Authors - Hemamalini Siranjeevi, Swaminathan Venkatraman, Dharshini V, Gayathri A, Sushma Sri R Abstract - Urban environments generate massive video data from surveillance and mobile sensors, necessitating efficient and intelligent summarization for smart city and transportation systems. This paper proposes a multimodal video summarization framework that moves beyond object-centric analysis toward high-level urban scene understanding. Unlike traditional methods that rely on low-level visual features or isolated object detection, the proposed approach captures contextual relationships and temporal continuity through a multi-stage pipeline. The system integrates multimodal perception, combining deep learning-based object detection, multi-object tracking, and acoustic analysis to preserve entity identities and environmental context. We employ relational inference and motion heuristics to model spatial and semantic interactions, which are then structured into a Dynamic Knowledge Graph (DKG) representing entities, interactions, and temporal events. A semantic synthesis module, powered by a transformer-based language model, generates concise, coherent, and semantically meaningful summaries. This architecture enables scalable, context-aware video summarization adaptable to real-world urban applications.
Authors - Nithin Gattappagari, Lakshmi Sagar S, Reddy Lokesh K, Banu Prakash N, Asritha A, Varalakshmi U, Karthik P, Praveen Kumar Rayani Abstract - Conventional one-time authentication cannot prevent session hijacking after login. This paper proposes a session-level impostor de tection framework based on Siamese learning over mouse dynamics for continuous authentication. The model combines statistical behavioral de scriptors with lightweight temporal modeling (Conv1D+GRU) to learn compact embeddings for open-set verification. It supports one-shot en rollment by comparing a query session against a single verified reference session and stores non-reversible embeddings instead of raw trajectories to improve privacy. We evaluate on Balabit and SAPiMouse under se vere class imbalance using balanced batching, semi-hard negative mining, and focal contrastive loss. The framework achieves AUROC 0.95/0.96, F1 0.80/0.85, and accuracy 0.92/0.93, with 46K trainable parameters and approximately 15ms inference time, indicating practical deployment potential.
Authors - Rishav Kumar Agrawal, Maharshi Bhowmick, Mir Abbas Hussain, Sachin, Vaishali Shinde Abstract - This paper presents a platform for scalable validation, visu alization, and explanation of synthetic tabular data in a rigorous and operationally practical workflow. The system integrates statistical test ing, dimensionality reduction, anomaly detection, and AI-assisted in terpretation into a single analysis pipeline. Through an insurance-data case study, we show that the platform can detect subtle distributional artifacts, support utility–privacy trade-off assessment, and provide in terpretable evidence that is difficult to obtain from isolated univariate checks. We conclude by discussing practical value, current limitations, and directions for future development.
Authors - Rowena Ocier Sibayan, Hazel C. Tagalog, Ronald S. Cordova Abstract - As digital marketing expands in Oman, many organizations struggle to transform large volumes of customer data into actionable insights. This study presents an AI-driven marketing intelligence framework designed for non-technical users, combining automated customer segmentation, sentiment analysis, and personalized recommendations. The framework employs an autoencoder-based feature extraction approach to capture key behavioral patterns, followed by K-Means clustering to define meaningful customer segments (Berahmand et al., 2024). A fine-tuned BERT model analyzes multilingual feedback in Arabic and English to assess customer sentiment (Manias et al., 2023). The framework was evaluated using 12 months of campaign data from 450 customers across multiple Omani businesses. Analysis revealed four distinct customer groups and an overall positive sentiment of +0.55. Controlled A/B experiments demonstrated that AI-guided campaigns outperformed traditional methods, increasing conversion rates by 27%, improving retention by 15%, and generating a threefold return on marketing spend. These results indicate that accessible AI tools can deliver measurable marketing benefits in emerging markets and provide a scalable solution for Gulf-region businesses.
Authors - Maria George Anthraper, Kusuma Sanjaykumar, Sinchana K C, V R, Badri Prasad Abstract - Post-quantum migration is increasingly constrained by time: deployed cryptographic mechanisms may need to be retired, hybridized, or re-keyed before effective security margins fall below asset-specific pol icy thresholds. This timing problem is complicated by uncertainty in clas sical hardware acceleration, algorithmic progress, implementation ero sion, and the arrival of cryptographically relevant quantum comput ers. This paper presents a compact probabilistic pipeline that translates evolving assumptions and evidence into decision-facing migration guid ance. The approach couples three layers: (i) a security-trajectory model that encodes expected margin erosion under scenario parameters, (ii) a latent-regime model that represents partially observed risk states and updates them as evidence changes, and (iii) an option-style timing layer that quantifies the diminishing value of delaying migration as thresholds approach. Outputs are conditional on stated assumptions and are in tended to be reported with sensitivity bands and lead-time constraints. In practice, the pipeline is intended to be re-run as assumptions and evidence evolve, preserving an auditable trail from scenario inputs to in termediate states and final decision artifacts. The primary deliverables are comparative rankings and conservative “start-by” windows under stated assumptions, rather than single predicted break dates.
Authors - Jayalakshmi D, N. Priya Abstract - Online product reviews play a key role in the success or failure of an e-commerce business. Often, online reviews from previous customers provide buyers with detailed advice about the product and help them decide before purchasing a product or service. However, some e-commerce products can be promoted or damaged by fraudsters who post fake reviews. Synthetic Reviews (SRs) have the capacity to deceive consumers, influence purchasing decisions, and lead to losses. Thus, SRs pose a significant risk to e-commerce companies and content creators, undermining consumer loyalty and brand reputation. Specifically, the development of AI-generated fake reviews has made them harder to detect, as they are very similar to human-written texts. This review paper presents a Deep Learning (DL)-based framework that offers comprehensive insight into fraud and synthetic review detection in an evolving e-commerce environment. This review paper discusses the importance of DL for detecting online product fake reviews in sentiment analysis using various approaches based on Graph Convolutional Network (GCN), Hierarchical Graph Attention Network (HGAN) Sentiment Majority Voting Classifiers (SMVC), Convolutional Neural Networks with Bidirectional Long Short-Term Memory Networks (CNN-Bi-LSTMs), and a proposed Optimized Bidirectional Encoder Representation Transformers (OBERT) model. This review paper focused on the importance of DL models, particularly the GCN, for effective identification of fake online reviews. This review paper proposed a DL algorithm for fake review detection in online products and demonstrated its practical application in a real-world scenario.
Authors - Miroslav Cech, Rastislav Roka Abstract - Private 5G networks require a reliable, high-capacity, and secure transport infrastructure, especially in industrial and critical applications. Free Space Optics is a promising solution enabling multi-gigabit transmissions with low latency and increased physical security. The article analyses the possibili ties of integrating FSO technology into Standalone Non-Public Network and Public Network Integrated Non-Public Network architectures and evaluates the role of FSO links as a transport or interconnection layer and their impact on la tency, reliability, and security for 5G services such as eMBB, URLLC, and mMTC. The article then summarizes current research trends, including the use of artificial intelligence and machine learning to optimize FSO-based transmission.
Authors - Tanmoy De, Vimal Kumar, Pratima Verma Abstract - The process of operating modern engineering companies is often compartmentalized due to the straightforward nature of the operations requirements that mani-fest themselves within the realm of the software creation and hardware manufacturing. The absence of integration between Agile practices and Waterfall lifecycles is a waste of administrative resources and delays time-to-market. A hybrid project management SaaS is offered in this project called Converge, which will target the integration of these areas without sacrificing the integrity of the data stored in digital code repositories and physical Bill of Materials (BoM). The adoption of Multi-Modal Documentation, Real-time State Synchronization and IoT-oriented Task Automation have their measures of efficiency of workflow, responsiveness of interface, and cross-domain data consistency. The most recent breakthroughs in Natural Language Processing (NLP) and Computer Vision are used to make the experience more practical; a custom AI pipeline based on the ResNet50 and LSTM networks are able to extract visual storyboards of technical video reports with an impressive F Score of 83.00% (with 79.20% Precision and 86.50% Recall), and Transformer based models (including BART) are able to generate structured textual summaries with the leading ROUGE-L score of 0.42. The system is anchored on a dynamic split-brain architecture to display coherent information in either Kanban boards or Gantt charts as the case arises. Status updates increase exponentially with integrated IoT triggers to computerize the execution of tasks via a direct hardware to software communication. The survey is based on the trade offs between the flexibility of UI, the complexity of the database schema, and the latency of the API to compare the old siloed tools to this new hybrid framework. The future of engineering management relies on new tendencies, such as Hybrid Machine Learning, to predictively allocate resources, cutting the error rates in estimating the effort by three times (MMRE to 0.32) with the help of such dominant historical measures of resources as Lines of Code (feature importance score of 0.73) and automated reporting of resource depend-ency. Finally, it is demonstrated that the suggested architecture with the support of a CNN optimized backend video storage, which will save 61.80% of the time at a small cost of 2.30% BDBR, will save about 60% of time on manual docu-mentation and synchronize assets in real-time with a latency less than 200ms (2 seconds).
Authors - Dennis A. Dizon, Gleen A. Dalaorao Abstract - Access to formal financial services remains limited in many develop ing regions, largely due to economic and infrastructural constraints. This study uses the ISO/IEC 25010 as the evaluation framework to present a software quality assessment of a lending automation system installed in a financial insti tution in Butuan City, Philippines. The evaluation focuses on five essential as pects of software quality: usability, reliability, functional suitability, perfor mance efficiency, and security. Usability surveys using SUS and UMUX-Lite, operational and performance testing, and an evaluation of security and data privacy compliance were used to gather empirical data. According to the results, the system achieved high performance with an average inference latency of 0.208 ms per record, uptime reliability of ≥99.5%, excellent usability with a mean SUS score of 82.5, and full compliance with data privacy regulations. Predictive analytics, specifically the Random Forest model with isotonic calibration, further enhanced the automated loan assessment’s interpretability and reliability. The system proved that it is appropriate for real-world applications and can encourage financial inclusion in resource-constrained environments, as it exceeded the intended benchmarks for each quality model. To guarantee the long-term adoption of lending automation technologies, the study emphasizes the significance of thorough software quality evaluation in addition to predictive accuracy.
Authors - Nita Dimble, Satish Narayanrav Gujar Abstract - The fabrication of components across various industries is accom plished through welding. Although welding has been practiced for more than a hundred years, defects may still occur during the welding process. Thus, indus trial standards require welded joints to be inspected and evaluated to ensure their quality and reliability. Conventional ultrasonic testing (UT) has long been widely used in industry for detecting and evaluating defects in weld specimens. Over the last few decades, advances in sensor technology and signal analysis techniques have significantly advanced ultrasonic testing methods. Advanced methods, such as Time Of Flight Diffraction (TOFD), are more likely to detect linear defects. However, one of the major challenges in applying TOFD to the inspection of austenitic stainless steel (ASS) weldments is noise in the signals. Various signal processing approaches have been developed to suppress such noise, each with its own advantages and limitations. In this work, the focus is placed on the applica tion of multi-level discrete wavelet transform (DWT) decompositions with ‘n’- order wavelet filters for de-noising ultrasonic TOFD A-scan signals. The results show that this approach achieves greater improvement in signal-to-noise ratio (SNR) while requiring less computational time.
