Academic Excellence • Open Access • Global Reach
UU Academic Repository
Discover, preserve, and share the scholarly achievements of Uttara University’s faculty, researchers, and students — accessible to the world.
Preserved Research
Global Open Access
Permanent Archiving
Recent Submissions
Exploring Nonlinear Dynamics and Solitary Waves in the Fractional Klein–Gordon Model
(Advances in Mathematical Physics, 2025-06-05) Md. Abde Manna; Muktarebatul Jannah; Md. Habibul Bashar; Md. Ekramul Islam; Md. Zuel Rana; Mst. Tania Khatun; Md. Noor-A-Alam Siddik; Md. Shahinur Islam
Various nonlinear evolution equations reveal the inner characteristics of numerous real-life complex phenomena. Using the
extended fractional Riccati expansion method, we investigate optical soliton solutions of the fractional Klein–Gordon equation
within this modified framework. This model in physics claims the existence of energy particles and defines the relativistic wave. The
proposed procedures provide insight into wave spread and optical solitons, which enterprises in current broadcast communications can utilize to empower fast and long-distance information transmission with minimal signal degradation. They are important
for their reliability and optical communication networking. This method can analytically formulate optical soliton solutions using
rational, hyperbolic, and trigonometric functions. The interaction between the breather and the king wave, as well as the bright and
dark bell-shaped singular soliton waves, are the numerical forms of the obtained solutions, examined using three- and twodimensional diagrams. These solutions are obtained using the proposed method. For [α, β=0.1, 0.5, 0.9], we illustrate the impact of
conformable and beta fractional parameters in two-dimensional graphs. Understanding and clarifying the physical characteristics
of waves may be made easier by the collected results. Nonlinear optics, optical communications, and engineering all rely on unique
and precise soliton solutions, and the aforementioned applied techniques may, therefore, serve as a valuable tool for this purpose.
Exploring Nonlinear Dynamics and Solitary Waves in the Fractional Klein–Gordon Model
(Advances in Mathematical Physics, 2025-07-05) Md. Abde Mannaf; Muktarebatul Jannah; Md. Habibul Bashar; Md. Ekramul Islam; Md. Zuel Rana; Mst. Tania Khatun; Md. Noor-A-Alam Siddiki; Md. Shahinur Islam
Various nonlinear evolution equations reveal the inner characteristics of numerous real-life complex phenomena. Using the
extended fractional Riccati expansion method, we investigate optical soliton solutions of the fractional Klein–Gordon equation
within this modified framework. This model in physics claims the existence of energy particles and defines the relativistic wave. The
proposed procedures provide insight into wave spread and optical solitons, which enterprises in current broadcast communications can utilize to empower fast and long-distance information transmission with minimal signal degradation. They are important
for their reliability and optical communication networking. This method can analytically formulate optical soliton solutions using
rational, hyperbolic, and trigonometric functions. The interaction between the breather and the king wave, as well as the bright and
dark bell-shaped singular soliton waves, are the numerical forms of the obtained solutions, examined using three- and twodimensional diagrams. These solutions are obtained using the proposed method. For [α, β=0.1, 0.5, 0.9], we illustrate the impact of
conformable and beta fractional parameters in two-dimensional graphs. Understanding and clarifying the physical characteristics
of waves may be made easier by the collected results. Nonlinear optics, optical communications, and engineering all rely on unique
and precise soliton solutions, and the aforementioned applied techniques may, therefore, serve as a valuable tool for this purpose.
Numerical Modeling and Machine Learning-Assisted Analysis of Ultra-Thin CuSbS2 Solar Cells Incorporating SnS2 ETL and V2O5 BSF Layers
(Check access Back Numerical Modeling and Machine Learning-Assisted Analysis of Ultra-Thin CuSbS2 Solar Cells Incorporating SnS2 ETL and V2O5 BSF Layers Journal of Inorganic and Organometallic Polymers and Materials, 2025-08-11) Md. Abdul Monna; Avijit Ghosh; Asadul Islam Shimu; Qaium Hossain; Laboni Ferdoush; Mohammad Fokhrul Islam Buian; Anup Kumar Roy; Shahan Ahmed; Md Mahfuzur Rahman; Nondon Lal Dey; Aijaz Rasool Chaudhry; Md Al Imran
The clean and environmentally friendly characteristics of photovoltaic solar panel research have long been a source of
fascination. A promising option for solar absorber material for ultrathin film solar cells is the triple chalcostibite CuSbS2
(Copper Antimony Sulfide) system, It has earth-abundant components, affordable prices, vacuum-free production techniques, and an exceptionally high light absorption coefficient. However, the efficiency of a typical CuSbS2/CdS heterojunction solar panel is extremely low because of the Schottky barrier that forms at the back contact and the significant
recombination of carriers at the CdS/CuSbS2 interface. In this article, SnS2 (tin disulfide) is suggested as a substitute for
the CdS layer in CuSbS2-based TFSCs (Thin Film Solar Cells). SnS2, CuSbS2, and V2O5 have been used as ETL (Electron
Transport Layer), absorber layer, and BSF (Back Surface Field) layer, respectively. A new n-p-p+heterojunction solar
cell based on Al/FTO/SnS2/CuSbS2/Ni has been designed and simulated using the SCAPS-1D photovoltaic cell simulator. It has been investigated how integrating the V2O5 BSF layer affects the photovoltaic performances of the CuSbS2-
based heterojunction solar cell, about the back-contact recombination of carriers, and the built-in potential. Furthermore,
a systematic study has examined the effects of several device characteristics, including operating temperature, shunt and
series resistances, back-contact metalwork function, carrier concentration, and layer thickness. The outcomes are examined in connection with the device’s photovoltaic characteristics to maximize the suggested solar cell’s efficiency. At high
temperatures, the improved CuSbS2-based solar cell exhibits good performance stability and a maximum efficiency of
31.09%, VOC = 1.39 V, JSC = 25.09 mA/cm2
, FF=88.54%. Additionally, a Random Forest Machine Learning algorithm
predicts the optimum PCE (Power Conversion Efficiency) using semiconductor parameters. The model quantifies each
parameter’s importance using SHAP (Shapley Additive Explanations) values, revealing their contributions. The model
predicts performance accurately and provides precise results with a mean correlation coefficient (R2
) of 0.84. This study
highlights CuSbS2-based potential as a viable material for sustainable solar cells.
Exploring the Effectiveness of Machine Learning and Deep Learning Algorithms for Sentiment Analysis: A Systematic Literature Review
(Computers, Materials and Continua, 2025-07-30) Jungpil Shin; Wahidur Rahman; Tanvir Ahmed; Bakhtiar Mazrur; Md. Mohsin Mia; Romana Idress Ekfa; Md. Sajib Rana; Pankoo Kim
Sentiment Analysis, a significant domain within Natural Language Processing (NLP), focuses on
extracting and interpreting subjective information—such as emotions, opinions, and attitudes—from textual data.
With the increasing volume of user-generated content on social media and digital platforms, sentiment analysis has
become essential for deriving actionable insights across various sectors. This study presents a systematic literature
review of sentiment analysis methodologies, encompassing traditional machine learning algorithms, lexicon-based
approaches, and recent advancements in deep learning techniques.The review follows a structured protocol comprising
three phases: planning, execution, and analysis/reporting. During the execution phase, 67 peer-reviewed articles were
initially retrieved, with 25 meeting predefined inclusion and exclusion criteria. The analysis phase involved a detailed
examination of each study’s methodology, experimental setup, and key contributions. Among the deep learning models
evaluated, Long Short-Term Memory (LSTM) networks were identified as the most frequently adopted architecture for
sentiment classification tasks. This review highlights current trends, technical challenges, and emerging opportunities
in the field, providing valuable guidance for future research and development in applications such as market analysis,
public health monitoring, financial forecasting, and crisis management.
Bangla Speech Processing: An Analytical Study of Feature Extraction and Recognition Methods
(Mathematical Modelling of Engineering Problems, 2025-07-31) Md. Shafiul Alam Chowdhury; Md. Farukuzzaman Khan; Mohammed Sowket Ali; Md. Zahidul Islam; Md. Abdul Mannan; Md. Amanat Ullah
Speech recognition has always been an interesting yet challenging task for researchers,
especially when working with Bangla, which is complex due to its linguistic structure.
This research is extensive in scale, encompassing Bangla phonemes, isolated Bangla
words, commands, and sentences in the experiments. Bangla speech recognition is a
comparison analysis in large scale that focuses on different feature extraction
techniques, recognition tools, window frame feature, other methods and techniques
applied. A system is developed by writing code in MATLAB. Mel Frequency Cepstral
Coefficient (MFCC), Power Spectral Analysis (FFT), and Linear Predictor Coefficient
Analysis (LPC) methods are utilized as feature extraction techniques. Time delays
neural network (time series) and a two-layer feed forward hidden neural network are
used as speech recognition tools. The maximum likelihood method is also incorporated
to enhance the accuracy of speech recognition. Blackman, Hamming, and Hanning
Window frame techniques are applied in parallel during feature extraction to observe
their influences on speech recognition accuracy. The datasets gathered from native
speakers. MFCC as a feature extraction technique, combined with two-layer Feed
Forward Neural Network (FFNN) or TDNN as speech recognition tools, outperforms
FFT and LPC with the deep learning tools. The study discovered that both the quantity
of speech samples, the opposite gender’s voice, and different windowing techniques all
had an impact on the recognition accuracy rate. This study will encourage researchers
to conduct further research to advance Bangla speech recognition