Authors - Ankush Soni, Sanjay K. Sahay Abstract - The integration of unmanned aerial vehicles (UAVs) into remote and beyond-visual-line-of-sight missions has increased the demand for secure, efficient, and low-latency communication systems, particularly in scenarios where satellite connectivity is the only viable option. However, high-latency satellite links and constrained onboard resources pose significant challenges to traditional cryptographic protocols. In this paper, we present a lightweight, symmetric key-based authentication and secure communication protocol designed specifically for satellite-driven UAV applications. Our protocol replaces resource-intensive public key operations with a pre-shared key approach and incorporates AESHA3, a variant of AES that leverages SHA-3 for key scheduling, along with SHA2-512 for hashing. We implement this design within the TLS and QUIC frameworks, adapting their internal packet structures to accommodate constrained environments. Experimental evaluations on Raspberry Pi testbeds reveal that our proposed QUIC implementation significantly outperforms TLS in encryption speed, handshake latency, and overall protocol execution time. The results demonstrate the suitability of the proposed system for real-time mission-critical UAV operations over satellite links, highlighting the benefits of integrating optimized symmetric cryptography into modern transport protocols.
Authors - Reena (Mahapatra) Lenka, Jaya Chitranshi, Vanishree Pabalkar Abstract - Marketing automation with the use of data-driven insights will help in anticipating customer-behavior. It will enable personalized campaigns, improve the process of decision-making, and ensure market-accountability by measuring effectiveness of the system and help in optimizing strategies This will ultimately drive customer engagement, loyalty, and higher-returns on investment in the competitive business environments. The present invention relates to a method and a system for customer predictive analysis for marketing automation and market accountability. The present invention attempts to develop a layout for the job of marketing-computerization (MA) in estimating the return on displaying exercises and the difficulties related with arriving at responsibility in marketing. To explore the goal of the assessment, the creators took on a subjective methodology, leading an exploratory review among ten key witnesses. Based on the aftereffects of the subjective investigation, an applied system was proposed, which incorporates both key and functional level elements fully intent on making a worth based plan. In this plan, leaders, for example the Chief Marketing Officer. arise as worth makers, encouraging business versatility, what further contentions can be given to legitimize spending plan portion to MA exercises. Through cautious examination of the components that describe the peculiarity under study, the present invention eventually adds to a superior comprehension of MA and responsibility inside the current business worldview. Focusing on the marketing setting, an organized conversation of how AI can recognize the objective clients exactly in spite of their various practices was introduced in this contemporary invention. The uses of AI in client focusing and the extended viability all through the unique periods of client lifecycle were similarly inspected.
Authors - Reena (Mahapatra) Lenka, Jaya Chitranshi, Vanishree Pabalkar Abstract - The systems and regulations that control human behaviour are the main emphasis of HRM. Finding the greatest talent from around the world, training them, evaluating their performance, rewarding them, and creating a positive work atmosphere are all part of the HRM field's magnificent duty. Developing a framework that accurately forecasts the need for talent and workforce skills becomes crucial because every organization's strategy depends, in part or in full, on its skill sets. When the human resource data is accessed for study of the higher cognitive processes involved, a variety of techniques may be used to extract the most useful information from the dataset. A common strategy for using data to inform decisions is data processing. On the other side, "state-of-the-art accuracy" in decisions is what predictive analytics is known for. The goal of this study is to provide a strategic decision-making model for "human resource management (HRM)." The study develops a decision-making framework based on data processing and predictive analytics for decisions pertaining to human resources. Given that HRM has a significant impact on an organization's longevity and efficacy, the model was created for global corporations. The suggested approach will be useful in enhancing HR systems' effectiveness, which could have a favourable impact on business results.
Authors - Bryna Meivitawanli, Liu Fen Phaw, Daniella Maria Natalia Abstract - The increasing influence of social media and user generated con-tent has significantly transformed consumer behavior, with negative publicity emerging as a critical factor affecting brand perception and purchase intention. This study aims to investigate the impact of negative publicity on the purchase intentions of Generation Z consumers in Indonesia, focusing on the mediating roles of brand advocacy and brand betrayal. A quantitative research approach was employed, utilizing a structured questionnaire distributed to 158 respond-ents. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test four hypotheses regarding the relationships be-tween negative publicity, brand advocacy, brand betrayal, and purchase intention. The findings indicate that negative publicity significantly reduces purchase intention, with brand advocacy mitigating its adverse effects, while brand betrayal amplifies the negative impact. These results underscore the importance of managing brand reputation in the digital age, highlighting the need for brands to adopt ethical marketing strategies and proactive reputation management to counter the detrimental effects of negative publicity. This research contributes to the understanding of how digital media influences consumer behavior, offering valuable insights for marketers seeking to enhance brand loyalty and mitigate the risks associated with negative publicity. The findings are particularly relevant for businesses targeting Generation Z in emerging markets like Indonesia, where social media plays a pivotal role in shaping consumer decisions.
Authors - Sateesh Kumar TK, Vishnu Achutha Menon, Juby Thomas, Lijo P Thomas Abstract - This study examines emotion classification in news headlines related to Operation Sindoor, a precision military response initiated by India in May 2025 following the Pahalgam terrorist attack. The operation’s symbolic naming, reflecting cultural mourning and national resolve, resulted in emotionally charged media coverage. To analyze the emotional framing within these head-lines, a dataset from multiple news outlets was compiled and annotated with six emotion categories: anger, fear, joy, sadness, surprise, and neutral. Preprocessing involved TF‑IDF vectorization with unigram and bigram features, followed by classification using Logistic Regression and Support Vector Machines (SVM). Model performance was evaluated using accuracy, precision, recall, and F1-score, with macro-averaging to address class imbalance. Both models achieved an overall accuracy of 54%, with SVM yielding a higher macro‑F1 score (0.40) compared to Logistic Regression (0.22). The results indicate that SVM performed better in identifying dominant emotions such as fear and sur-prise, while both models struggled with underrepresented categories like joy and disgust. The findings suggest the need for advanced approaches such as deep learning architectures or data augmentation methods to improve classification of minority emotions in crisis reporting contexts.
Authors - Ananth Chiravuri Abstract - Prior studies have indicated a positive effect of a student’s digital competence on their academic performance in higher education institutes. However, the relationship between variables such as digital competence and academic performance may not be direct. There are other variables such as student engagement and self-efficacy that could mediate the role between digital competence and academic performance, warranting a further investigation Specifically, this research investigates whether students' digital skills/literacy influence their academic success through the mediating factors of self-efficacy and engagement. Findings from the study aim to emphasize the importance of empowering students with digital literacy skills to enhance their learning experiences and achievements.
Authors - Cu Quoc Le, Huy Khanh Hua, Nguyen Viet Ngo, Tien Trung Bach, Hoang Ngoc Tran Abstract - This paper presents an improved multi-camera people tracking pipeline with a focus on enhancing object detection to improve overall tracking performance. The system integrates object detection, singlecamera tracking, re-identification, and multi-camera matching. We evaluate five versions of the YOLO model, from YOLOv8 to YOLOv12 in the same tracking pipeline to ensure a fair comparison. Part of Track 1 data from AIC2024 dataset is used for both training and inference purposes. For each frame, detected objects are tracked using a Kalman filter and matched via the Hungarian algorithm based on both spatial and appearance features. Inter-camera identity association is achieved through cluster-based matching, followed by refinement steps to improve consistency. Tracking performance is assessed using the MOTA metric. Experimental results demonstrate that stronger detection models significantly improve tracking accuracy, with YOLOv12 achieving the highest MOTA score.
Authors - T. A. Akshaya, M. Suresh Abstract - This study is a journey to identify the opportunities, challenges, and future research scope in the metaverse entrepreneurship based on a systematic literature review. The Scopus database is employed for the selection of documents. For review, 25 documents are selected through the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. The study identified the opportunities in metaverse entrepreneurship connected with the technological enablers of entrepreneurship, innovative business and market opportunities, transformation of education and entrepreneurial learning, economic growth and social inclusion, and further development in metaverse entrepreneurship. The major challenges of the metaverse in entrepreneurship encompass economic, technological, regulatory, security, and skill-based aspects. Metaverse entrepreneurship is a galaxy waiting to be explored. This study represents the first attempt to synthesize the existing research through a systematic literature review methodology. To policymakers, this study guides to creation regulatory framework in social, economic, and legal dimensions. To entrepreneurs, this study helps to identify opportunities in metaverse entrepreneurship not just as a marketplace but as an ocean for innovation and ideation. To educators, this study is a light into the rethinking in redesigning of the curriculum and the pedagogical approach to integrating metaverse tools and entrepreneurship education to create a generation with immense knowledge in digital entrepreneurship. The limitation of this in-depth study is, it only included the documents from the Scopus database till August 2025. The novelty of the study is to the theory by informing future research possibilities in this evolving domain by highlighting current research trends.
Authors - Chirani Perera, Uvini Ranaweera, Indra Mahakalanda Abstract - This study investigates tourist perceptions of seven southern beaches in Sri Lanka using Google Reviews. With the increasing influence of online plat-forms in travel decision-making, analyzing review content provides valuable in-sights into tourist experiences and preferences. The research employs trans-former-based models from Hugging Face for sentiment analysis and topic modeling, offering a modern, data-driven approach to textual review interpretation. Word clouds and bigram visualizations are used to highlight common positive and negative expressions associated with each beach. The findings reveal themes such as cleanliness, natural beauty, surfing opportunities, crowd, and local ser-vice quality as key themes associated with the southern coastline of Sri Lanka. Sentiment patterns vary across beaches, with some consistently rated positively while others receive mixed feedback. This analysis offers practical insights for stakeholders in the field of tourism to improve destination management and marketing strategies. The study demonstrates the effectiveness of modern-day NLP techniques in understanding tourist experiences and provides a scalable frame-work for future such analysis that centres around the user responses.
Authors - Mayanka Gupta, Ayman Amjad, Arjun Prabhakaran, Bhanoday Kurma, Bhanu Prakash M, Kiran Agarwal Gupta, Chaitra Ravi, Sindhoor N Abstract - Despite significant progress in autonomous driving, detecting and segmenting obstacles under poor conditions remains a major challenge. This paper reviews deep learning models that tackle real-world difficulties like occlusion, fog, motion blur, and uneven road surfaces such as potholes and broken speed bumps—factors that heavily impact safety and detection accuracy. Over ten recent models are analyzed, including prompt-based approaches like Semantic-SAM and EPCFormer, memory-augmented ones like OOSIS and XMem, and task-specific detectors such as DR-YOLO, D-YOLO, and motion-aware YOLO variants. Each model type comes with trade-offs: prompt-based systems are flexible but depend on large vision-language datasets, while memory-based methods offer temporal consistency at the cost of increased computation. A key focus is on handling unstructured and uncertain road conditions, especially common in countries like India, where irregular infrastructure and unpredictable traffic are everyday challenges. Models trained solely on structured data often fail in these environments. To address this, the survey includes detailed comparisons and benchmark tests under difficult traffic and weather conditions. These insights inform the design of XenSense-V.1, a real-time deep learning framework using optical f low, temporal reasoning, and efficient segmentation to handle occlusion, weather issues, and complex road scenarios—particularly suited to Indian driving conditions.
