American International University-Bangladesh

Providencia stuartii is emerging as an Extensively Drug-Resistant (XDR) pathogen commonly found in animals, insects, and in burned and immunocompromised conditions. The misuse of antibiotics in poultry feed causes the emergence of XDR bacteria in the poultry industry. The knowledge of zoonotic transmissibility of poultry-derived P. stuartii remains elusive in Noakhali, Bangladesh. Poultry fecal and rectal swab samples were collected from selected farms in Noakhali, Bangladesh. Bacterial isolation and identification were performed using MacConkey agar, biochemical tests, and 16S rRNA Sanger sequencing. Antimicrobial susceptibility was assessed by the Kirby-Bauer disk diffusion method, and isolates with high multiple antibiotic resistance (MAR) indices were selected for whole-genome sequencing (WGS). Quality control, genome assembly, annotation, gene identification, pan-genome analysis, pathogenicity profiling, and comparative proteome analyses were subsequently conducted. Antibiogram analysis showed that ps_nstu_001 and ps_nstu_002 were resistant to 17 and 13 tested antibiotics, respectively. Furthermore, whole-genome sequencing revealed that both strains harbored resistance determinants to aminoglycosides, tetracyclines, sulfonamides, cephalosporins, β-lactams, and carbapenems. Additionally, mobile genetic elements (MGEs) and plasmids were identified, which represent the horizontal gene transfer capability. Moreover, pangenome analysis revealed ongoing gene acquisition and substantial genomic diversity among the isolates. The isolate ps_nstu_001 was identified as a putative human pathogen and clustered closely with a clinical strain isolated in the United States. In contrast, ps_nstu_002 was predicted to be a non-human pathogen; however, it exhibited a clear evolutionary relationship with a clinical isolate obtained from a diarrheal patient in Bangladesh, suggesting potential pathogenic relevance. Global pathogenic potential of the studied strains and key proteomic similarities between pathogenic and non-pathogenic strains revealed by pathogenicity profiling and proteome comparison. To conclude, these XDR isolates indicate the potential for zoonotic transmission and the spread of resistant genes to other animals, posing a significant public health risk.

Chronic Kidney Disease (CKD) remains a major contributor to global morbidity, often progressing unnoticed until advanced stages when treatment options become limited and costly. Recent advances in deep learning have reshaped early CKD assessment by enabling the analysis of complex imaging, clinical, and longitudinal laboratory datasets. This mini-review synthesizes findings from studies published between 2020 and 2025, highlighting models that report diagnostic accuracies ranging from 88% to 99.96%, AUC values reaching 0.93, and ensemble architectures capable of forecasting CKD 6 to12 months before clinical diagnosis with up to 99.31% accuracy. These systems spanning Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), hybrid CNN–LSTM designs, and transfer-learning frameworks have demonstrated clear advantages over conventional diagnostic markers such as serum creatinine and eGFR. Despite impressive numerical performance, key limitations persist: class imbalance in early-stage CKD, restricted generalizability due to single-centre datasets, variability in imaging quality, and the limited interpretability of high-capacity neural networks. As deep learning continues to advance, robust external validation, transparent model explanations, and multi-institutional datasets will be essential to support safe and reliable clinical integration.

Using Wi-fi in both public and private networks could be challenged by IEEE <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 0 2. 1 1}$</tex> MAC-layer protocol problems. This study concentrates on the Clear-to-Send (CTS) flood attack, characterized as a low-effort, high-impact threat that is significantly perilous and not widely recognized. This vulnerability “poisons” the Network Allocation Vector (NAV) of real devices by taking advantage of the fact that CTS control packets do not need to be checked. This weakness makes them silent for long periods of time and badly messes up communication.Today's intrusion detection systems (IDS) do not know about this issue since they focus on monitoring data and management frames. In addition, traditional rule-based protections also do not anymore work, which may be easily avoided by attackers. The proposed system learns the typical time, frequency, and length of the CTS traffic behavior, allowing it to identify anomalous changes. This paper offers a novel approach to detecting abnormalities employing sketch-based profiling to address significant security vulnerabilities. The proposed architecture offers a flexible and scalable solution to safeguard various Wi-Fi networks, ranging from public networks to corporate campuses, against an unidentified attack vector.

Lung cancer remains a leading cause of cancer-related mortality worldwide, largely due to delayed diagnosis and limitations in existing screening modalities. While computed tomography is widely used for lung cancer detection, concerns regarding ionizing radiation exposure and diagnostic specificity have encouraged the exploration of alternative imaging approaches. Recent advances in magnetic resonance imaging have improved its applicability for thoracic imaging, enabling enhanced visualization of lung tissue and functional characteristics without radiation risk. In parallel, rapid progress in machine learning has transformed medical image analysis by enabling automated, data-driven detection of complex pathological patterns. This editorial examines the emerging role of machine learning–based analysis of MRI images for lung cancer detection from a computer science and engineering perspective. It discusses technological developments in MRI acquisition, algorithmic advances in machine learning and deep learning, and the computational challenges associated with data scarcity, variability, and model interpretability. Ethical, clinical, and regulatory considerations relevant to real-world deployment are also highlighted. By synthesizing current trends and future research directions, this editorial underscores the potential of integrating machine learning with MRI to improve early lung cancer detection, reduce diagnostic risk, and support clinical decision-making. The topic represents a significant interdisciplinary opportunity for advancing intelligent medical imaging systems with meaningful impact on healthcare outcomes.

The study investigates the behavioral intentions of banking professionals to adopting cybersecurity in a developing economy, in this case, Bangladesh, through the impact of PE, EE, SI, and FC on the behavioral intentions. The research design used was a quantitative research design which involved 192 valid respondents who were collected using structured questionnaire on a five-point Likert scale. The convenience sampling was employed and PLS-SEM with SmartPLS 4 was implemented to test the relationships between constructs. The results reveal that the independent variables have significant effects on behavioral intention with SI being the strongest predictor which is the reason why the encouragement of peers and managers is so important in the establishment of secure behavior. In practice, these findings indicate that banks can improve CA by changing employee perception, streamlining security measures, organizational support, and applying social pressure via the use of leadership and peer pressure. On the social front, enhanced security activities in cybersecurity make banking operations safer to handle and secure the data of customers as well as build confidence in online financial services. The paper is novel in that it introduces the UTAUT 2 framework into the sphere of cybersecurity adoption in the Bangladeshi banking sector and provides theoretical and empirical knowledge on how behavioral factors are involved in adopting secure practices.

A deep water culture (DWC) hydroponic system was developed and tested with the purpose of measuring the quality of nutrient solution and plants. The system consists of the combination of a pH sensor, TDS sensor, the temperature sensor, and an ESP32 microcontroller to constantly obtain the nutrient reservoir data. The idea supports the Sustainable Development Goals, specifically Goal 15: Life on Land by reducing pressure on natural soil ecosystems and allowing a sustainable, land-independent way of food/crop production that coexists with nature. The sensor readings are sent to Blynk cloud dashboard through Wi-Fi, where the real-time data can be observed. Once a parameter goes out of the threshold values defined, the ESP32 sends a notification through the Telegram bot. The outcomes indicate that the pH was kept in 5.6-6.9, the TDS was in 853-1075ppm and temperature of solutions was kept between 26.4°C and 32.7°C. On the same time period the plant height increased from 5 inch (12.7 cm) to more than 20 inches (50.8 cm). Through the integration of IoT, the proposed system demonstrates the presence of reliable real-time control, active data recording, and efficient created alerts, which depict the ability to improve the precision and sustainability of modern hydroponic farming.

Cancer diagnosis has entered an era where precision depends not only on image quality but on the intelligence that interprets it. While deep learning has revolutionized medical imaging, its reliance on centralized data limits collaboration due to privacy constraints and fragmented data ownership. Federated Learning (FL) offers a breakthrough enabling multiple institutions to co-train robust diagnostic models without sharing sensitive patient data. This survey provides a comprehensive cancer-specific synthesis of state-of-the-art FL applications in medical imaging, spanning five critical domains: lung, breast, brain, skin, and colorectal cancers. Beyond summarizing prior work, we uncover patterns in architecture choice (U-Net variants, Convolutional Neural Network (CNN)–Recurrent Neural Network(RNN) hybrids, dataset reuse, and state-of-the-art privacy frameworks such as homomorphic encryption and blockchain-backed consensus. We expose performance bottlenecks, heterogeneity risks, and critical absences of clinical deployment benchmarks. What emerges is not just a landscape of what has been done but a roadmap for what is needed to build secure, distributed, and clinically validated Artificial Intelligence (AI) systems. This work offers a foundation for researchers and healthcare technologists aiming to close the gap between research silos and real-world deployment.

Ransomware poses a growing threat to healthcare systems, compromising patient safety, operational continuity, and financial stability. Although machine learning techniques have been widely used for intrusion detection, most approaches do not support real-time, cost-sensitive response planning. In this paper, we propose a hybrid framework that integrates deep learning with quantum optimization to both predict the severity of ransomware infection and recommend optimal recovery strategies. The system employs a multilayer perceptron (MLP) trained on structured ransomware incident data to forecast infection rates, followed by a quantum post-processing module using the Quantum Approximate Optimization Algorithm (QAOA) to minimize operational and financial costs through discrete response decisions. We evaluated the framework on a realistic healthcare ransomware dataset consisting of 5,000 simulated attack scenarios. Our approach achieves a root mean squared error (RMSE) of 0.073 in the prediction of infection rates and demonstrates up to 25% cost savings over classical heuristics in recovery decisions. Extensive experiments confirm the generalizability of the model to unseen attack types, the scalability across data volumes, and the stability of the decision in risk contexts. The proposed method represents a step toward intelligent real-time ransomware mitigation systems for high-risk environments such as healthcare.