Prime University Bangladesh

Speech emotion detection is driven by complex neurological mechanisms within the brain's nervous system, reflecting an individual's emotional state and psychological makeup. This unique characteristic serves as a foundation for many applications, including personalized user experiences, mental health diagnostics, and demographic analysis based on emotional responses. Traditional approaches to emotion recognition frequently face challenges such as variability in speech patterns, environmental noise, and speaker diversity. Within the evolving landscape of human-computer interaction, this research introduces a novel synthesis of Convolutional Neural Networks (CNNs) with the domain of speech emotion recognition, thus surpassing the boundaries of traditional methodologies. This research analyzed 957 audio samples from 5 individuals using benchmark datasets, extracting Mel Frequency Cepstral Coefficients (MFCC) to classify emotions accurately-including ‘neutral’, ‘happy’, ‘angry’, ‘fear’, ‘surprise’, ‘calm’, ‘disgust’, and ‘sad’. Departing from traditional approaches reliant on manual feature engineering, our end-to-end model autonomously learns from raw speech, with our CNNs achieving an exceptional 96.7% accuracy. This marks a significant advancement in speech emotion recognition through deep learning, paving the way for more intuitive, emotion-aware human-computer interactions.

The burgeoning adoption of Software Defined Networking (SDN) has revolutionized network management, yet it introduces unprecedented challenges, notably the susceptibility to Distributed Denial-of-Service (DDoS) attacks. Recognizing this imperative, our research delves into fortifying SDN security, proposing a novel approach that marries machine learning prowess with the intricacies of SDN architecture. This study endeavors to bolster DDoS detection within SDN environments, strategically leveraging an ensemble-based Random Forest (RF) algorithm and Recursive Feature Elimination. The overarching goal is to enhance the efficacy of SDN security measures, providing a dynamic defense against evolving DDoS threats. An implementation process unfolds through comprehensive data pre-processing, featuring the strategic selection of key features via Recursive Feature Elimination. Central to our approach is the application of an ensemble-based Random Forest algorithm, which has been rigorously trained using a dedicated dataset tailored for Software Defined Networking. A comprehensive assessment follows, where critical performance indicators such as Recall, Accuracy, Precision, F-1 Score, and Area Under the Curve (AUC) substantiate the reliability of our method. The outcome is a paradigm shift in DDoS detection within SDN. Our ensemble-based RF algorithm not only exhibits commendable accuracy but also outperforms traditional methods across key metrics. The strategic feature selection contributes not only to heightened efficiency but also bolsters the overall resilience of SDN networks against DDoS incursions. Beyond the confines of conventional methodologies, this model, attaining almost 100% accuracy, heralds a milestone in SDN security.

Three different wheat varieties were the subject of an experiment to see how salt affected seed germination and seedling development. BARI Gom20 (Gourab) was employed as a salt sensitive variety, whereas BARI Gom28 and BARI Gom25 were utilized on salt tolerant types. Three replications, three salinity levels (0, 150, and 200 mM of NaCl), and a complete randomized design (CRD) were used to set up the experiment. All the wheat varieties had a noticeable growth loss due to salt stress. Wheat growth was inhibited more in salt-sensitive cultivars compared to salt-tolerant cultivars, BARI Gom28 showed overall stronger salt tolerance. When wheat was tested for salt tolerance, there were striking variations in the antioxidant enzymes (catalase, peroxidase, and ascorbate peroxidase) that were present. The effect of rising salt content, catalase (CAT), ascorbate peroxidase (APX), and peroxidase (POD) activity varied between salt-sensitive and salt-tolerant cultivars. Intriguingly, TaAOX1a and TaAOX1c gene expression levels increased in BARI Gom28 as the degree of salt concentration increased. Additionally, the salt-tolerant BARI Gom28 had smaller accumulations of hydrogen peroxide, malondialdehyde, and higher activity of the antioxidant enzymes than the salt-sensitive Gourab, indicating that the latter had comparatively less oxidative damage. However, the contribution of antioxidant enzymes and AOX gene family to salinity stress response in wheat is further requisite to unlock the molecular functions. Exploring the molecular mechanism for lowering salt stress in wheat cultivars will require more research.

In an era where the relentless evolution of cyber threats necessitates the perpetual advancement of security measures, the detection of obfuscated malware has emerged as a formidable challenge. The clandestine tactics employed by malicious actors demand innovative solutions that transcend conventional approaches. In this context, this research present a groundbreaking research endeavor that redefines the frontiers of obfuscated malware detection using artificial intelligence. In this research, a comprehensive methodology is introduced that combines three pivotal feature selection techniques: correlation analysis, mutual information, and principal component analysis. This hybrid approach not only enhances the discrimination of meaningful features but also ensures the efficiency and effectiveness of the feature subset, thus mitigating the curse of dimensionality. To harness the full potential of these meticulously selected features, an array of ensemble-based machine learning algorithms, including AdaBoost, stacking, random forest, bagging, and voting, is deployed. Amongst these, our findings demonstrate that AdaBoost emerges as the preeminent choice, achieving unprecedented levels of performance. The outcomes underscore the profound impact of our research in the realm of obfuscated malware detection, a paradigm shift that reimagines the very essence of security. In a world where cyber-security challenges continually escalate, our research represents a pivotal milestone in the unceasing battle to safeguard digital landscapes. It is an exultant testament to the boundless potential of innovative feature selection techniques and the supremacy of AdaBoost within the domain of malware detection.

The hotel industry needs a clearly defined brand image in order to thrive and survive in a rapidly changing global market (Chi, 2016). This study strives to refine the determinants of customer-based brand equity (CBBE) that impact brand image and identify the moderating effect of tourism management and advertisement on the determinants and brand image in the context of the Bangladesh tourism industry. Data were collected through structured questionnaires from the selected four- and five-star hotel customers in Bangladesh and analysed using SmartPLS 2.0. It found that brand awareness, brand association, brand superiority, brand resonance and corporate social responsibility (CSR) were significant factors influencing brand image. CSR was the most significant among these five determinants, followed by brand superiority, brand association, brand awareness, and brand resonance. It was also explored that tourism management has a mediation effect on the degree of relationship between brand superiority and brand image, and brand resonance and brand image. Alternatively, the degree of advertisement affects the extent of the relationship between brand awareness and brand image, brand association and brand image, CSR and brand image. The tourism industry can utilize the findings of this study to enhance its marketing and branding strategies.

Foreign language anxiety (FLA) is one of the major affective factors that influence the process of foreign language learning. This study examined the causes of foreign language anxiety and its impact on the oral performance of the students of an English language course at a private university in Bangladesh. An action research project attempted to illustrate how this foreign language anxiety could be minimized by adopting entertaining activities in the class. The study employed qualitative method using in-depth semi-structure interviews, field notes, and audio-video recordings of the oral performance of the learners. The thematic analysis highlighted a substantial gap between the English learning of Bangladeshi university students and their speaking anxiety. The findings of this research suggest that creating a stress-free environment through enjoyable activities helps reduce learner anxiety and maximizes speaking output. The study uncovers a new dimension of the nature of FLA experienced by English learners at the tertiary level, which may help teachers adapt teaching materials and sustain the classroom environment to ensure maximum outcomes in the EFL context.

Background Telehealth is comprised of telecommunications and electronic information systems to support and maintain long-distance healthcare services. Although it has not been thoroughly explored, the intention of using the service among the general public is critical to its success. We investigated the factors associated with the intention to utilize telehealth services among the general population of Bangladesh. Methods This cross-sectional study was conducted between May 22, 2021 and June 15, 2021 in Bangladesh, where the total number of participants was 1038. The Pearson chi-square test and Kruskal-Wallis H tests were used to examine the unadjusted relationship between the explanatory variables and the intention to use telehealth services. A multinomial logistic regression model was fitted to determine the adjusted association. Shapiro-Wilk tests were used to check the normality of continuous data. Data were processed and analyzed by software STATA-16. Results The probability of utilizing the service increased significantly with increasing knowledge, perceived benefit, and predisposition levels among respondents. However, when perceived concern increased, the likelihood of utilizing the service dropped significantly. Age, marital status, educational status, profession, residence, and perceived health status were significantly associated with the participants’ intention to utilize the telehealth service. Conclusions The influencing aspects of telehealth service utilization should be recognized by the respective authorities. Possible activities to enhance usability among people are also recommended.

This analysis is framed to take care of the high heat transfer demand from various application prospects and in this context nanofluid may be in consideration because nanofluids are well-known liquids with higher heat transfer capabilities. Here, a mixed convection flow of Molybdenum disulfide-water (MoS 2 -H 2 O) nanofluid on a moving vertical plate is examined when there are chemical reactions. Nanofluid problem is investigated using Tiwari and Das model, and numerically solved by “bvp4c” method. For the considered effects in the study, overshoots in temperature profile of nanofluid are observed. Momentum and thermal layer thicknesses become thinner with suction and thicker with blowing. Temperature overshoot is higher for blowing cases. Enhanced homogeneous–heterogeneous reactions lead to rise of the concentration boundary layer’s thickness. Also, magnitude of surface-drag force decays and cooling rate enhances with MoS 2 -H 2 O nanofluid mixed convection. For weaker suction and for blowing, cooling of nanofluid is faster with larger volume fraction of nanoparticles, but contrast results are obtained for greater suction. Importantly, compared to the homogeneous reaction, the heterogeneous reaction exhibits more impactful influences on flow and heat-mass transport characters.

Background and Aim Social media is undeniably more accessible and more appreciated today. It is undoubtedly one of the most crucial instruments for student communication. Mental health status can also meaningfully influence the students at the higher levels of the educational institutions. This study aims to evaluate the social media usage of university students and its impact on academic performance and mental health. Methods To examine under confirmatory factor analysis (CFA) several scale measurements were confirmed by justifying the validity and reliability of several necessary indices and structural equation model. The mediation analysis was also estimated to evaluate the students' Social media addiction (employed Bergen Social Media Addiction Scale) under maximum likelihood estimation with 2000 bootstrapping and 95% bias‐corrected bootstrap confidence intervals. Results This study shows that the usage of social media significantly improves academic performance on psychological well‐being, with a Comparative Fit Index of 0.921 and an RMSEA of 0.06 indicating a good fit of the CFA model. Finally, we exhibit a strong statistically significant positive impact of social media usage on academic success, and as supporting the hypothesis, the study observed a positive mediating role of mental health between social media addiction and academic performance. Conclusion The present research investigations produced unique results, that is, online social media enhances mental health and mediates the link between social media addiction and academic performance in Bangladeshi students. This finding also add to the empirical database on social media usage and have significant theoretical and practical ramifications.

Stress creates a major health-related issue in our society, because many health-related problems, such as a lot of economic losses, social disruptions, and human mental problems, are the consequences of it. In general, humans experience stress, especially those who are involved in work in developed capitalist countries and under huge mental workloads continuously and endless technological development. Stressors come across in our daily life (for instance, the difference of opinion among family members or hard work deadlines) and may play a vital role in personal health and well-being. In this study, we introduce a model of health awareness system that incorporates two assessment strategies: questionnaire asking method and physical measurement method, to determine the stress level using the android application-enabled portable device. The questionnaire asking method is useful for detecting psychological and behavioral scores of the stress level. These questionnaires were fixed based on the score of the subjective measure by surveying twenty (20) psychological questions related to the stressor. To estimate the stress level more precisely, we also used the measurement analysis method which includes the physical health-related fitness tests. The application has been developed using android studio IDE and smartphones. By identifying ongoing stress situations using the application, the users can modify physical or behavioral lifestyles to successfully avoid them. It is revealed that such an application can be applied effectively in research experiences and advances the research on stress level estimation.

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a realtime framework that incorporates the Internet of Things (IoT), Blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the Internet of Things (IoT) and Blockchain technologies facilitates establishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the Blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 seconds after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms.

Because Open Access is such an essential component in the method of fostering the development of scientific research and progress, a great number of academics are fixated on this issue. When it comes to the process of building a complete library collection for academic institutions, the incorporation of Open Access materials presents the opportunity for the process to be sped up, which is a positive development. This study seeks to identify how professionals and users perceive Open Access resources (OARs), identify any obstacles that professionals may have while incorporating OARs into their libraries, and provide a set of tactics to eliminate these obstacles. A study of six Bangladeshi academic institutions with 454 participants was conducted, with the current study quite promising for any library intending to implement or advocate for OARs in their systems.

Background and Aims The number of social media users is growing with each passing day at full tilt, keeping pace with digitalization and technological advances. Despite several advantages, there are also certain negative aspects to using social networking sites (SNS) for communication, amusement, self‐expression, impression management, and other purposes. This study sought to investigate the association between mental health status and flaunting behaviors in social media among the general population in Bangladesh. Methods We conducted this nationwide cross‐sectional online survey among 465 people aged between 18 and 60 between October 15, 2021 and January 15, 2022. Following electronic consent, we collected the socio‐demographic profiles and psychometric parameters of the respondents. Additionally, we assessed the diverse perspectives on SNS usage and its relationship to the self‐reported symptoms of depression and loneliness. Results The estimated prevalence of loneliness and depressive symptoms were 65.16% (mild: 39.57%, moderate: 16.56%, severe: 9.03%) and 55.49% (mild: 26.67%, moderate: 22.15%, severe: 6.67%), respectively. Key factors associated with flaunting on social media were mental health issues such as depression and loneliness. Several social factors were also considered, such as being young, of the male sex, unmarried, illiterate, a student, urban dwelling, average economic status, nuclear family structure, types of SNSs, checking social media first in the morning, and the use of SNS for gaining popularity. Conclusion A significant portion of SNS users reported symptoms of mental illness. Current study findings urge for longitudinal studies with larger sample sizes to have a nearly equal distribution of users from each social media platform for in‐depth exploration of how user attitudes about SNSs and site usage patterns impact the general public's mental health. We suggest that regulating SNS usage patterns and treatment approaches would improve the situation.

The internet of things (IoT) relies on fifth generation (5G) networks as the foundation for interconnecting devices.Wireless networks are an essential component of 5G-IoT technology, as they provide the means to interconnect devices and transmit data wirelessly. The performance of the wireless network, including its capacity, integrity, bandwidth, and latency, is critical in ensuring the reliable and secure transmission of data in 5G-IoT. 5G is being developed with the goal of delivering exceptionally high capacity, solid integrity, high bandwidth, and low latency. With the development and innovation of new approaches for 5G-IoT, new significant security and privacy concerns are certain to arise. As a result, secure data transmission mechanisms will be required as the foundation for 5G-IoT technologies in order to solve these emerging difficulties. Deoxyribonucleic acid (DNA) cryptograms encrypt data by utilizing it as a carrier and biological technology. On an 8 by 8 multiantenna single carrier frequency division multiple access (SC-FDMA) wireless system, we evaluate and compare the performance of DNA sine map-based encrypted images using three different modulation algorithms: 16-QAM, 16-PSK, and 16-DPSK.We also determine the bit error rate (BER) value for different signal to noise ratio (SNR) by analyzing the decrypted image’s modulation performance. The methods minimum mean square error (MMSE), minimum mean error square successive interference cancelation (MMSE-SIC), and zero-forcing (ZF) are utilized for signal detection. Simulations in MATLAB reveal that the system is very effective and reliable when tested with MMSE-SIC signal detection, 16-QAMmodulation, and low-density parity check (LDPC) channel coding.

In this research, we proposed new inorganic ZrS 2 /CuInS 2 heterojunction solar cells based on 2D dichalcogenides material using SCAPS-1D software. Transition metal dichalcogenides (TMDs) are two-dimensional materials with outstanding semiconducting properties due to their high optical absorption coefcients, nontoxic nature, signifcant charge carrier mobility, and tunable energy band structures. In this study, eco-friendly solar cells having the arrangement Al/ZrS 2 /CuInS 2 / Au have been quantitatively analyzed. Tis simulation employed the absorber layer CuInS 2 and the bufer layer ZrS 2 with aluminum as the front contact and gold as the back contact. Te impact of the absorber layer thickness, band gap, bufer layer thickness, acceptor density, defect density, series and shunt resistances, C-V, Mott-Schottky, and the operating temperature has been studied for the proposed solar cell structure. Te best performance of proposed solar cell structure thickness, band gap, and donor density for n-ZrS 2 is 0.3 µm, 1.7 eV, 1 × 10 19 cm −3 , and for p-CuInS 2 , respectively, 4 µm, 1.43 eV, 2 × 10 17 cm −3. Te suggested solar cell has a power conversion efciency of 21.1% with 0.81 V Voc, 30.5 mA/cm 2 Jsc, and 85.78% FF. Te analysis reveals that CuInS 2 absorber material and ZrS 2 semiconducting transition metal dichalcogenides (TMDs) are potential materials for photovoltaic applications.

The development in textile dying technologies has presented new types of dyes that are toxic to the ecosystem. Azo dyes are the main artificial dyes used in textiles, food, and other applications. Typically, these dyes are introduced into the environment as wastewater discharged from factories. The discharged influence penetrates the ecosystem and causes deadly diseases to human and animals. Several studies present activated carbon as a proper solution to eliminating the presence of azo dyes in the environment. However, various types of azo dye have different properties and chemical structures. Thus, there is a crucial need for more studies on the application of activated carbons to eliminate the presence of azo dyes in the environment. This paper discusses the toxic effects of azo dyes on the environment and human health. Moreover, this work presents a general review of the preparation of activated carbon and the parameters that influence the adsorption performance.

The COVID-19 coronavirus, which primarily affects the lungs, is the source of the disease known as SARS-CoV-2. According to “Smoking and COVID-19: a scoping review”, about 32% of smokers had a severe case of COVID-19 pneumonia at their admission time and 15% of non-smokers had this case of COVID-19 pneumonia. We were able to determine which genes were expressed differently in each group by comparing the expression of gene transcriptomic datasets of COVID- 19 patients, smokers, and healthy controls. 37 dysregulated genes are common in COVID-19 patients and smokers, according to our analysis. We have applied all important methods namely Protein-Protein Interaction, Hub-Protein Interaction, Drug-Protein Interaction, TF-Gene Interaction, and Gene-MiRNA Interaction of bioinformatics to analyze to understand deeply the connection between both smoking and COVID-19 severity. We have also analyzed Pathways and Gene Ontology where five significant signaling pathways were validated with previous literature. Also, we verified 7 hub-proteins, and finally, we validated a total of 7 drugs with the previous study.

In this research, we have proposed a Sn-based perovskite solar cell using solar cell capacitance software. The main aim of this study is to develop an environment-friendly and highly efficient structure that can be used as an alternative to other toxic lead-based perovskite solar cells. This work performed a numerical analysis for the proposed (Al/ZnO/SnO2/CH3NH3SnI3/Ni) device structure. The absorber layer CH3NH3SnI3, buffer layer SnO2, and electron transport layer (ETL) ZnO, with aluminium as the front contact and nickel as the back contact, have been used in this simulation. Several analyses have been conducted for the proposed structure, such as the impact of the absorber layer thickness, acceptor density, defect density, series and shunt resistances, back contact work function, and operating temperature. The device simulation revealed that the optimum thickness of the absorber layer is 1.5 μm and 0.05 μm for the buffer layer. The proposed Sn-based perovskite structure has obtained a conversion efficiency of 28.19% along with FF of 84.63%, Jsc of 34.634 mA/cm2, and Voc of 0.961 V. This study shows the upcoming lead-free perovskite solar cell’s enormous potential.