Authors - Stuti Kumari, Kunal Dey Abstract - Teen suicide remains a significant public health concern in the Unit ed States, with substantial geographic variation across counties. Understanding how socio-environmental and healthcare access factors relate to suicide risk can help identify communities that may benefit from targeted interventions. This study aims to support this effort by analyzing county-level teen suicide patterns using K-means clustering, an unsupervised machine learning technique. A da taset of 248 U.S. counties with reported teen suicide data was constructed using five-year aggregated suicide crude rates (2019-2023) alongside multiple socio environmental and healthcare indicators, including hospitalization rates, mental health provider availability, primary care provider rates, social association rates, uninsured population percentages, poverty levels, food insecurity, and rural population share. K-means clustering was then applied to identify county-level risk profiles. The results reveal two distinct county groups: one characterized by lower suicide rates, greater healthcare provider availability, stronger social as sociations, and lower socioeconomic disadvantage; and another characterized by higher suicide rates, reduced healthcare access, higher poverty and food in security, and greater rural residency. These findings highlight meaningful coun ty-level disparities and demonstrate the utility of machine learning approaches to identify regional risk profiles associated with teen suicide. The results may help inform public health strategies and policy efforts aimed at prioritizing re sources and expanding mental health services in high-risk communities.
Authors - Sanjida Karim Peuly, Sharmin Alam Mou, Tamanna Hossain Badhon Abstract - Diabetes diagnosis at the early stages is an important factor in avoiding long-term complications. The existing body of literature tends to be based on small, saturated datasets that are not very interpretable and externalized. This pa-per will suggest a powerful machine learning model to predict diseases at the first stage of diabetes on the basis of a symptom-based dataset of One thousand five hundred and sixty cases. Six classifiers, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, and XGBoost, were considered on the stratified cross-validation and independent test sets. Systematic hyperparameter optimization using GridSearchCV was used to prevent overfit-ting and improve the generalization. Additionally, a Stacking Ensemble model was provided; the Logistic Regression, Random Forest, and XGBoost were com-bined to obtain a high level of predictive stability. Experimental evidence has shown that ensemble-based methods are more effective than single classifiers, as XGBoost and Stacking Ensemble have the highest accuracy and ROC-AUC val-ues. The analysis of feature importance suggested polyuria and polydipsia as the most important clinical signs, which is consistent with medical knowledge. This study offers a practical and interpretable decision support model in screening early diabetes, which bridges the predictive performance and clinical utility gap.
Authors - Jose R. Rosas-Bustos, Mark Pecen, Jesse Van Griensven The, Roydon Andrew Fraser, Nadeem Said, Sebastian Ratto Valderrama, Andy Thanos 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.
Nadeem Said is a computer engineer with research and professional interests in artificial intelligence, machine learning, cryptography, and secure computational systems. Currently pursuing his Master’s, his academic work includes peer-reviewed contributions to quantum security... Read More →
Authors - Ronald S. Cordova, Rowena O. Sibayan, Hazel C. Tagalog, Rolou Lyn R. Maata Abstract - Awareness regarding consumer sentiments will benefit a business entity and/or a company in making their marketing strategies more effective and engaging in the current digital marketing context. In traditional marketing scenarios, since there is a lack of actual emotional aspect in expressing views in real-time contexts, it has always been challenging for a business to perform a significant adjustment in their marketing campaigns and achieve a greater success rate. The proposed idea focuses on AI and ML-based approaches for sentiment analysis in digital marketing. The framework is made up of seven core steps: data collection, preprocessing and data cleaning, sentiment analysis models, feature extraction and model training, sentiment classification and analysis, insights and decision-making, and application in digital marketing. From social media to e-commerce reviews to online discussions, consumer sentiment data comes from many digital sources. The text for analysis is standardized, and noise is cleaned in data preparation. Then, apart from other artificial intelligence-based sentiment classification models, sentiments are classified as positive, negative, or neutral using lexicon-based, machine learning, and deep learning approaches. The learned knowledge enables businesses to react dynamically to consumer sentiment, target advertisements, and adjust marketing strategies. Businesses will be able to conduct more profitable promotions, communicate with customers better, and monitor real-time sentiment through this AI-driven sentiment analysis platform. The paper emphasizes the benefit of incorporating artificial intelligence in decision-making within digital marketing, even in addressing issues like ambiguous sentiment expression management and multi-language data. This paper provides a strategic way towards maximum customer interaction and brand loyalty and also emphasizes the need for sentiment analysis that is sustained by available data in modern digital marketing.
Authors - Mandala Nagarjuna Naidu, Bandi Hemalatha, Kadavakallu Viswanath, Kotapati Venkata Pavan, Ms.Ragavarthini Abstract - Autonomous vehicles rely on powerful perception systems with real-time object detection and tracking capabilities. Our paper presents a unified deep learning framework based on YOLOv8n and ByteTrack for multi-class detection of vehicles, pedestrians, traffic signs and lights on roads. Our work maintains consistent tracking between frames without the limitations of previous works that rely on static images or single-object-type detection. The lightweight model, with only 3.2 million parameters in YOLOv8n, provides a good trade-off between accuracy and efficiency for embedded automotive hardware. Experiments conducted on the COCO validation dataset, achieving 52.11% mAP @ 0.5,with precision and recall values of 63.42% and 47.44% respectively.It runs real-time on traffic videos with an average frame rate of 62 FPS and a mean inference time of 10.10 ms.Results for tests on traffic videos show, on average 10.15 objects detected with 68.29% confidence.These findings make this approach apt for both autonomous navigation and intelligent traffic monitoring.
Authors - Mazdak Zamani, Mohammad Naderi Dehkordi, Riham Hilal, Azizah Abdul Manaf, Achyut Shankar, Touraj Khodadadi 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 pri vacy 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 cali bration, 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 predic tive accuracy.
Authors - Sai Sundarakrishna, Vedant Maheshwari Abstract - Recent literature has posed LLMs as nonlinear dynamical systems. LLM safety, in these modern LLMs is about the systematic and critical monitoring of logit based oscillations, hidden state rotations and entropy fluctuations. Many of these important factors are spectral proxies for the generation of imaginary eigenvalues. These imaginary eigenvalues are, in a way, determinants of the latent oscillation energy. Though the system in its original state space is inherently nonlinear, through the Koopman operator, we can linearize the evolution in the lifted space of observables. We design a spectral jailbreak detector that has a Sparsely regularized koopman autoencoder as its backbone. We obtain the koopman operator through this SR-KAE, and also obtain the imaginary component of the eigenvalues of that spectral operator, A new risk score metric is proposed that is used to classify prompts as either jailbreak or safe. This becomes a physics-style stability classifier on prompts. We present several test cases, while we discuss the strengths and limitations of this new system.
Authors - Mazdak Zamani, Mohammad Naderi Dehkordi, Riham Hilal, Azizah Abdul Manaf, Achyut Shankar, Touraj Khodadadi Abstract - The rushed development of edge computers, including Internet-of-things (IoT) nodes, wearable similes, and embedded cyber-physical systems has enhanced the necessity to deploy machine-learning (ML) models with a high diligence to function within harsh resource restraint conditions. Although traditional deep-learning models have high predictive accuracy, they usually require significant computational resources, memory and power which makes them infeasible in these settings. This paper provides a thorough proposal of accuracy-efficiency trade-off of lightweight ML models adapted to resource-constrained resource providers. We compare classical and modern lightweight methods of determining classification: linear frameworks, tree-based learners, shallow and compressed neural networks, on various performance metrics of accuracy, inference latency, memory base, and energy usage. Experimental outcomes based on commonly used benchmark datasets show that lightweight models can achieve competitive accuracy at significantly reduced overall computation overhead. The results also provide useful recommendations to select and design ML models in edge intelligence, real-time decision-making, and low-power AI models.
Authors - Sri Kavya Swarna, Varun Kumar Reddy Kola, DS Bhupal Naik, Dinesh Reddy Tiyyagura, Lakshmi Charitha Bandaru, Srinivasa Rao P. Abstract - This paper presents PricePulse, a web-based price comparison system that supports consumers with real-time multi-platform price analysis and AI-powered shopping insights. The system aggregates product data from Amazon, Flipkart, and Meesho via SerpAPI’s Google Shopping API and enriches results with recommendations generated by Google’s Gemini AI. Built on Next.js and Flask, PricePulse addresses gaps in the e-commerce ecosystem by eliminating manual price comparison across platforms. The system uses JWT-based authentication, maintains search history in SQLite, and provides an intuitive interface with React and Tailwind CSS. Evaluation shows average response times under 2 seconds and 95% accuracy in price extraction, demonstrating significant potential to help consumers make informed purchasing decisions and save on purchases.
Authors - Mazdak Zamani, Mohammad Naderi Dehkordi, Riham Hilal, Azizah Abdul Manaf, Achyut Shankar, Touraj Khodadadi Abstract - Nowadays, small networks are commonly used by people at home, in laboratories, or by small offices. These networks are not secured and an attacker can easily attempt to intrude these networks. To prevent this we need to continue to monitor the network and detect wrong activity early. Our simple system is called NetSentinels, and was developed in this project. It monitors the network traffic at all times and displays alerts message in case of a questionable event. We have used Snort which is free and open source tool. It assists in identifying attacks such as port scans, ICMP floods and multiple attempts of logging in. This system does not require the use of sophisticated devices thus can be installed in ordinary computers. NetSentinels can be applied in small networks to remain safe against attackers and enhance general security practices. In addition to real-time monitoring, the system also stores alert logs which can be used for later analysis and understanding attack patterns. The use of a virtual machine environment ensures safe deployment and easy portability across different systems. The system is designed to consume minimal CPU and memory, making it suitable for continuous operation without affecting system performance. Overall, NetSentinels provides a simple, low- cost and practical approach for improving network visibility and security awareness in small-scale environments.
