https://ar.iub.edu.bd/handle/123456789/643 By CSE Department Thu, 16 Apr 2026 01:20:50 GMT 2026-04-16T01:20:50Z https://ar.iub.edu.bd/handle/11348/1053 TinyML for Environmental Intelligence: Optimizing Tree-Based Ensembles for Real-Time, On-Device IAQ Forecasting Mahir, Hasin; Zihan, Tahfizul Hasan Sun, 14 Dec 2025 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1053 2025-12-14T00:00:00Z https://ar.iub.edu.bd/handle/11348/1052 Design and Development of IoT-Integrated Medical Devices for Real-Time Clinical Monitoring Shihab, Rahat Hasan Reliable and affordable clinical monitoring remains a major challenge in low- and middle-income countries, where hospitals often face shortages of equipment, unstable power, and fragmented digital infrastructures. This thesis presents an integrated IoT- enabled biomedical system that addresses these gaps through three complementary com- ponents: a rapidly deployable phototherapy device for neonatal jaundice, a wearable vital sign monitoring platform for continuous measurement of temperature, ECG, pulse rate, and SpO2, and a scalable Internet of Medical Things (IoMT) framework that unifies de- vice data into a single operational environment. The phototherapy device provides stable irradiance, modular battery-backed operation, and remote status reporting. The vital sign monitor enables long-duration physiological sensing supported by bedside and cloud dashboards. The IoMT platform incorporates MQTT-based data ingestion, Node-RED edge processing, hybrid MySQL–MongoDB storage, and automated WhatsApp alerts to clinicians. System evaluations demonstrate sub-second latency, reliable data delivery, and improved response times in simulated clinical conditions. Together, these contribu- tions form a cohesive, low-cost, and scalable biomedical ecosystem designed for resource- constrained healthcare settings. Mon, 01 Dec 2025 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1052 2025-12-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/1039 A Comprehensive Hierarchical Review of Blockchain Technology in Revolutionizing Carbon Emission Trading System Khan, Nayeem; Tahmid, Ahanaf; Monir Adar, Ahasanul The growing climate crisis has raised the need to search for reliable mechanisms which could reduce greenhouse gas gas emissions worldwide. Carbon markets can continue to play a central role in this effort but often there is a problem of duplication of credit, fraud, multi-jurisdiction and poor traceability. There is an attempt to avoid such shortcomings by suggesting a multi-layered framework, called BAIC-Gov. The framework enables automated and verifiable monitoring and enforcement of carbon credits through blockchain technology, smart contracts, live real-time data on the Internet of Things (IoT) and anomaly detection via Artificial Intelligence (AI). Smart contracts are coded in such a way that they will be directly connected to the emissions data measured by IoT sensors, thus credit issuance as well as compliance will take place autonomously and with precision. Such application was illustrated with the use of the IBM-China ETS and Toucan Protocol. The framework enhances the climate governance, the ability of the market to scale, and the willingness by countries around the world to achieve carbon neutrality to establish a more sustainable future by strengthening confidence and integrity in carbon trading. Fri, 01 Aug 2025 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1039 2025-08-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/1037 A Unified Lightweight Framework for Detecting Multimodal Attacks Sayem, Md; Rakibul Hasan, Md.; Islam Anika, Morium Modern Vision-Language Model (VLM) has shown tremendous performance in multi- modal reasoning, captioning, retrieval and generative tasks, but it is also fatally suscep- tible to two types of attacks: adversarial perturbation and jailbreak prompts. In order to tackle these balancing threats, the current thesis presents two lightweight, model-free detection systems that will improve the security and resilience of modern multimodal systems. The former contribution is LMDF, which is a semantic-consistency-based ad- versarial detection model to detect perturbation-based attacks by evaluating cross-modal correspondence between image and text embeddings. Based on the conceptual underpin- nings of contrastive learning, LMDF uses the fact that adversarial perturbations, though imperceptible on the eye, introduce quantifiable distortions in the shared embedding space. LMDF identifies adversarial manipulations of frozen pretrained languages encod- ings like CLIP and BLIP-2 with high accuracy through the evaluation of cosine similarity between language and vision encodings. Major experiments between various datasets and attack algorithms (FGSM, PGD and Adversarial Patch) show high effectiveness with maximum accuracy and AUC scores reaching up to 91.2 and AUC up to 0.950 with mini- mum computational costs; only two forward passes and similarity calculations are needed. The second addition is a multimodal jailbreak detecting framework based on confidence which expands the concepts of Free Jailbreak Detection to vision-language environment. This method compares temperature scaled token probability distributions produced by decoder-based VLMs and derives five important statistical properties, namely minimum token confidence, first-token confidence, mean token confidence, entropy and confidence standard deviation. Jailbreak induces the typical instability of these confidence profiles which allow effective classification with a small threshold-based detector. Empirical val- idation shows great discriminative ability with AUC = 0.979, 90 percent accuracy and F1-score = 0.907 at an optimal temperature setting without adjusting its model, gradi- ent access, or evident computing cost.Combined, these two detection modules deal with different yet more and more common multimodal attack vectors. This paper combines semantic alignment analysis of adversarial perturbations with behavioral analysis based on confidence to offer a consistent, practical, and efficient defense mechanism to protect the modern VLMs. The suggested frameworks promote the objective of developing reli- able, multimodal AIs, which can work safely in the real-world environment, high-stakes, and adversarial settings. Sat, 13 Dec 2025 00:00:00 GMT https://ar.iub.edu.bd/handle/11348/1037 2025-12-13T00:00:00Z