arXiv subjects

Rakibul Islam

Publications and source records attributed to Rakibul Islam.

7 recordsLinked to original sources

arXiv2026

Meta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering

Accurate plant growth estimation is essential for greenhouse monitoring, yet obtaining labeled data remains costly and time-consuming. To address this, we propose a few-shot regression framework that combines Vision Transformer (ViT) feature embeddings, clustering-based task construction, and gradient-based meta-learning, and show that task construction in embedding space is a primary driver of performance. The approach leverages an unlabeled image pool to organize data into structured tasks using fuzzy c-means clustering, enabling efficient learning from a small number of labeled samples. We systematically evaluate meta-learning methods and show that second-order methods (e.g., Model-Agnostic Meta-Learning variants such as MAML++) outperform classical baselines in the few-shot regime. Furthermore, intra-cluster support selection has a limited and dataset-dependent impact. Experiments on two plant datasets show that structured task design combined with meta-learning enables reliable plant growth estimation under severe label scarcity.

cs.CV↗

arXiv2026

A micromechanical frequency reference with parts-per-trillion holdover stability

Microelectromechanical (MEMS) resonators are widely used in timekeeping applications, and recent advances in fabrication, materials, and encapsulation technology have advanced their potential as high stability frequency references. However, for holdover applications that require the highest levels of long-term frequency stability, compact vapor atomic clocks remain dominant. In this work, we demonstrate a 268 MHz MEMS clock that achieves record fractional frequency stability of ~8 parts-per-trillion at an averaging time of 8 hours, competitive with chip-scale atomic clocks. We achieved this using a single-crystal silicon electrostatic resonator that has no currently known intrinsic drift mechanism and is protected from the environment with a wafer-level encapsulation. We specifically identify gain variations in the sustaining electronics as the dominant limitation in conventional phase-locked oscillator architectures -- originating from temperature sensitivity and drifts in the electronic components -- and overcome this by implementing a frequency-locked loop architecture based on dual-frequency resonance tracking (DFRT). This novel approach removes the specific gain of the supporting electronics as a frequency determining variable in the oscillator. When combined with dual-mode tracking and ratiometric temperature stabilization of the resonator, this approach enables a dramatic enhancement to long-term frequency stability and establishes gain-insensitive DFRT locking as a general paradigm for high-stability MEMS clocks.

physics.app-ph↗

arXiv2025

Code Smell Detection via Pearson Correlation and ML Hyperparameter Optimization

This study addresses the challenge of detecting code smells in large-scale software systems using machine learning (ML). Traditional detection methods often suffer from low accuracy and poor generalization across different datasets. To overcome these issues, we propose a machine learning-based model that automatically and accurately identifies code smells, offering a scalable solution for software quality analysis. The novelty of our approach lies in the use of eight diverse ML algorithms, including XGBoost, AdaBoost, and other classifiers, alongside key techniques such as the Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance and Pearson correlation for efficient feature selection. These methods collectively improve model accuracy and generalization. Our methodology involves several steps: first, we preprocess the data and apply SMOTE to balance the dataset; next, Pearson correlation is used for feature selection to reduce redundancy; followed by training eight ML algorithms and tuning hyperparameters through Grid Search, Random Search, and Bayesian Optimization. Finally, we evaluate the models using accuracy, F-measure, and confusion matrices. The results show that AdaBoost, Random Forest, and XGBoost perform best, achieving accuracies of 100%, 99%, and 99%, respectively. This study provides a robust framework for detecting code smells, enhancing software quality assurance, and demonstrating the effectiveness of a comprehensive, optimized ML approach.

cs.CE↗

arXiv2024

An Experiment on Feature Selection using Logistic Regression

In supervised machine learning, feature selection plays a very important role by potentially enhancing explainability and performance as measured by computing time and accuracy-related metrics. In this paper, we investigate a method for feature selection based on the well-known L1 and L2 regularization strategies associated with logistic regression (LR). It is well known that the learned coefficients, which serve as weights, can be used to rank the features. Our approach is to synthesize the findings of L1 and L2 regularization. For our experiment, we chose the CIC-IDS2018 dataset owing partly to its size and also to the existence of two problematic classes that are hard to separate. We report first with the exclusion of one of them and then with its inclusion. We ranked features first with L1 and then with L2, and then compared logistic regression with L1 (LR+L1) against that with L2 (LR+L2) by varying the sizes of the feature sets for each of the two rankings. We found no significant difference in accuracy between the two methods once the feature set is selected. We chose a synthesis, i.e., only those features that were present in both the sets obtained from L1 and that from L2, and experimented with it on more complex models like Decision Tree and Random Forest and observed that the accuracy was very close in spite of the small size of the feature set. Additionally, we also report on the standard metrics: accuracy, precision, recall, and f1-score.

cs.LG↗

arXiv2022

Human-guided Collaborative Problem Solving: A Natural Language based Framework

We consider the problem of human-machine collaborative problem solving as a planning task coupled with natural language communication. Our framework consists of three components -- a natural language engine that parses the language utterances to a formal representation and vice-versa, a concept learner that induces generalized concepts for plans based on limited interactions with the user, and an HTN planner that solves the task based on human interaction. We illustrate the ability of this framework to address the key challenges of collaborative problem solving by demonstrating it on a collaborative building task in a Minecraft-based blocksworld domain. The accompanied demo video is available at https://youtu.be/q1pWe4aahF0.

cs.HC↗

arXiv2017

Effect of air confinement on thermal contact resistance in nanoscale heat transfer

We report herein the pressure dependent thermal contact resistance (Rc) between Wollaston wire thermal probe and samples which is evaluated within the framework of an analytical model. This work presents heat transfer between the Wollaston wire thermal probe and samples at nanoscale for different air pressure ranging from 1 Pa to 10e5 Pa. We make use of a scanning thermal microscopy (SThM) for the thermal analysis of two samples, fused silica (SiO2) and Titanium (Ti), with different thermal conductivities. The thermal probe's output voltage difference (deltaV) between out-off contact output voltage (Voc) and in-contact output voltage (Vic) was recorded. The result shows that the heat transfer increases with the increasing air pressure and it is found higher for higher thermal conductive material. We also propose an analytical model based on the normalized output voltage difference to extract the probe-sample thermal contact resistance. The obtained Rc values in the range of ~1.8e7 K/W to ~14.3e7 K/W validate the presented analytical model. Further analysis reveals that the thermal contact resistance between the probe and sample decreases with increasing air pressure. Such behavior is interpreted by the contribution of heat transfer through confined air on thermal contact resistance. In addition, the signature of Rc is also evidenced in the context of thermal mismatch behavior which is studied by using acoustic mismatch model (AMM) and diffuse mismatch model (DMM).

cond-mat.mes-hall↗

arXiv2015

Material Independent Long Distance Pulling, Trapping, and Rotation of Fully Immersed Multiple Objects with a Single Optical Set-up

Optical pulling with tractor beams is so far highly dependent on (i) the property of embedding background or the particle itself , (ii) the number of the particles and/or (iii) the manual ramping of beam phase. A necessary theoretical solution of these problems is proposed here. This article demonstrates a novel active tractor beam for multiple fully immersed objects with its additional abilities of yielding a controlled rotation and a desired 3D trapping. Continuous and stable long distance levitation, controlled rotation and 3D trapping are demonstrated with a single optical set-up by using two coaxial, or even non-coaxial, superimposed non-diffracting higher order Bessel beams of reverse helical nature and different frequencies. The superimposed beam has periodic intensity variations both along and around the beam-axis because of the difference in longitudinal wave-vectors and beam orders, respectively. The difference in frequencies of two laser beams makes the intensity pattern move along and around the beam-axis in a continuous way without manual ramping of phase, which allows for either linear motion (forward or backward) or angular movement (clockwise or anticlockwise) of fully immersed multiple particles. As a major contribution, the condition for increasing the target binding regions is also proposed to manipulate multiple immersed objects of different sizes and shapes.

physics.optics↗