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Research Article
Conversational AI and Chatbots: Enhancing User Experience on Websites
Manoj Kumar Dobbala*
,
Mani Shankar Srinivas Lingolu*
Issue:
Volume 7, Issue 3, September 2024
Pages:
62-70
Received:
18 June 2024
Accepted:
11 July 2024
Published:
29 July 2024
Abstract: This research paper explores the transformative potential of conversational AI and chatbots in enhancing website user experience (UX). It addresses two key research questions: How do these technologies improve user engagement and satisfaction on websites, and what are the primary challenges in implementing them, along with effective solutions. The study examines case studies across diverse industries, including e-commerce, travel, healthcare, and finance, to gain insights into the underlying technologies powering conversational AI and chatbots, such as natural language processing (NLP), natural language understanding (NLU), and machine learning techniques. The paper highlights the significant benefits of integrating conversational AI and chatbots into websites, including providing personalized assistance, streamlining complex processes, ensuring 24/7 availability, and enhancing accessibility for users. However, the study also addresses the key challenges faced in implementation, ranging from handling ambiguity and context in natural language processing to ensuring data privacy and security, managing user expectations, and the need for continuous improvement and training. The research proposes solutions to these challenges, such as employing advanced NLP algorithms, robust API management tools, and establishing user feedback loops. Ethical considerations, including data privacy and addressing biases in AI responses, are also explored, emphasizing the importance of robust encryption, adherence to data privacy regulations, and advanced access control mechanisms. The paper concludes by providing a comprehensive overview of the current state and future directions of conversational AI and chatbots in enhancing website user experience, exploring emerging trends such as multimodal interactions, contextual awareness and personalization, integration with IoT devices, and the development of emotional intelligence and empathy in chatbots.
Abstract: This research paper explores the transformative potential of conversational AI and chatbots in enhancing website user experience (UX). It addresses two key research questions: How do these technologies improve user engagement and satisfaction on websites, and what are the primary challenges in implementing them, along with effective solutions. The ...
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Research Article
Rethinking Multilingual Scene Text Spotting: A Novel Benchmark and a Character-Level Feature Based Approach
Siliang Ma,
Yong Xu*
Issue:
Volume 7, Issue 3, September 2024
Pages:
71-81
Received:
30 July 2024
Accepted:
26 August 2024
Published:
6 September 2024
Abstract: End-to-end multilingual scene text spotting aims to integrate scene text detection and recognition into a unified framework. Actually, the accuracy of text recognition largely depends on the accuracy of text detection. Due to the lackage of benchmarks with adequate and high-quality character-level annotations for multilingual scene text spotting, most of the existing methods train on the benchmarks only with word-level annotations. However, the performance of multilingual scene text spotting are not that satisfied training on the existing benchmarks, especially for those images with special layout or words out of vocabulary. In this paper, we proposed a simple YOLO-like baseline named CMSTR for character-level multilingual scene text spotting simultaneously and efficiently. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations, thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel. Furthermore, we show the surprisingly good extensibility of our method, in terms of character class, language type, and task. On the one hand, DeepSolo not only performs well in English scenes but also masters the Chinese transcription with complex font structure and a thousand-level character classes. On the other hand, based on the extensibility of DeepSolo, we launch DeepSolo++ for multilingual text spotting, making a further step to let Transformer decoder with explicit points solo for multilingual text detection, recognition, and script identification all at once.
Abstract: End-to-end multilingual scene text spotting aims to integrate scene text detection and recognition into a unified framework. Actually, the accuracy of text recognition largely depends on the accuracy of text detection. Due to the lackage of benchmarks with adequate and high-quality character-level annotations for multilingual scene text spotting, m...
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Research Article
Enhancing Software Testing Practices in Tanzanian Software Development Companies: A Case Study Approach
Magori Alphonce*
Issue:
Volume 7, Issue 3, September 2024
Pages:
82-89
Received:
16 July 2024
Accepted:
24 August 2024
Published:
20 September 2024
Abstract: Purposes: The primary objective of this conducted research is to investigate and propose strategies for improving software testing practices in Tanzanian software development companies. Specifically, the study identify the current state of software testing practices, understand challenges faced by software development companies in Tanzania, propose effective solutions, and evaluate their impact. Methodology: A mixed-methods approach employed to achieve the research objectives. Qualitative and quantitative data collection methods, including surveys, interviews, observations, documentation analysis, and experimentation, utilized to gather comprehensive insights into software testing practices. Purposive sampling employed to select diverse software development companies across different regions in Tanzania. Thematic analysis and statistical analysis applied to analyze qualitative and quantitative data, respectively, ensuring a robust examination of software testing practices. Findings: The research findings reveal the prevailing software testing practices in Tanzanian software development companies. Challenges such as resource constraints, inadequate test coverage, and limited collaboration between developers and testers are identified. Additionally, the study identifies best practices and proposes context-specific solutions to enhance software testing practices in Tanzanian companies. Statistical analysis provides quantitative insights into the effectiveness of proposed solutions. Unique Contribution to Theory, Practices and Policy: The study contributes to bridging the gap between academic research and industrial practices in software testing. Through addressing the unique challenges and opportunities in the Tanzanian context, the research provides actionable recommendations for improving software testing practices. The findings underscore the importance of tailored strategies and collaboration between academia and industry to enhance software quality and reliability in Tanzanian software development companies.
Abstract: Purposes: The primary objective of this conducted research is to investigate and propose strategies for improving software testing practices in Tanzanian software development companies. Specifically, the study identify the current state of software testing practices, understand challenges faced by software development companies in Tanzania, propose...
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Research Article
Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images
Purna Kar,
Sareh Rowlands*
Issue:
Volume 7, Issue 3, September 2024
Pages:
90-103
Received:
6 August 2024
Accepted:
2 September 2024
Published:
20 September 2024
Abstract: Colorectal Cancer is one of the most common and lethal forms of cancer hence, an early and accurate detection is crucial. Traditional manual diagnosis is a tedious and time-consuming job susceptible to human errors; therefore, it is imperative to use computer-aided detection systems to interpret medical images for a quicker and more accurate diagnosis. In recent years deep-learning approaches have proved to be efficacious in predicting cancer from pathological images. This study assesses several deep-learning techniques for cancer diagnosis on digitized histopathology images, amongst which GoogLeNet and Xception emerged as the most effective, with GoogLeNet exhibiting slightly better precision in identifying cancerous tissues. Building on these findings the study proposes a new model (Xception+) by borrowing the idea from Xception architecture, which outperforms existing architectures with an accuracy of 99.37% for cancer diagnosis and 94.48% for cancer-grade classification. The primary inference of our research is assisting pathologists in detecting colorectal cancer from pathological images faster and more accurately. With notable accuracy and robustness, our proposed model has significant potential to analyze pathological images and detect the patterns associated with other types of cancer. Our study holds promise for driving the advancement of innovative medical diagnostic tools, aiding pathologists and medical practitioners in expediting cancer diagnosis processes.
Abstract: Colorectal Cancer is one of the most common and lethal forms of cancer hence, an early and accurate detection is crucial. Traditional manual diagnosis is a tedious and time-consuming job susceptible to human errors; therefore, it is imperative to use computer-aided detection systems to interpret medical images for a quicker and more accurate diagno...
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Research Article
Joint Entity and Relation Extraction Using Machine Reading Comprehension for Urdu
Maria Riasat*
Issue:
Volume 7, Issue 3, September 2024
Pages:
104-114
Received:
4 July 2024
Accepted:
3 September 2024
Published:
26 September 2024
Abstract: Joint Entity and Relation Extraction (JERE) plays an important role in natural language processing (NLP) by identifying names, locations, and the relationships among them from unstructured text. Despite extensive research in languages like English, JERE poses significant challenges in low-resource languages, particularly Urdu, due to limited annotated da-ta and inherent linguistic complexities. In this paper, we propose a novel Machine Reading Comprehension (MRC)-based approach that effectively addresses the JERE task for Urdu, integrating a text encoder and a question-answering module that work synergistically to enhance entity and relationship extraction. We introduce an annotated Urdu JERE dataset and demonstrate how our methodology will significantly contribute to multilingual NLP efforts. We propose an innovative Machine Reading Comprehension (MRC)-based method to tackle JERE in Urdu. This method has two main components: a text encoder and a question answering (QA) module. The text encoder converts Urdu text into a compact vector form, which is then fed into the QA module. The QA module generates answers to queries regarding the desired entities and relationships, producing a sequence of tokens that represent these entities and their interactions. The model is trained to minimize the difference between its predicted answers and the correct ones. Our approach, along with the introduction of an annotated Urdu JERE dataset, significantly advances multilingual NLP and information ex-traction research. The insights gained can be applied to other low-resource languages, aiding in the development of NLP tools and applications for a broader array of languages.
Abstract: Joint Entity and Relation Extraction (JERE) plays an important role in natural language processing (NLP) by identifying names, locations, and the relationships among them from unstructured text. Despite extensive research in languages like English, JERE poses significant challenges in low-resource languages, particularly Urdu, due to limited annota...
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Research Article
Evolving Adversarial Training (EAT) for AI-Powered Intrusion Detection Systems (IDS)
Ahmed Muktadir Affan*
Issue:
Volume 7, Issue 3, September 2024
Pages:
115-121
Received:
23 June 2024
Accepted:
15 July 2024
Published:
29 September 2024
Abstract: Intrusion Detection Systems (IDS) are crucial components of network security, yet traditional IDS models often fail to cope with rapidly evolving adversarial attacks that exploit their static nature. This study proposes a novel approach, Evolving Adversarial Training (EAT), to enhance the adaptability and robustness of AI-powered IDS against dynamic threats. The EAT framework integrates continuous model evolution with advanced adversarial training techniques, enabling the IDS to dynamically adjust to new attack patterns. Experimental results demonstrate that the EAT framework significantly enhances IDS performance, leading to increased detection accuracy and reduced false positive rates compared to conventional methods. These findings emphasize the potential of EAT in fortifying network defenses against evolving cyber threats, offering a promising avenue for future research in scalable and adaptive IDS solutions that can effectively combat the complexities of modern cyber adversaries. The research explores three key objectives: dynamic adaptation and adversarial training, continuous learning and enhanced threat detection, and robustness and generalization. By focusing on these objectives, the study aims to develop AI-powered IDS that can effectively navigate the ever-changing cyber threat landscape. The research methodology includes data collection, model architecture design, training and evaluation, continuous learning, simulation, and real-world testing, all aimed at enhancing the resilience of AI-powered IDS against adversarial attacks. By systematically following this framework, the study intends to enhance the security system of IDS through the effective implementation of EAT.
Abstract: Intrusion Detection Systems (IDS) are crucial components of network security, yet traditional IDS models often fail to cope with rapidly evolving adversarial attacks that exploit their static nature. This study proposes a novel approach, Evolving Adversarial Training (EAT), to enhance the adaptability and robustness of AI-powered IDS against dynami...
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