arXiv subjects

K. S. M. Tozammel Hossain

Publications and source records attributed to K. S. M. Tozammel Hossain.

3 recordsLinked to original sources

arXiv2026

Fin-Analyst at FinMMEval 2026 Task 3: A Live Hybrid Trading Agent with LLM Specialists and Rule-Based Signals

Large language model (LLM) trading agents show promising performance in equity markets, yet remain narrowly focused on US equities with little evidence from live deployment. We present Fin-Analyst, a hybrid agent for FinMMEval 2026 Task 3: an eight-specialist LLM pipeline over news, SEC filings, fundamentals, analyst forecasts, technical indicators, and social sentiment, aggregated by a Meta-Agent for Tesla (TSLA), and a lightweight rule based three-signal vote for Bitcoin (BTC). On the final official leaderboard (accessed 2026-07-05), Fin-Analyst ranks first of all agents on TSLA with a +13.51% return, +28.33 points over Buy-and-Hold (Sharpe 4.10, 88% win rate), while the BTC vote ends flat yet well above a sharply falling baseline. Relative to the interim performance, the asset ranking reversed, indicating that short live windows yield volatility-sensitive rankings. Ablation identifies event-driven 8-K disclosures as the most influential TSLA signal. Error analysis shows that the memoryless agents repeat wrong calls for days at a time, and that the fixed-threshold BTC rules lost money by trading on noise in a sideways market while the LLM pipeline gained under similar conditions, motivating a memory-aware, LLM-based successor for both assets.

cs.CL↗

arXiv2024

CerviXpert: A Multi-Structural Convolutional Neural Network for Predicting Cervix Type and Cervical Cell Abnormalities

Cervical cancer is a major cause of cancer-related mortality among women worldwide, and its survival rate improves significantly with early detection. Traditional diagnostic methods such as Pap smears and cervical biopsies rely heavily on cytologist expertise, making the process prone to human error. This study introduces CerviXpert, a multi-structural convolutional neural network model designed to efficiently classify cervix types and detect cervical cell abnormalities. CerviXpert is built as a computationally efficient model that classifies cervical cancer using images from the publicly available SiPaKMeD dataset. The model architecture emphasizes simplicity, using a limited number of convolutional layers followed by max pooling and dense layers, trained from scratch. We assessed the performance of CerviXpert against other state of the art convolutional neural network models including ResNet50, VGG16, MobileNetV2, and InceptionV3, evaluating them on accuracy, computational efficiency, and robustness using five fold cross validation. CerviXpert achieved an accuracy of 98.04 percent in classifying cervical cell abnormalities into three classes and 98.60 percent for five class cervix type classification, outperforming MobileNetV2 and InceptionV3 in both accuracy and computational requirements. It showed comparable results to ResNet50 and VGG16 while reducing computational complexity and resource needs. CerviXpert provides an effective solution for cervical cancer screening and diagnosis, balancing accuracy with computational efficiency. Its streamlined design enables deployment in resource constrained environments, potentially enhancing early detection and management of cervical cancer.

eess.IV↗

arXiv2016

Forecasting the Flu: Designing Social Network Sensors for Epidemics

Early detection and modeling of a contagious epidemic can provide important guidance about quelling the contagion, controlling its spread, or the effective design of countermeasures. A topic of recent interest has been to design social network sensors, i.e., identifying a small set of people who can be monitored to provide insight into the emergence of an epidemic in a larger population. We formally pose the problem of designing social network sensors for flu epidemics and identify two different objectives that could be targeted in such sensor design problems. Using the graph theoretic notion of dominators we develop an efficient and effective heuristic for forecasting epidemics at lead time. Using six city-scale datasets generated by extensive microscopic epidemiological simulations involving millions of individuals, we illustrate the practical applicability of our methods and show significant benefits (up to twenty-two days more lead time) compared to other competitors. Most importantly, we demonstrate the use of surrogates or proxies for policy makers for designing social network sensors that require from nonintrusive knowledge of people to more information on the relationship among people. The results show that the more intrusive information we obtain, the longer lead time to predict the flu outbreak up to nine days.

cs.SI↗