Authors - Humma Ghaffar, Usman Ali, Muhammad Arfan, Sajid, Muhmmad Mujeeb Akbar Abstract - The growing mental health challenges around the globe need access to scalable, available, and safety conscious digital interventions. The paper describes a mental health support platform, based on AI, which combines conversational intelligence, multi-therapeutic persona modeling, structured mood analytics, proactive crisis identification, multi-lingual interaction, and voice-based access in a secure full stack design. The system, which runs on the Google Gemini AI, provides context-sensitive therapeutic dialogue and performs four-dimensional mood analysis of anxiety, stress, depression, and wellbeing, allowing longitudinal assessment by providing interactive dashboards and automated reporting. A safety-first crisis override system offers validated emergency capacity in the high-risk situations. The platform also includes multilingual voice feedback to facilitate inclusion of the visually impaired users and non-English speaking communities in providing inclusive digital mental health care. The proposed system is capable of changing the prevalent perception that AI and its applications may never be responsible and scalable because it integrates therapeutic diversity, structured analytics, accessibility features, and proactive safety controls into a single framework.
Authors - Pranay Kavthankar, Rutuj Koli, Ronit Ghadi, Yug Mora, Abhijit Joshi Abstract - Speech-to-Speech Translation (S2ST) has evolved from cas caded pipelines into end-to-end neural architectures. However, preserv ing emotion, prosody, and speaker identity across languages remains challenging. This survey examines state-of-the-art emotion and identity preserving S2ST and neural TTS systems, covering discrete-representation models, end-to-end systems, and cascaded pipelines. We analyze architec tures including Translatotron, VQ-Translatotron, SeamlessM4T, VALL E, VALL-E X, VITS, YourTTS, StyleTTS2, and XTTSv2. The survey discusses speaker identity preservation (x-vectors, d-vectors, codec repre sentations), prosody modeling (pitch, duration, energy), emotion reten tion (categorical, dimensional, embeddings), datasets, evaluation met rics, and challenges including data scarcity, cross-lingual emotion trans fer, and computational costs. We propose future directions toward large scale expressive datasets, improved cross-lingual modeling, and respon sible AI practices.
Authors - Maykin Warasart, Pallop Piriyasurawong, Panita Wannapiroon, Prachyanun Nilsook Abstract - This paper introduces an AI-based investment assistant that helps users to understand the fundamental principles of the financial markets. This work is mainly focused on stock market data to provide accurate insights and helps in various decision-making purposes. The rising volatility in the financial markets, massive data set, and the complexity of financial instruments, makes decision-making in financial sectors more difficult to individual investors.In order to cope with this problem, our model integrates time series forecasts, large language model intelligence with real-time financial information with interactive visualizations and personalized insights. The suggested system will interpret user queries in natural language with the help of a Large Language Model (Gemini 2.5 Flash) and extracts the corresponding stock tickers and financial objects and transforms them into structured inputs to be used in predictive analysis. Past and current stock market data are retrieved with the help of yfinance API and fed into an LSTM-based time-series predictive model that predicts future price fluctuations.The results predicted are presented in interactive charts created with Plotly, which users can analyze trends easily and compare several stocks. The system can also give personalized recommendations, textual summaries of stock movements (moving up or down), multi-turn chatbot conversations, portfolio, wishlist and real time price moves besides forecasting. The proposed investment assistant improves the gap between complicated financial information and practical results by incorporating natural language comprehension, deep learningbased prediction, and intuitive visualization etc. The system promotes user knowledge and helps them in effective decision making .
Authors - Gabriel M. da Silva, Nicolas O. da Rocha, Heloise V. C. Brito, Joao V. N. M. da Silva, Sergio A. S. da Silva, Anderson R. de Souza, Carlos A. O. de Freitas, Vandermi Joao da Silva Abstract - Spiking Neural Networks (SNNs) have been investigated as a biologically inspired alternative for efficient information processing, particularly in energy-sensitive applications. This work presents a comparative evaluation of the energy efficiency of different SNN techniques, including Liquid State Machines (LSM), Recurrent Spiking Neural Networks (RSNN), Spiking Convolutional Neural Networks (SCNN), and learning based on Spike-Timing Dependent Plasticity (STDP). The experiments were conducted on conventional hardware plat-forms, namely an Android smartphone and a notebook, using simulated implementations of SNNs without dedicated neuromorphic acceleration. The analysis considered different network scales by varying the number of neurons and was based on neural activity metrics, particularly the total number of generated spikes, employed as a proxy for the indirect estimation of energy consumption during audio signal processing. The results demonstrate a consistent relationship between neural activity and estimated energy consumption, as well as an energy saturation behavior as network complexity increases. Differences among the an-alyzed techniques are more pronounced in small-scale configurations, whereas larger networks exhibit convergent patterns of neural activity and energy consumption. Although conducted in a digital simulation environment, this study highlights the limitations of conventional platforms for the efficient execution of SNNs and reinforces the potential of dedicated neuromorphic hardware for embedded and low-power applications.
Authors - Maykin Warasart, Veerasith Wongkarn, Phonesavanh Nammakone, Duangtavanh Thatsaphone Abstract - Manual correction of written examination scripts is still the default practice in many institutions, but it is slow, tiring for evaluators, and not always consistent, especially when large numbers of papers must be graded in a short time. In this work we look at how recent advances in optical character recognition (OCR), machine learning (ML), and natural language processing (NLP) can be used together to support automatic evaluation of both objective and descriptive answers. In this paper We study a two–stage system: first, a handwriting recognizer based on convolutional and recurrent neural networks (CRNN) is used to read handwritten responses from scanned answer sheets; next, the recognized text is scored using semantic and syntactic similarity measures driven by transformer-based language models. By training the recognizer on a mixture of public handwriting corpora and locally collected scripts, and by combining keyword features with sentence-level embeddings, the system is able to approximate faculty grading patterns with good accuracy. This study examines the way that real tests are administered, including variations in writing styles, background noise in scans, the arrangement of answers on paper, and terms related to specific subjects. We clearly address each of those factors in our approach. Teachers won’t vanish because of this setup; instead, it aims to ease their ongoing tasks while offering fairness and consistency across student results.
Authors - Hai D. Nguyen, Nguyen Ngoc Quan, Viet H. Le, Mai T. Nguyen, Nguyen Huy Trung, Le Duc Huy, Nhu Son Nguyen Abstract - Military forces launch offensive operations to defeat and destroy enemy. Battlefield surveillance enables provisioning of timely and correct battle space information to commanders, both prior and during the launch of offensive operations. Static battlefield surveillance devices have certain limitations which restrict their usage during offensive operations. In the current paper, we review the requirement of surveillance devices during various periods of offensive operations, the limitations of static surveillance devices and efficacy of Unmanned Aerial Vehicles (UAVs) as prime battlefield surveillance device for offensive operations. We then explore the possibility of connecting UAVs with existing cellular base stations and with vehicle mounted cellular base stations which can be moved into enemy territory with the progress of offensive operations. Furthermore, a UAV communication model for enhanced battlefield surveillance during offensive operations is presented after analyzing various antenna techniques utilized to achieve desired data rates for UAV operations.
Authors - Quan Nguyen, Chau Vo, Phung Nguyen Abstract - In order to create reliable connectivity where there is no direct line-of-sight (LOS) path between ground terminals, this study provides the design and performance evaluation of a dual-hop Unmanned Aerial Vehicle (UAV) assisted free space optical communication system. The proposed ground–UAV–UAV–ground architecture enables non-LOS communication by employing aerial relays to bypass physical obstructions and extend transmission coverage. Three modulation formats—Non-Return to Zero (NRZ), Return to Zero (RZ), and Carrier-Suppressed Return to Zero (CSRZ)—under various weather conditions and turbulence regimes are used to assess the system performance. While all modulation schemes perform closely for different attenuation level, differences in performance is prominent under turbulence, CSRZ demonstrates superior robustness, followed by NRZ and RZ.
Authors - Rajesh Kapoor, Vishal Goyal, Aasheesh Shukla Abstract - This paper presents a systematic review of visual sarcasm detection research with a focus on learning-based approaches. The review examines input representations, feature extraction methods, model architectures, datasets, and evaluation practices reported in the literature. Studies are analyzed with respect to the use of visual information, including images and image–text pairs, along with associated deep learning frameworks such as convolutional, transformer-based, and hybrid models. A structured search strategy, defined inclusion criteria, and an analytical framework are employed to ensure consistency and reproducibility of the review process. The findings are synthesized to identify prevailing research patterns, methodological limitations, and gaps related to visual feature representation, model design, and experimental consistency. By organizing and comparing existing approaches, this systematic review provides a consolidated reference and supports future research in visual sarcasm detection.
Authors - G. Sabera, Kanajam Murali Krishna, N. Sabitha, Tummala Purnima, A. Naresh, Shaik Janbhasha Abstract - Complementing the continuous deep integration of culture and tour-ism, the tourism market environment and visitor consumption demand are constantly evolving, with cultural theme attractions playing an increasingly prominent role in tourism industry development. Tourism resources constitute the basic foundation of scenic destination development, while scientific and effective tour-ism marketing provide a key factor in enhancing market competitiveness and achieving sustainable development. Relying on the cultural resources of the Song Dynasty and martial arts culture, The Song Dynasty of Kungfu City has formed a distinctive thematic identity against the background of cultural–tourism integration and has gained a particular level of market attention. However, its tourism marketing practices still face practical challenges such as brand strengthening, intensified market competition, and changing visitor expectations. This study takes The Song Dynasty of Kungfu City as the research object and analyzes the current status of its tourism marketing, exploring the developmental foundation and practical challenges faced by the scenic area under the contemporary tourism market environment. A qualitative research approach is adopted. Relevant data were collected through field observation and in-depth interviews to review the scenic area’s tourism marketing activities. Based on this, the SWOT analytical framework was applied to systematically examine the strengths, weaknesses, opportunities, and threats associated with the tourism marketing status of The Song Dynasty of Kungfu City.
Authors - Sambhram Pattanayak, Akankasha Kathuria, Shreesha Mairaru Abstract - Reliable prediction of rare critical events is a key enabler for modern risk management, civil protection, and decision support sys tems, yet it remains challenging due to extreme class imbalance and strict requirements on false alarm rates. We present an ensemble learn ing framework that combines a deep feed-forward neural network with a Random Forest classifier, complemented by temporal feature engineering and precision-oriented optimization. The approach addresses three ob jectives: extracting informative temporal and regional patterns from raw event logs, learning calibrated probabilistic scores under severe imbalance using focal loss, and tuning per-region decision thresholds to achieve high precision while preserving acceptable recall. As a case study we apply the framework to air alert prediction over 25 administrative regions across 38 months, totalling 774,125 hourly observations. The system attains 96.13% accuracy, 75.1% precision, and 77.9% recall, demonstrating that high-precision early warning is feasible in strongly imbalanced settings. The framework is applicable to a wide range of safety-critical rare event prediction tasks.
