2023

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A Diverse and Explainable Multi-hop QA Dataset for Bengali Language
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Intiser, Md. Aseer; Islam, Mohammad Munimul; Salehin, Md. Reyanus
Bengali is a resource-scare language with a scarcity of quality data sets both in single and multi-hp question answering. In an approach to fill that gap, we want to take a little step by generating a reading comprehension based open-domain multi-hop question answering which will be explainable and diverse. We will generate about 100 passages from news and Wikipedia articles and 500 question-answer pairs. We will maintain the diversity in selecting domains of contexts and also in generating questions and answers. Our data set will be explainable in generating the answer to a given question by providing supporting facts and showing the reasoning chain
A Machine Learning approach to Data Augmentation with Semantic Similarity on a Low-Resource Language
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Islam, Shah Jawad; Chowdhury, Mohammad Abrar; Alam, Taufiqul
The augmentation of data in low-resource languages gained significant importance re cently, primarily because of scarcity of datasets or the presence of highly unbalanced datasets. In the case of the Bengali language, the detection of fake news has turned up as a relevant problem, particularly in light of the surge in false information related to Covid-19 and the pandemic [1]. However, there has been a lack of adequately balanced data sets specifically designed for training Machine Learning (ML) and Deep Learning (DL) models in the detection of fake news in Bengali. Furthermore, previous attempts at augmenting fake news texts have yielded satisfactory results in lexical analysis but unsatisfactory results in terms of semantic relevance. To address these challenges, we propose a framework that involves the use of Text Augmentation techniques with the assistance of the Bangla Text-to-Text Transfer Transformer (T5) model. This frame work aims to balance an unbalanced Bengali fake news dataset, while ensuring that the augmented text retains semantic similarity and structural accuracy. By employing this approach, we seek to strengthen the effectiveness and reliability of fake news detection models in the Bengali language.
A peer to peer blockchain based approach for blood donation community
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Kamal, Minhaz; Abdullah, Chowdhury Mohammad; Shaiara, Fairuz
An Efficient Deep Learning-based approach for Recognizing Agricultural Pests in the Wild
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Mahjabin, Mohammad Ratul; Rahman, Md Sabbir; Raf, Mohtasim Hadi
One of the biggest challenges that the farmers go through is to fight insect pests during agricultural product yields. The problem can be solved easily and avoid economic losses by taking timely preventive measures. This requires identifying insect pests in an easy and effective manner. Most of the insect species have similarities between them. Without proper help from the agriculturist academician it’s very challenging for the farmers to identify the crop pests accurately. To address this issue we have done extensive experiments considering different methods to find out the best method among all. This paper presents a detailed overview of the experiments done on mainly a robust dataset named IP102 including transfer learning + finetuning, attention mechanism and custom architecture. Some example from another dataset D0 is also shown to show robustness of our experimented techniques. In both datasets our proposed model performed very well with an accuracy of 78% and 99.70% respectively.
An Efficient Feature Extraction Method For Static Malware Analysis Using PE Header Files
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Hossain, Onamika; Dhruba, Sadia Tasnim; Jalal, Fabiha
Detecting malware is crucial for safeguarding various devices, ranging from per sonal computers to large-scale systems,because computer systems continue to face serious security concerns from an increasing number of malware occurrences. Static analysis offers the ability to extract multiple file characteristics across var ious categories of information, eliminating the expenses and risks associated with dynamic analysis. By leveraging PE header information in machine learning classi fiers, an efficient feature extraction method can be developed to minimize the time required for feature extraction and therefore improve the analysis process. The objective is to enhance extraction time while maintaining a reasonable balance with other parameters, such as execution time, accuracy, and f measure.
An Empirical Study of the Impact of Developer Proficiency on Bug fixing Efficiency and Accuracy
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Hissan, Khairatun; Hasan, Adiba; Sananda, Fatema-tuz-Zohora
In the modern software systems’ evolution, solving bugs efficiently and reducing the life cycle of a bug has become increasingly essential. The developer’s proficiency has a huge impact in this case. So our target is to study the effect of developer’s profi ciency on bug fixing efficiency and accuracy. We conducted an empirical study on a bug repository of an open-source project containing approximately 42574 issues. We proposed six factors, Total number of solved tasks, Mean time to solve a task, Task reopen ratio, Total number of fixed bugs, Mean time to fix a bug, and Bug reopen ratio for calculating developers’ proficiency value. For validating our metric we also im plemented Structural Equation Model (SEM) in our study. The analysis of your data revealed that the selected factors do indeed impact a developer’s proficiency. Addition ally, assigning bugs to proficient developers was found to reduce the bug life cycle. We also observed that, highly proficient developers may not always exhibit a high level of accuracy. Therefore, to effectively reduce the bug life cycle, it is crucial to focus on both the proficiency and accuracy levels of developers
An Ensemble Method for Cancer Classification and Identification of Cancer-Specific Genes from Genomic Data
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Rizwan, Siana; Tabassum, Farzana; Islam, Sabrina
Classifying cancer using gene expression can be an important tool for under standing the specific characteristics of a patient’s cancer and for guiding the most appropriate treatment approach. By identifying the specific genes that are involved in the development and progression of a particular cancer, it may be possible to tailor treatment to target those genes and improve outcomes for the patient. In addition, by understanding the genetic makeup of a patient’s cancer, it may be possible to identify clinical trials or targeted therapies that may be more effective for that patient. Here, in our study, we worked with the TCGA Pan Cancer dataset where we used the RNA-seq data for analyzing the gene expres sions. The dataset comprises 33 types of cancer. Our study mainly focuses on implementing an explainable AI-based panCancer classification approach using gene expression analysis. The goal is to accurately detect the type of cancer in in dividuals within a short time. We employed seven classifier algorithms- Logistic Regression, SVM, XGBoost, Random Forest, MLP, 1-D CNN, and TabNet. To enhance the performance of the models, we utilized feature selection techniques such as Lasso, SelectFromModel, Select-K-Best, and ElasticNet. SelectFrom Model with 500 features yielded the best performance. We applied ensemble methods of probability averaging and max voting, with probability averaging achieving the highest accuracy of 96.60%. Validation of the selected features’ contribution and comparison with gene sets from DESeq2 analysis confirmed their significance and relevance. This approach provides insights into cancer specific molecular mechanisms and pathways. Overall, our study demonstrates the effectiveness of feature selection in reducing dimensionality while maintain ing predictive power and biological relevance
Answer-agnostic Bangla Question-answer Pair Generation Using Transformer-based Approaches
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Altaf, Md Sajid; Ekram, Syed Mohammed Sartaj; Rahman, Adham Arik
High-resource languages, such as English, have access to a plethora of datasets with various question-answer types resembling real-world reading comprehension. However, there is a severe lack of diverse and comprehensive question-answering datasets in under-resourced languages like Bangla. The ones available are either translated versions of English datasets with a niche answer format or created by human annotations focusing on a specific domain, question type, or answer type. To address these limitations, we introduce BanglaRQA, a reading comprehension-based Bangla question-answering dataset with various question answer types. BanglaRQA consists of 3,000 context passages and 14,889 question-answer pairs created from those passages. The dataset comprises answerable and unanswerable questions covering four unique categories of questions and three types of answers. In addition, we also implemented four different Transformer models for question-answering on the proposed dataset. The best-performing model achieved an overall 62.42% EM and 78.11% F1 score. However, detailed analyses showed that the performance varies across question-answer types, leaving room for substantial improvement of the model performance. Furthermore, we demonstrated the effectiveness of BanglaRQA as a training resource by showing strong results on the bn_squad dataset. We focus on Bangla Question-answer pair generation for the next part of our work. Bangla, being a less explored language in NLP, lacks comprehensive research in the do main of question-answer pair generation. We focus on developing this untapped sector by fine-tuning BanglaT5, a generative model on the BanglaRQA dataset. The quality of the generated questions is first evaluated using various metrics. The best-performing model, BanglaT5, achieved a BLEU score of 21.56 and a BERT score of 85.04, indicating that the generated questions exhibit decent quality. Subsequently, the research progresses toward the main task of generating question-answer pairs. The quality of the generated pairs is evaluated through human assessment and baseline comparison, demonstrating that the generated QA pairs possess comparable quality to human-annotated QA pairs. Therefore, this work proposes an end-to-end Question-Answer-Generation (QAG) pipeline and presents a reading-comprehension-based dataset, that has the potential to contribute to future research
Attack and Anomaly Detection in IoT Devices using Federated Learning
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-04-30) Adib, Mosabbir Sadman; Raf, Moshiur; Pranto, MD Jabear Hossain
There has been a lot of focus from governments, universities, and businesses in recent years on the intersection of cybersecurity and machine learning (ML) for the Internet of Things (IoT). The Internet of Things (IoT) can be made more secure and efficient in the future through the groundbreaking concept of federated cybersecurity (FC). This new idea has the ability to efficiently identify security problems, implement countermeasures, and contain them within the IoT network infrastructure. Cybersecurity goals are met through the federation of a shared and learned model among several actors. Protecting the insecure IoT environment requires privacy-aware ML models like federated learning (FL).
Bangla Dataset Generation for Natural Language Inference
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Islam, Md. Shohidul; Khan, Abdun Nayeem; Nizami, Md Shaidur Rahman
Understanding entailment and contradiction is fundamental to understanding nat ural language, and inference about entailment and contradiction is a valuable test ing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of resources in Bangla. To address this, we propose to introduce our own corpus curated for natural language inference which is labeled pairs of sentences with a label that depicts their inner entailment. Our goal is to create a dataset that has over 30K instances and to do so we have now created a Bangla dataset by machine trans lating the SNLI corpus into Bangla. After that, we show that benchmark models can be used to evaluate and do the task of inference in Bangla . We hope that our dataset will catalyze research in Bangla sentence understanding by providing an informative standard evaluation task.For this we provided two baseline models which are both considered integral in the task of inference in any langauge.
Blockchain based Message Dissemination in Vehicular Ad Hoc Networks
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Muntaha, Sidratul; Maliat, Ramisa; Haider, Urbana Musharrat
Vehicular Adhoc Networks (VANETs) is a promising research interest in the field of wireless networks. It is an application of the principle of Mobile Adhoc Networks (MANETs). It is used to provide services such as road safety, navigation, traffic monitoring etc. The continuously changing topology of the network introduces challenges in implementing VANET. Resolving these challenges following different strategies introduces other trade-offs. One of the most important applications in VANET is to disseminate incident messages to nearby vehicles. The effectiveness of the application depends on the correctness of the incident message and its timely delivery to the vehicles. Blockchain is one of the mechanisms that can be used in this respect. Consensus mechanism is used to validate the message and then a proper forwarding mechanism is used to disseminate the message. An incentive mechanism is used to encourage honest behaviour of the nodes. The prominent consensus mechanisms used in blockchain such as Proof of Work (PoW), Proof of Stake (PoS), Proof of Elapsed Time (PoET) are not suitable to be used in VANET in their basic form. For example, PoW is highly time consuming and PoS is biased. Among the existing ones Practical Byzantine Fault Tolerant (PBFT) is the most suitable one for blockchain based VANET. So in our thesis we propose a new consensus mechanism which is a hybrid of the best practices of PoW and PBFT. It includes selective voting mechanism with weighted values for faster and more accurate validation. The threshold values are updated whenever needed. Limiting the number of voters makes the entire process efficient. The challenges in this respect are handled by imposing proper conditions on the voters. The concept of weighted sum of votes is introduced where honest voters are prioritized over others which results in higher accuracy in message validation in shorter amount of time. Efficient selection of relay nodes ensures minimum latency in the dissemination process by ensuring minimum number of messages are passed. It also ensures quality of the message being passed. Along with the consensus mechanism we also present a mechanism to select relay nodes which will give the best performance in the message dissemination process by selecting node that will cover the maximum possible distance. The selected nodes spread the message to the maximum number of vehicles with the minimum number of broadcast messages. The ii Abstract iii forwarding process is continued until a threshold is reached. An incentive mechanism based on both reputation and monetary units is also proposed which will encourage integrity and honest behaviour from the vehicles. Previous works show the success of incentive mechanism based on both reputation and monetary units over the ones based on only one of them. We also integrate the concept of reputation in the validation process to increase its importance. The simulation is done in the Omnet++ simulator platform integrated with Sumo. We showed the analysis of the results obtained from the simulation. The results give impressive improvements from the existing systems
Blood Cancer Prediction using Leukemia Microarray Gene Data and Deep Learning
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-06-30) Hossain, MD Mehdad; Siddiquee, MD Abul Kalam; Hossain, Muhammad Yeasin
The diagnosis of blood cancer with the use of any leukemia microarray gene se quence data and a machine learning approach is one of the most important fields of medical research. More advancements are needed to obtain the requisite accu racy and efficiency notwithstanding research efforts. Our work’s major goal is to present a method that, using microarray gene data, can accurately predict blood cancer. By increasing the classification accuracy for automated analysis of microarray data analysis, our research seeks to suggest a deep learning model to identify and categorize various types of leukemia. We will use the Leukemia GSE28497 dataset for training our model which contains 281 samples consisting of 22,285 genes (features) of 7 target classes. We preprocess the dataset by deleting null items before training our models. For the prediction of the blood cancer classes, we investigate three classification algorithms: logistic regression, single-layer neural networks, and TabNet. We use a variety of met rics, such as model accuracy, model loss, confusion matrix, train value accuracy, train value loss, and ROC curve, to measure the performance of our models. The outcomes of our studies analyze the effectiveness of deep learning models for clas sifying different forms of blood cancer from microarray gene dat
Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Haque, Md. Maksudul; Islam, Samiul; Sadat, Abu Jobayer Md.
Traditional approaches in speech emotion recognition, such as LSTM, CNN, RNN, SVM, and MLP, have limitations such as difficulty capturing long-term dependen cies in sequential data, capturing the temporal dynamics, and struggling to capture complex patterns and relationships in multimodal data. This research addresses these shortcomings by proposing an ensemble model that combines Graph Con volutional Networks (GCN) for processing textual data and the HuBERT trans former for analyzing audio signals. We found that GCNs excel at capturing Long term contextual dependencies and relationships within textual data by leveraging graph-based representations of text and thus detecting the contextual meaning and semantic relationships between words. On the other hand, HuBERT utilizes self-attention mechanisms to capture long-range dependencies, enabling the mod eling of temporal dynamics present in speech and capturing subtle nuances and variations that contribute to emotion recognition. By combining GCN and Hu BERT, our ensemble model can leverage the strengths of both approaches. This allows for the simultaneous analysis of multimodal data, and the fusion of these modalities enables the extraction of complementary information, enhancing the discriminative power of the emotion recognition system. The results indicate that the combined model can overcome the limitations of traditional methods, leading to enhanced accuracy in recognizing emotions from speech.
Damaged Building Detection Using Global Local Attention
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Khadija, Nejd
Dark Triad detection and analysis from social media text
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Morsalina; Fairoz, Fariha; Anjum, Fariha
The increased use of social media platform usage such as Facebook has given an oppor tunity to express one’s thoughts and ideas to everyone. Social media posts can be used as a medium for determining different psychological traits, such as dark triad character istics. In our thesis, we used peoples’ social media posts, and using those posts we tried to detect presence of dark triad traits based on the hand crafted features extracted from their posts. We also have shown, the usage of code mixing as a effective feature. We have used traditional machine learning models, ensemble models as well as transformer based language models to find the best possible outcome. For finding linguistic fea tures analysis we have used Polarity of text, subjectivity, lexical density, word tokens, word count, avg word length, word freq dist, stopword count, part of speech, Topic seg mentation and many more. We then compared the performance of all the models.For all the traits, ensemble of 4 traditional machine learning models outperformed all other models.The highest accuracy achieved in narcissist detection was 96.36% , for Machi avelli it was 98.27%, and for psychopathy it was 99.62%.
Deep Learning Approach: Image Captioning in French and Arabic Language
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Keita, Abdoulaye; Hamadou, Mohaman Dairou; Asag, Mazen Abdulwahab Mahyoub Salem
This research report introduces a novel dataset of French captions translated from the Flickr30k dataset using different translation models, namely we have Google Trans late and the powerful Transformers: T5 Small and T5 base models. A novel dataset of French captions means creating fresh data collection by translating existing captions from the Flickr30k dataset into French. The Flickr30k dataset is valuable for training and evaluating image captioning models in French. The main objective is to address the problem of generating precise image captions in French. The performance of an image captioning model is evaluated on the translated datasets, employing ResNet-50 for image feature encoding and LSTM network with attention in generating captions. These results demonstrate that the accuracy of image captions varies depending on the translation(or Language) models, with the Trans formers models outperforming Google Translate. The proposed approach achieves state-of-the-art performance in generating accurate French captions when combined with ResNet-50 and LSTM network with attention. The findings contribute to the field of image captioning and machine translation for French speakers, highlighting the importance of using advanced translation models for improved caption accuracy and other NLP tasks in French. Furthermore, this research provides insights into the potential of smaller-scale models in limited data scenarios. Based on our findings, we can explore alternative translation models, and data aug mentation techniques, and consider multi-modal approaches that could lead to more accurate and contextually relevant captions and the potential of this approach in other languages
Dialog Generation with Conversational Agent in the Context of Task-Oriented using a Transformer Architecture
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Petouo, Faysal Mounir; Arafat, Yaya Issa
The use of conversational agents has become increasingly popular in recent years due to their ability to mimic human-like interactions in Human Com puter Interaction (HCI) and provide personalized assistance to users. How ever, creating effective dialogues between humans and conversational agents remains a challenging task, particularly in the context of task-oriented ap plications. This is because such applications require agents to understand complex user requests and generate appropriate responses that take into ac count the user’s goals, preferences, and constraints.To address this challenge, we propose to adapt the LongT5 (Long Text-To-Text-Transfer Transformer) architecture, a transformer-based language processing model well known for its performance in a lot of Natural Language Processing (NLP) tasks. Then, to explore the use of the new proposed model named MegaT for generating task-oriented dialogues between conversational agents and human user. This involves designing and implementing a task-oriented conversational agent trained on annotated dialogues related to specific tasks. The agent’s per formance will be evaluated using metrics such as belief accuracy, belief loss, response accuracy, and response loss. The results have been analyzed to identify the strengths and weaknesses of the T5 transformer, the current state-of-the-art model in task-oriented dialogue generation . Experimental results demonstrate that MegaT outperforms the T5-based agent in terms of generating accurate, fluent, and coherent responses to user queries, as well as handling longer sequences of text and producing more informative and engag ing responses. We also found that our proposed Transient Global attention for task-oriented dialogue systems produce better results than the local at tention mechanism used in LongT5 on MultiWoz 2.2 dataset. The thesis aims to contribute to the development of more effective conversational agents by 1 leveraging the LongT5 model for generating high-quality task-oriented dia logues. This Study provides insights into the use of this recent transformer model and paves the way for further advancements in the field of dialogue generation with conversational agents. . Furthermore, it opens new avenues for future research in the field of dialogue generation with conversational agents.
Efficient Ensemble-Based Approaches to Personal Health Mention Detection
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-04-30) Nower, Nuzhat; Kamal, Fida; Khan, Alvi Aveen
An important component of public health surveillance is the analysis of personal health-related posts on social media platforms, known as Personal Health Mention (PHM) Detection. PHM detection is essential to quickly detecting epidemics, allowing health organisations to prepare themselves and warn the general public to take precautionary steps. One of the key complexities of this task is the informal nature of the language used in social media, which makes it difficult to understand their context. The architectures that are capable of discerning context are also computationally expensive to use and often mistrusted in the medical community due to their decision-making strategies being hidden behind a black box. In this thesis, we address each of these issues separately. We introduce four transformer-based ensemble architectures that have not been previously explored in the PHM domain and show that these architectures can achieve state-of-the art results across the domain. Combined with computationally efficient training mechanisms, our architectures also use fewer resources than existing ones. Addi tionally, we provide methods to explain the outputs produced by the architectures in order to address the concerns related to explainability.
Efficient Leader Head Selection In Vanet By Minimizing Message Overhead
(2023-05-30) Aboubakar, Mouhammad; Mamoudou, Abdoulaye; Nom, Aminata
Vehicular Ad-hoc Networks (VANETs) require efficient leader head selection algorithms to optimize network management tasks and communication overhead In this paper, we offer a proactive approach technique to reduce message overhead while retaining effective network performance for leader head selection in VANETs. To create clusters and choose leader heads, the system makes use of predictive models of vehicle motion and communication patterns. The proposed technique achieves better scalability, decreased communication costs, and improved network performance by minimizing the number of messages exchanged and optimizing cluster formation. The algorithm's success in decreasing message overhead and ensuring effective leader head selection in VANETs is demonstrated by simulation results. Vehicle-to-vehicle (V2V) communications have rapidly advanced in recent years, opening the door for new applications tackling concerns like autonomous driving, traffic efficiency, and vehicle safety. These applications may greatly enhance the driving experience, travel time, fuel efficiency, traffic safety, and other elements directly connected to automobiles. In many of these circumstances, a coordinator vehicle is necessary to coordinate the right-of-way among many cars. Such a coordinator or leader vehicle is essential in many situations where a cooperative goal is desired for all cars in the group. Effective leader head selection algorithms are crucial in the context of vehicular ad hoc networks (VANETs) for optimizing network management activities and reducing communication overhead. The proactive approach strategy that is suggested in this study attempts to lower message overhead while maintaining efficient network performance in VANETs. To generate clusters and choose leader heads, the method makes use of predictive models of vehicle movements and communication patterns. The suggested method delivers higher scalability, lower communication costs, and increased network performance by utilizing predictive models. This is accomplished by optimizing cluster formation and reducing the volume of communications transferred during the leader-head selection process. To choose the best leader, the system considers a number of variables, including vehicle velocity, proximity, connection, and communication patterns. The algorithm can efficiently divide the leadership position among cars in a way that improves network performance and lowers communication costs by taking these aspects into account. Results from simulations show how the suggested technique is successful in lowering message overhead and guaranteeing efficient leader head selection. The simulations illustrate how the algorithm can build effective clusters and choose appropriate leader heads, improving throughput, latency, and reliability across the network. The algorithm's proactive strategy and implementation of predictive models let it adapt effectively to VANETs' dynamic nature, where cars are continually moving and entering/leaving the network. The development of vehicle-to-vehicle (V2V) communications has created new possibilities for applications relating to autonomous driving, traffic efficiency, and vehicle safety in a more general sense. The existence of a coordinator or leader vehicle is essential in many of these scenarios in order to promote coordination and collaboration among several vehicles. In order to enable smooth coordination and collaboration inside VANETs, the suggested leader head selection algorithm solves this need by effectively recognizing and choosing leaders' heads. Overall, the proactive strategy and technique based on predictive modeling described in this study provide solutions that have promise for lowering message overhead, improving cluster formation, and ensuring efficient leader head selection in VANETs. These developments have the potential to improve a number of directly linked features of vehicles and their interactions in V2V communication scenarios, including the driving experience, journey duration, fuel efficiency, traffic safety, and others.
Evaluation of User Experience of Bangladesh E-government Services: A Student’s Perspective
(Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Imtiaz, Nafiz; Rahman, Ibtid; Robe, Md. Adnan Rahman
With the government striving to make the country digitally driven, the Bangladesh Na tional Digital Architecture (BNDA) framework was made to ensure improved and more user-friendly services. The e-services of the Bangladesh Government have already im plemented the BNDA framework. The usability of those services is a prime concern to be able to reach a large number of users. Although previous studies on the usability of government websites were made, no significant research from the user experience per spective has been done on the newly designed e-service websites. This research aims to evaluate the User Experience(UX) of two e-service websites namely, Railway Ser vice and Surokkha-Vaccine Management System of the Bangladesh Government from the students’ perspective. As students are a majority part of the user base of these e-services both directly and indirectly, it is crucial to know about their user experi ence. The study uses the widely recognized Jakob Nielsen’s 10 Usability Principles for user interface design for Heuristic Evaluation (HE) and User Experience Questionnaire (UEQ) questionnaire for the evaluation of user experience. The findings prove that the user experience of the e-services is still not meeting the UX standards. Specifically, the novelty factor needs more improvement than the rest of the factors as their results are mostly below average or less according to the gathered data. The data gathered from this research can be used to make e-service websites that give a more user-friendly ex perience by following UX standards