Browsing by Author "Islam, Md.Hasibul"

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Internship on Software Quality Assurance (SQA)
(Daffodil International University, 2024-07-24) Islam, Md.Hasibul
Quality assurance for software (SQA) is crucial in making sure that the software products are reliable and trustworthy. This process eventually increases the consumer’s confidence as well as decreasing expensive post-release problems. Quality assurance (QA) involves meticulously analyzing and enhancing software engineering methods; hence, it creates an atmosphere in which efficiency and quality are entrenched while developing programs and tools. This post pertains to my internship experience at Creative Soft Technology Ltd. Creative Soft Technology Ltd was the choice for this internship because of its industry-wide recognition as a top provider of IT services marked by dynamic growth rate and swift project implementation through the use of standard technologies, among many others. It is a well-established company with an excellent track record.
TRACER: task-aware risk-adaptive architecture for continual edge learning
(BRAC University, 2026-02) Islam, Md.Hasibul; Fuad, Mir Muhammad; Tanzin, A.B.M. Fahim Hasan; Rahman, Md. Khalilur
Current computer-vision architectures are being deployed as long-lived services, especially on edge and on-device platforms, where input distributions change with changes in environment, users, sensors, and class frequencies. In these cases, to achieve sustainable performance, continual learning is required. Also, we need to keep in mind that the process needs to be feasible under strict constraints like latency and memory. Previous experience demonstrates that device-centric measures of deployment efficiency should be used instead of proxy metrics like FLOPs, and that tail latency (e.g., p95) is a more constrained measure of deployment efficiency than mean latency. At the same time, full neural architecture search (NAS) is generally too costly to integrate into a repeated learning loop, motivating restricted, hardware-aware search strategies. This thesis presents TRACER: Task-aware Risk-adaptive Architecture for Continual Edge leaRning, a deployability-oriented continual learning pipeline that keeps a fixed feature backbone and repeatedly selects and adapts a lightweight MLP classifier head. The system follows a restricted design space with a NAS-inspired controller and a Net2Net-optimized evolutionary population so that it can adapt efficiently. The stability between tasks is ensured through risk-aware exemplar rehearsal (high-risk samples are prioritized) and knowledge distillation. Experiments on Split CIFAR-100 (10 tasks x 10 classes) and CIFAR-10 (5 tasks x 2 classes) report class-incremental (CIL) and task-incremental (Task-IL) performance. Our proof-ofconcept implementation has a final CIL mean accuracy of 0.8145 and average forgetting of 0.0341, and TIL has a final mean accuracy of 0.970 on CIFAR-10. Also, in CIFAR-100, we got 0.6136 final CIL mean accuracy, and TIL has a final mean accuracy of 0.9055 with 0.0451 forgetting. These results, which are derived from a single deterministic run, demonstrate that Lagrangian-relaxation-based constraintaware head selection, combined with risk-sensitive stabilization, provides a practical accuracy-feasibility trade-off for continual learning under explicit latency targets.