengthened by applying hyperparameter tuning and cross-validation to improve the model's performance. The results are predicted to drive mobile retailers in the direction of improving demand forecasting and making data-driven decisions towards operational efficiency. 2025 Bharati Vidyapeeth, New Delhi.
Patel, Dharma; Sambandam, Rakoth Kandan; Vetriveeran, Divya; Jenefa, J.; Aruna, S.K.; Vinodha, D.
https://doi.org/10.23919/INDIACom66777.2025.11115351 https://www.scopus.com/pages/publications/105016114906?origin=resultslist
Patel D., Soet, Christ University, Dept. of Cse, Bangalore, India; Sambandam R.K., Soet, Christ University, Dept. of Cse, Bangalore, India; Vetriveeran D., Soet, Christ University, Dept. of Cse, Bangalore, India; Jenefa J., Soet, Christ University, Dept. of Cse, Bangalore, India; Aruna S.K., Soet, Christ University, Dept. of Cse, Bangalore, India; Vinodha D., Soet, Christ University, Dept. of Cse, Bangalore, India
Sustainable X-Band Microwave Absorber Using Tea Waste and Recycled Carbon Composites
Microwave absorber; recycled battery carbon; sustainable; tea waste; X-band
This paper introduces an innovative eco-friendly flat microwave absorber designed for X-band applications, utilizing plant waste and recycled materials, thereby contributing to a circular economy. The composite material is composed of 75 % used tea powder and 25 % carbon sourced from discarded batteries. The electromagnetic (EM) absorption performance of the proposed composite has been characterized, revealing enhanced absorption capabilities, averaging around 26.2 % for a thickness of 5 mm, compared to a 100 % tea waste configuration across the X-band frequency range. Furthermore, the results indicate that EM absorption improves with increasing frequency. The environmental advantages of this composite, including waste reduction and a minimal carbon footprint, position it as an attractive alternative to conventional RF absorbers. This sustainable solution shows promise for applications in EM interference shielding and microwave energy harvesting. 2025 European Association on Antennas and Propagation.
Kumar, Naveen; Koohestani, Mohsen; Roy, Sourav; Murthy, Hari
EuCAP 2025 - 19th European Conference on Antennas and Propagation;
https://doi.org/10.23919/EuCAP63536.2025.11000026 https://www.scopus.com/pages/publications/105007515129?origin=resultslist
Kumar N., Eced, School of Engineering and Technology, Christ University, Bangalore, India; Koohestani M., ole Supieure d'ectronique de l'Ouest (ESEO), Angers, 49107, France; Roy S., Eced, School of Engineering and Technology, Christ University, Bangalore, India; Murthy H., Eced, School of Engineering and Technology, Christ University, Bangalore, India
MVTamperBench: Evaluating Robustness of Vision-Language Models
Multimodal Large Language Models (MLLMs), are recent advancement of Vision-Language Models (VLMs) that have driven major advances in video understanding. However, their vulnerability to adversarial tampering and manipulations remains under-explored. To address this gap, we introduce MVTamperBench, a benchmark that systematically evaluates MLLM robustness against five prevalent tampering techniques: rotation, masking, substitution, repetition, and dropping; based on real-world visual tampering scenarios such as surveillance interference, social media content edits, and misinformation injection. MVTamperBench comprises 3.4K original videos, expanded into over 17K tampered clips covering 19 distinct video manipulation tasks. This benchmark challenges models to detect manipulations in spatial and temporal coherence. We evaluate 45 recent MLLMs from 15+ model families. We reveal substantial variability in resilience across tampering types and show that larger parameter counts do not necessarily guarantee robustness. MVTamperBench sets a new benchmark for developing tamper-resilient MLLM in safety-critical applications, including detecting clickbait, preventing harmful content distribution, and enforcing policies on media platforms. We release all code, data, and benchmark to foster open research in trustworthy video understanding. 2025 Association for Computational Linguistics.
Agarwal, Amit; Panda, Srikant; Charles, Angeline; Patel, Hitesh; Kumar, Bhargava; Pattnayak, Priyaranjan; Rafi, Taki Hasan; Kumar, Tejaswini; Meghwani, Hansa; Gupta, Karan; Chae, Dong-Kyu
Proceedings of the Annual Meeting of the Association for Computational Linguistics;pp.18804-18828
Association for Computational Linguistics (ACL)
https://doi.org/10.18653/v1/2025.findings-acl.963 https://www.scopus.com/pages/publications/105028648871?origin=resultslist
Agarwal A., Liverpool John Moores University, United Kingdom; Panda S., Birla Institute of Technology, India; Charles A., Christ University, India; Patel H., New York University, United States; Kumar B., Columbia University, United States; Pattnayak P., University of Washington, United States; Rafi T.H., Hanyang University, South Korea; Kumar T., Columbia University, United States; Meghwani H., Liverpool John Moores University, United Kingdom; Gupta K., New York University, United States; Chae D.-K., Hanyang University, South Korea
Online Fake News Detection using Machine Learning and Natural Language Processing Algorithms
BERT; Fake News Detection; Logistic Regression; Machine Learning; Natural Language Processing; Precision-Recall; Random Forest; ROC AUC; Text Classification
Fake news in the digital platforms plays a vital threat to many of the influencing factors of the society. The research focuses on the challenges of online fake news detection from the performance achieved by the machine learning classifiers using the natural processing techniques. Logistic Regression and Random Forest are taken to test the dataset containing labelled fake and real news for the study. The models are evaluated using the key metrices as accuracy, precision, recall, F1-score, confusion matrix and from the ROC AUC score. The research demonstrates which model is more reliable and more consistent for finding the fake and real news. For semantic text analysis BERT embeddings are used in the research as it will help analyze the article with more accuracy and precision. The metrics evaluation and the ROC curve of the both models helps in knowing even the slight deviations projected in the metrics. The differences in the valuations and the metrics values showed the capabilities of both the models in detecting the online news as fake or real. The comparisons made from the two models helps in evaluating the models and to understand the limitations of it as the analyze and detection of the text is more complicated. The research aims to deliver a strong foundation for real-time fake news detection in this new era. 2025 IEEE.
Vincent, Stephin Tom; George, Jossy; Kumar, Pawan
2025 World Skills Conference on Universal Data Analytics and Sciences, WorldSUAS 2025;
https://doi.org/10.1109/WorldSUAS66815.2025.11199266 https://www.scopus.com/pages/publications/105022238751?origin=resultslist
Vincent S.T., CHRIST (Deemed to Be University), India; George J., CHRIST (Deemed to Be University), India; Kumar P., CHRIST (Deemed to Be University), India
Efficient Multilingual Language Detection Using Machine Learning Algorithms
FastText; Long Short-Term Memory; Natural Language Processing; Recurrent Neural Networks; Support Vector Machines
Natural Language Processing (NLP) is one of the important technologies in recent days, because language detection this NLP is play a vital role. This research focuses on detecting languages using various machine learning algorithms. FastText, Recurrent Neural Networks (RNN), Support Vector Machines (SVM) algorithms are used for this experiment. The following datasets are used to take this result that is Europarl and Tatoeba. The proposed method is to preprocess, train, and test these models. Evaluation is done by measuring precision, recall, and F1 score of the three algorithms. Results show that RNN provides precision close perfect or near-perfect results in both bilingual and multilingual datasets. SVM performs with high precision and recall, but less than RNN. Its performance slightly decreases as the dataset increases. On the other hand, FastText, although fast and efficient, drops significantly in performance as the dataset grows, especially with the inclusion of a third language. It provides an all-inclusive methodology that has pinned the strengths and weaknesses of each algorithm, providing valuable insight into which one best fit real-world language detection task: RNN with their ability to handle complex sequences, SVM for large-scale high-dimensional sparse features, and FastText for simpler, smaller dataset. 2025 IEEE.
Josemon, Felix; Anil, Anakha; Jayapandian, N.
https://doi.org/10.1109/WorldSUAS66815.2025.11199239 https://www.scopus.com/pages/publications/105022265928?origin=resultslist
Josemon F., Christ University, Department of Computer Science and Engineering, Kengeri Campus, Bangalore, India; Anil A., Christ University, Department of AI, ML and Data Science, Kengeri Campus, Bangalore, India; Jayapandian N., Christ University, Department of Computer Science and Engineering, Kengeri Campus, Bangalore, India
An Extensive Analysis of Artificial Intelligence Integration in Management Approaches
Artificial Intelligence; HRM; LDA; Management; Operational Efficiency; Predictive Analysis
Artificial Intelligence (AI) is now a strategic enabler across a large number of management domains in the digital transformation era. We conducted this review which analyzes AI and its integration into management by 990 peer reviewed publications from 2015 to 2025 from the Scopus database. It filtered studies on AI's role in strategy, HR, finance, operations and decision-making in a systematic manner. Latent Dirichlet Allocation (LDA) formed five key themes of predictive analytics, AI in HR, financial planning, intelligent decision systems, and explainable AI. The findings suggest that digital resilience needs drive the surge of AI related management research after 2019. This review points out emerging trends, difficulties in integrations, as well as critical insights which can orient the future research over such specific studies. 2025 IEEE.
Farooqui, Safia; Radhakrishnan, G.V.; Shankar, Uma; Baryah, Neha; Upreti, Kamal
https://doi.org/10.1109/WorldSUAS66815.2025.11199229 https://www.scopus.com/pages/publications/105022304860?origin=resultslist
Farooqui S., Dr. D.Y. Patil Vidyapeeth, Centre for Online Learning, Pune, India; Radhakrishnan G.V., Kalinga Institute of Technology, Kalinga School of Management, Bhubaneswar, India; Shankar U., Qaiwan International University, Faculty of Management and Social Science, Iraq; Baryah N., Chandigarh Group of Colleges, Department of Forensic Sciences, Punjab, India; Upreti K., CHRIST University, Dept. of Computer Sciecne, India
Thematic Mutual Funds: Performance and Benchmark Analysis Leveraging AI in Futuristic New-Age Investment Themes
AI-driven analytics; Assets under Management (AUM); Banking and Financial Services; ESG (Environmental, Social, Governance); Infrastructure funds; Pharma & Healthcare; Technology funds; Thematic mutual funds
Young investors love thematic mutual funds. By 2022, AUM will reach 1.73 lakh crore. These funds have a great possibility of long-term growth since they are timing the market and investing wisely. This research uses AI-driven analytics to examine a select thematic mutual fund that has done well despite risk, diversity, and market volatility concerns. Five actively managed themes were chosen from various fund firms. Infrastructure, ESG, Technology, Banking and Financial Services, and Pharma & Healthcare. We evaluated the best strategies in each topic using qualitative and AI-enhanced quantitative success metrics, including Alpha, Beta, Standard Deviation, Rolling Return, and Sharpe Ratio. We compared themes to key metrics to evaluate their performance over five years (2018-2022). Themed mutual funds perform well for future savings. In particular, AI-driven asset selection algorithms that were market-appropriate outperformed other purchasers. The findings demonstrate the importance of economic cycle-based themes and clever distribution strategies to maximize profits. This research illuminates investment choices and illustrates how AI-enabled active investing might improve market forecasts, volatility, and returns in themed mutual funds. 2025 IEEE.
Premalatha, K.P.; Patil, Shrinivas R; Renuka, S.; Yadav, Rashmi Akshay
https://doi.org/10.1109/WorldSUAS66815.2025.11199217 https://www.scopus.com/pages/publications/105022284407?origin=resultslist
Premalatha K.P., CMS Business School, JAIN (Deemed-To-Be University), Faculty of Management Studies, Bangalore, 560009, India; Patil S.R., CMS Business School, JAIN (Deemed-To-Be University), Faculty of Management Studies, Bangalore, 560009, India; Renuka S., JAIN (Deemed-To-Be University), Faculty of Management Studies, Bangalore, 560009, India; Yadav R.A., MBA CHRIST (Deemed-To-Be University), School of Business and Management, Bangalore, India
Automated Detection of Deepfakes using Integrated AI and Computer Vision Strategies
Computer Vision; Deepfake Detections; Ensemble Learning; Gradient Boosting; Random Forest; SMOTE
Deepfakes, or artificial intelligence-generated fake videos, are becoming a greater concern for online information trust, personal privacy, and digital content security. This paper presents a straightforward and understandable technique for automatically identifying deepfakes in order to address this significant problem. The approach makes use of conventional computer vision and machine learning methods. The model examines manually produced visual cues such as eye distance, mouth movement, and head tilt in video footage. To increase accuracy, it employs a variety of classifier types, including Random Forest, Gradient Boosting, and a soft Voting Classifier. A method known as SMOTE was used to clean and balance the data, and categorical data was transformed into a format suitable for machine learning models. With an F1-score of 0.9802 and 98% accuracy, the results demonstrate that the Voting Classifier, which combines several models, works admirably while being straightforward and effective. This method makes detection successful and simple to comprehend while offering a helpful tool for swiftly identifying deepfakes, especially on systems with constrained resources. 2025 IEEE.
Varkey, Tojin; George, Jossy; Chanti, S.
https://doi.org/10.1109/WorldSUAS66815.2025.11199153 https://www.scopus.com/pages/publications/105022291507?origin=resultslist
Varkey T., CHRIST (Deemed to Be University), India; George J., CHRIST (Deemed to Be University), India; Chanti S., CHRIST (Deemed to Be University), India
Improving Financial Audits and Management of Compliance using Artificial Intelligence and Secure Cloud Technology
Artificial Intelligence; Compliance Management; Financial Audits; Homomorphic Encryption; Secure Cloud Technology
Modern financial ecosystem requires highly complex audit trails and more stringent compliance issues therefore require highly advanced secure and intelligent systems. This research outlines a hybrid framework which juxtaposes Artificial Intelligence (AI) and Secure Cloud Technology to improve financial audit process and establish strong compliance management. Taking advantage of the strengths of AI, the strengths in question including Natural Language Processing (NLP), anomaly detection and machine learning classifiers, this system is used to enhance data accuracy, and detect irregularities in real time and automate regulatory reporting. At the same time implementation of Zero Trust Cloud Architectures, along with homomorphic encryption, provides data integrity, privacy, and end to end security. The proposed methodology is centred around the integration of intelligent document processing and blockchain-verified logs in the federated learning framework - where both transparency and decentralization are fostered. In addition, predictive analytics are used for the prediction of possible risks and non-compliance incidents to facilitate proactive decision making. Extensive simulations are used to reveal enhanced performance relative to traditional systems, with increased accuracy of anomaly detections, audit traceability, and validation speed-up of compliance. This integration is not only focused on streamlining audit workflows, but can also cut on operational cost and human error as well. The results emphasize the importance of employing AI-enabled secure cloud infrastructures as a primary strategy for financial institutions in a growing regulated digital economy while trying to sustain compliance. The new system achieves a 96.2% rate of accuracy while auditing and only consumes 91.3% the time in compliance to encourage efficiency. 2025 IEEE.
Ramesh, R.; Zokarkar, Sumit; Rupani, Jigar; Sidhu, Kawerinder Singh; Sathiya Santhan, S.; Bhasha, A.
https://doi.org/10.1109/WorldSUAS66815.2025.11199152 https://www.scopus.com/pages/publications/105022294785?origin=resultslist
Ramesh R., SR University, School of Business, Telangana, Warangal, India; Zokarkar S., Prestige Institute of Management and Research, Indore, India; Rupani J., Christ University, School of Business and Management, Bengaluru, 560073, India; Sidhu K.S., Uttaranchal University, Uttaranchal Institute of Management, Uttarakhand, Dehradun, India; Sathiya Santhan S., St Joseph's College of Engineering, L - Mba Department of Management Studies, Omr, Chennai, 019, India; Bhasha A., St Martin's Engineering College, Dhulapally, Department of Information Technology, Telangana, Secunderabad, 500014, India
Strategic Integration of AI in Modern Data Management
Artificial Intelligence; Automation; Challenges; Data Management; Traditional Techniques
The exponential growth of data from sources such as social media, IoT, and enterprise systems has catalyzed a transformative shift in data management practices. This paper explores the integration of artificial intelligence (AI), edge computing, cloud-native frameworks, and graph-based techniques to support intelligent, low-latency, and scalable data processing across complex ecosystems. It presents a comparative analysis of classical versus modern data architectures, highlighting how technologies like Graph Neural Networks (GNN4TS), reinforcement learning, and large language models (LLMs) enable more adaptive, interpretable, and automated pipelines. The study also addresses challenges in legacy system modernization, time-series modeling, and cyber threat detection while underscoring the role of AI in autonomous database management and metadata enrichment. Further, it examines critical risks - including explainability, adversarial vulnerabilities, concept drift, and privacy preservation - associated with AI-integrated data workflows. A structured overview of emerging paradigms such as neuro-symbolic AI, adaptive governance in multi-agent systems, and the potential of quantum computing provides a future-focused lens on intelligent data ecosystems. The insights presented aim to assist researchers, data engineers, and decision-makers in navigating the evolving landscape of AI-driven data management. 2025 IEEE.
Divakaran, Prakash; Parnami, Tanmay; Gaur, Sonali; Baryah, Neha; Gupta, Komal; Upreti, Kamal
https://doi.org/10.1109/WorldSUAS66815.2025.11199110 https://www.scopus.com/pages/publications/105022276679?origin=resultslist
Divakaran P., Himalayan University, Department of Management, Arunchal Pradesh, India; Parnami T., GGSIPU, Dept. of Computer Science & Engineering, New Delhi, India; Gaur S., EMPI Business School, Dept. of Management, New Delhi, India; Baryah N., Chandigarh Group of Colleges, Chandigarh School of Business, Department of Forensic Sciences, Punjab, Mohali, India; Gupta K., Finance Manager Accenture, Bangalore, India; Upreti K., Christ University, Department of Computer Science, Ghaziabad, India
Navigating the Ripple Effect: A Risk-Resilient Framework for Supply Chain Optimization
Artificial Intelligence; blockchain; covid-19; global supply chain
Supply chains around worldwide are growing increasingly vulnerable to intricate and interrelated hazards, such as localized disruptions, ie. the COVID-19 pandemic - have repercussions that affect manufacturers, suppliers, and buying habits in other geographical areas. This study offers a risk-resilient paradigm for supply chain optimization that combines cutting-edge risk mitigation and recovery techniques with conventional efficiency-driven tactics. The framework tackles both short-term operational issues and long-term sustainability objectives by fusing lean management, scenario-based stochastic demonstrating, Bayesian network analysis, and digital technologies like blockchain and artificial intelligence. In order to reduce the spread of disruptions and enhance decision-making in the face of uncertainty, this work highlights the significance of supplier collaboration, decentralized planning, and predictive analytics. This paper includes a thorough strategy for managing the ripple effect, improving supply chain adaptation, and guaranteeing continuous value delivery in unstable circumstances using comparative analysis and data from recent literature. 2025 IEEE.
Singh, Nidhi; Arora, Rashmi; Arya, Prabha; Dadwal, Sapna; Upreti, Kamal
https://doi.org/10.1109/WorldSUAS66815.2025.11199081 https://www.scopus.com/pages/publications/105022288310?origin=resultslist
Singh N., G D Goenka University, Department of Managment, Haryana, India; Arora R., G D Goenka University, Department of Managment, Haryana, India; Arya P., G D Goenka University, Department of Managment, Haryana, India; Dadwal S., DPG Institute of Technology and Management, Department of Managment, Gurugram, India; Upreti K., Christ University Delhi NCR, Department of Computer Science, Ghaziabad, India
IT Strategies for Effective Marketing in Globally Diverse Corporate Environments
Cross-Cultural Management; Cultural Adaptability; Diverse Work Environments; Global Business Strategy; Inclusive Marketing; Information Technology(IT); Market Segmentation; Strategic Marketing
Today, organizations discern multicultural teams, dynamic consumer tastes and shrinking landscapes of competition bordering on the internet-centric global economy. This paper investigates the role of IT strategies in improving the strategic marketing within different corporate environments. An examination of IT's role in addressing marketing and management complexity in different cultural context is made. Theoretical models are reviewed and responsible global marketing practices are promoted through digital transformation to reshapes the business operations. We also illustrate IT based solutions for dealing with cross cultural communication barriers, resistance to change as well as team dynamics. In this case study and trend analysis with trends, we show how own best practices of market segments, digitalization as well as cross cultural management work together with IT to encourage agility, customer focus, and continuous learning in the organization. 2025 IEEE.
Upreti, Kamal; Yadav, Amit; Rai, Ashish Kumar; Atasever, Mesut; Sharma, Suman; Radhakrishnan, G.V.
https://doi.org/10.1109/WorldSUAS66815.2025.11199065 https://www.scopus.com/pages/publications/105022270076?origin=resultslist
Upreti K., CHRIST University, Dept. of Computer Sciecne, India; Yadav A., Chandiwala Institute of Professional Studies, Dept. of Management Banarsidas, Delhi, India; Rai A.K., Ramchandra Chandravansi University, Faculty of Commerce & Management, Jharkhand, India; Atasever M., T.C. Usak University, Faculty of Applied Sciences, Department of Logistics Management, Turkey; Sharma S., Chandigarh Group of Colleges Jhanjeri, Department of Multimedia and Journalism, Punjab, Mohali, India; Radhakrishnan G.V., Kalinga Institute of Technology, Kalinga School of Management, Bhubaneswar, India
Deep Learning in Project Planning and Scheduling
AI; critical path method (CPM); Deep Learning; linear scheduling (LS); Machine learning (ML)
Controlling construction projects requires careful planning, and the most popular modelling techniques are the discrete-event simulator (DES), linear schedule (LS), and the critical path method (CPM). DES techniques, however, may become laborious and struggle to appropriately represent decision possibilities as complexity and restrictions increase. Through the reinforcement learning methods, deep learning-based artificial intelligence (AI) may be a viable substitute, enabling a quicker evaluation and suggestion of planning solutions for intricate building projects. This study investigates if artificial intelligence (AI) can replace DES in Insight, an illustrated constraint-based procedure planning tool for production and building. In the study, the difficulties of integrating AI into planning for building are discussed, along with the process modifications required to support deep learning techniques. Enhanced schedule, expenses, and efficiency in operation result from early planning of projects, which also balances conflicting project requirements. The planning of modern building projects is suggested to use a new conceptual methodology. 2025 IEEE.
Shetty, Keerthi; Gautam, Kamlesh Kumar; Rupani, Jigar; Khan, Shakir; Sreedevi, Vaitla; Dhawale, Sagar Digambar
https://doi.org/10.1109/WorldSUAS66815.2025.11198937 https://www.scopus.com/pages/publications/105022260409?origin=resultslist
Shetty K., Nitte (Deemed to Be University), NMAM Institute of Technology, Department of MCA, Udupi, India; Gautam K.K., Mewar Institute of Management, Vasundhara, Department of Computer Science, Uttar Pradesh, Ghaziabad, India; Rupani J., Christ University, School of Business and Management, India; Khan S., Imam Mohammad Ibn Saud Islamic University (IMSIU), College of Computer and Information Sciences, Riyadh, 11432, Saudi Arabia, Chandigarh University, University Centre for Research and Development, Department of Computer Science and Engineering, Mohali, 140413, India; Sreedevi V., Malla Reddy Engineering College (A), Maisammaguda, Kompally, Department of CSE, Telagana State, Secunderabad, 500100, India; Dhawale S.D., Ajeenkya D y Patil School of Engineering, Lohegaon, Department of Electronics & Telecommunication Engineering, Maharashtra, Pune, 412105, India
Emerging Cyber Threats in 5G and Beyond: A Wireless Communication Perspective
There are digital age threats to cybersecurity that cost organizations and businesses their infrastructure, operations, and even sensitive data. Cybersecurity risk management is important to ensure that organizational assets are not subjected to cyberattacks, data breaches, and any other vulnerability. This paper also looks at other significant risk-reduction strategies, including threat intelligence, models of risk assessment, encryption, access control systems. It also talks about how machine learning and artificial intelligence can help improve the way threats are detected and handled. Organizations should take a proactive and layered approach to security that integrates leading-edge security methods, with regulatory compliance processes and employee awareness programs. Also, regular security checking, incident response plans and consistent monitoring help in reducing risks and business continuity. Organisations must continue to adjust as cyber threats change, utilizing cutting-edge cybersecurity solutions to fortify their defenses. Organisations may create robust security structures in a continuously linked and threatprone digital environment by using the perspectives this research offers on efficient risk management techniques. 2025 IEEE.
Sudhakar, A.V.V.; Madanan, Mukesh; Kanwer, Budesh; Amutha, A.; Joseph, Christabell; Singh Bedi, Harpreet
2025 World Conference on Cutting-Edge Science and Technology, WCCEST 2025;
https://doi.org/10.1109/WCCEST66994.2025.11389957 https://www.scopus.com/pages/publications/105036504500?origin=resultslist
Sudhakar A.V.V., School of Engineering, SR University, Department of EEE, Telangana, Warangal, India; Madanan M., Dhofar University, Computer Science, Oman; Kanwer B., Poornima Institute of Engineering and Technology, India; Amutha A., Dhanalakshmi Srinivasan College of Engineering, Department of Electrical and Electronics Engineering, Tamil Nadu, Coimbatore, 641105, India; Joseph C., School of Law, CHRIST University, Bangalore, India; Singh Bedi H., School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, Phagwara, 144411, India
A Smart Academic Ecosystem Framework for Enhancing Digital Skills and Startup Success Among Potential Women Entrepreneurs
Adaptive Learning; AI-driven Mentorship; Computer Science Framework; Digital Skills; Entrepreneurship Education; Fairness-aware Algorithms; Knowledge Tracing; Smart Academic Ecosystem (SAE); Startup Incubation; Women Entrepreneurs
Lack of digital literacy is still a significant impediment for women entrepreneurs to participate and succeed in sectors that rely on technology infrastructure, as mentoring and incubation support is uneven, even for those who are skilled. Conventional academic programs tend to lack well-defined links between structured digital education and entrepreneurial careers, and, as a consequence, gaps between learning outcomes and startup success result. This paper introduces a Smart Academic Ecosystem (SAE) Framework, with tailored and interactive digital adaptation, AI-based mentorship, and dedicated startup incubation, pleading for a standard, one-size-fits-all system. The framework is implemented with a layered architecture comprising data ingestion (from learning management systems), feature stores (learner profiling), and algorithmic modules (knowledge tracing, contextual learning path recommendations, graph-based mentor matching, and venture readiness scoring). Fairness-enabling interventions and privacy-preserving analytics are built into the system to support fairness and trust. A pilot evaluation with early-stage women entrepreneurs identified substantial gains: digital skills mastery increased by more than 20%, startup initiation improved by 12 percentage points, and equity gaps in digital confidence and access were significantly narrowed. Findings emphasize that the SAE model leads not only to faster development of digital capability but also to higher chances of entrepreneurial success. This paper provides a replicable, standards-based, and computer-science-centred framework for academic institutions to encourage women entrepreneurs and innovation ecosystems. 2025 IEEE.
Shashi Rekha, B.V.; Kallarakal, Tomy K.; Thomas, Kennedy Andrew
https://doi.org/10.1109/WCCEST66994.2025.11389884 https://www.scopus.com/pages/publications/105036495792?origin=resultslist
Shashi Rekha B.V., Christ Deemed to be University, Department of Commerce, Bangalore, India; Kallarakal T.K., Christ Deemed to be University, Department of Commerce, Bangalore, India; Thomas K.A., Christ Deemed to be University, Centre for Education, Bangalore, India