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Leveraging AI and NLP in Chatbot Development: An Experimental Study

Leveraging AI and NLP in Chatbot Development: An Experimenta...

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Frontiers of Information Technology (FIT)

作者： Abdul Wahab Paracha Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Faculty of Electronic Systems Engineering University of Regina Regina Canada

In the current era of chatbots, this research delves into the advancements in AI chatbots, drawing on artificial intelligence (AI) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model’s performance indicates a considerable dependence on the dataset’s size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems.

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Mitigating Crop Losses: AI-enabled Disease Detection in Tomato Plants

Mitigating Crop Losses: AI-enabled Disease Detection in Toma...

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Frontiers of Information Technology (FIT)

作者： Hassan Ashfaq Usama Arshad Raja Hashim Ali Zain ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Fostering crop health is vital for global food security, underscoring the need for effective disease detection. This research introduces an innovative artificial intelligence (AI) model designed to enhance the detection and diagnosis of diseases in tomato plants, particularly focusing on Early Blight and Late Blight. Significantly, our model leverages cutting-edge image processing techniques to improve disease detection efficiency, outperforming traditional methods in terms of speed and accuracy. Our results demonstrate an impressive model accuracy of 92.58% on training data and 86.83% on validation data, showing the effectiveness of AI in diagnosing plant diseases. These high accuracy rates underline the potential of our model for timely disease classification, allowing for immediate and appropriate interventions. However, our research also identified a potential overfitting problem in the model’s performance. To address this, we propose using regularization and data augmentation techniques to enhance the model’s generalizability on unseen data. Additionally, we delve into inherent challenges that plague AI-based plant disease detection, such as the scarcity of diverse datasets and the difficulty of achieving broad generalizability across different plant species. In identifying potential solutions for these issues, our research lays the groundwork for the wider and more practical implementation of AI technologies in agriculture.

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Android malware family classification using images from dex files 9

Android malware family classification using images from dex ...

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9th International Conference on Smart Media and Applications, SMA 2020

作者： Kang, Munyeong Park, Jihyeo Park, Seonghyun Cho, Seong-Je Park, Minkyu Dept. of Software Science Dankook University Korea Republic of Dept. of Computer Engineering Dankook University Korea Republic of Dept. of Software Technology Konkuk University Korea Republic of

ISBN: (纸本)9781450389259

With the popularization 1 of the Android platform, Android malware occupies the largest portion of mobile malware. Malware family classification is important for fast and accurate detection. We propose a new detection method using images generated from Dex files of Android apps. We generate two kinds of images: one from an entire DEX file and one from a data section of a DEX file. We apply the CNN algorithm to the classification of both kinds of images. The experiments show that the proposed method classifies malware families with 91% accuracy for both cases. In the case of using only the data section, the performance of the ExploitLinuxLotoor family and Gappisin family were improved. Also, the deviation between Precision, Recall, and F1-Score was greatly reduced. The area under the Precision-Recall curve is almost the same in both experiments, which means that detection time can be shortened without deteriorating detection performance. © 2020 ACM.

关键词： Android malware

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Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation

Performance Evaluation of Popular Deep Neural Networks for N...

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Frontiers of Information Technology (FIT)

作者： Muhammad Naeem Abu Bakar Siddique Raja Hashim Ali Usama Arshad Zain ul Abideen Talha Ali Khan Muhammad Huzaifa Shah Ali Zeeshan Ijaz Nisar Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

The field of Neural Machine Translation (NMT) has shown impressive performance for quick and easy communication in various languages spoken all over the world. NMT helps us by improving communication between different languages. For this purpose, different sequential models are used such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU). Analysis among these different models are important for making language translation better and choose the best model for the right job. This research investigates the performance of these models on two distinct language datasets, English-to-German and English-to-Urdu. Based on accuracy metrics, the findings reveal that GRU having test accuracy (88.22% ) outperforms RNN (87.21% ), and LSTM (85.70% )demonstrating the highest translation accuracy, followed by RNN and LSTM exhibiting comparatively lower accuracy levels.

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An innovative approach: Coupling project-based learning and game-based learning approach in teaching software engineering course

An innovative approach: Coupling project-based learning and ...

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2020 IEEE International Conference on Technology, engineering, Management for Societal Impact Using Marketing, Entrepreneurship and Talent, TEMSMET 2020

作者： Malhotra, Ruchika Massoudi, Massoud Jindal, Rajni Delhi Technological University Software Engineering Department Delhi India Parwan University Software Engineering Department Charikar Afghanistan Delhi Technological University Computer Science and Eng Dept Delhi India

ISBN: (纸本)9780738142715

software engineering Students often have a low level of motivation and lack of interest in traditional teaching methods. Project-Based Learning (PBL), coupled with Games, helps in improving student's involvement in the project and enhance their learning experience. In this paper, the overall concept of PBL-gamification for teaching software engineering has been discussed, and it is conclusive that students can outperform in this innovative concept of gamification and PBL. There is the future scope in terms of improving PBL-Gamification so that it will merge seamlessly with conventional education pattern, thereby filling the industry-academia gap. © 2020 IEEE.

关键词： Students

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A Novel Trust Assessment System for Online Social Networking Environment Using Learning Assisted Classification Model

A Novel Trust Assessment System for Online Social Networking...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Nithya D. Deepa Vunnava Dinesh Babu Pallavi G Hamed Fawareh R. D. Kayalvizhy Department of Computer Science Engineering Saveetha Engineering College Chennai Department of CSE St. Joseph's College of Engineering Chennai Department of CSE RV Institute of Technology Guntur Department of Civil Engineering Laxminarayan Innovation Technological University Nagpur India Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia Department of Electronics and Communication Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Trust is essential in the constantly developing environment of online social networks. The Nexus Terroism Impact and Intro Trust facilitate the communication, decision-making, and maintenance of digital communities among individuals. Trust-based Learning with Classification Principle (TLCP) is introduced in this research to improve the assessment of trust in social networks. To ascertain its efficacy, our proposed methodology undergoes a 10-fold cross-validation against the Trust Assessment Methodology (TAM). The traditional Trust Assessment Model (TAM) evaluates and regulates trust in Internet social networking. This method incorporates trust assessment factors to generate a user dependability judgment that is both discriminatory and dynamic. The Trustworthy Account Management (TAM) challenge is designed to enhance digital trust by addressing disinformation, deception, and hostile conduct. The TLCP is able to accurately evaluate trust by utilizing reputation scoring, community analysis, and user behavior metrics. Trust levels are dynamically adjusted as a result of the identification of trustworthy behavioral patterns and danger indicators by machine learning algorithms and social media data. This research examines the connections between trust management and digital interactions and suggests a general solution to alleviate specific trust evaluation issues across online social networks.

关键词： Measurement Machine learning algorithms Social networking (online) Privacy breach Real-time systems Hazards Maintenance Internet Fake news Trust management

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AD-PU: A Novel Approach for Automated Identification of the Outliers in User Interface Testing (UAT)

AD-PU: A Novel Approach for Automated Identification of the ...

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International Automatic Testing Conference, AUTOTESTCON

作者： S.M.N.S.K Seneviratne Victoria Penenco Dharshana Kasthurirathna Faculty of Graduate Studies and Research Sri Lanka Institute of Information Technology Sri Lanka UAT London Stock Exchange Group London Dept. of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

User acceptance testing (UAT) is the process that validates the system under test (SUT) behaves according to the business requirements. A User would use the graphical user interface to interact with the underlying code. With the improvement of various devices, end-user testing has become even more complex and time consuming. Every user has a unique interaction pattern. Due to this expected test coverage would not be met by focusing only on the functionality itself. This research is to address the issue of UAT test automation coverage and reduce defect leakage in production environments. The proposed automation solution is to identify more defects in the UAT static testing phase and make the system under test more user friendly before handing the product to the consumers. The proposed solution use Machine learning approach to identify user behaviors that does not match the proposed new functionality. The proposed AD-PU model would evaluate non-prominent user behaviours as per perceived usefulness which would effect the decision to accept the functionality as user acceptable or not.

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Authentic Learning on DevOps Security with Labware: Git Hooks To Facilitate Automated Security Static Analysis

Authentic Learning on DevOps Security with Labware: Git Hook...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Md Mostafizur Rahman MD Abdul Barek Mst Shapna Akter Abm Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Information Technology University of West Florida Florida USA Department of Intelligent Systems and Robotics University of West Florida Florida USA Center for Cyber Security University of West Florida Florida USA Dept. of Computer Science and Software Engineering Auburn University USA Tuskegee University Alabama USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This paper presents an innovative approach to DevOps security education, addressing the dynamic landscape of cybersecurity threats. We propose a student-centered learning methodology by developing comprehensive hands-on learning modules. Specifically, we introduce labware modules designed to automate static security analysis, empowering learners to identify known vulnerabilities efficiently. These modules offer a structured learning experience with pre-lab, hands-on, and post-lab sections, guiding students through DevOps concepts and security challenges. In this paper, we introduce hands-on learning modules that familiarize students with recognizing known security flaws through the application of Git Hooks. Through prac-tical exercises with real-world code examples containing security flaws, students gain proficiency in detecting vulnerabilities using relevant tools. Initial evaluations conducted across educational institutions indicate that these hands-on modules foster student interest in software security and cybersecurity and equip them with practical skills to address DevOps security vulnerabilities.

关键词： Learning systems DevOps Codes Education Static analysis Dynamic scheduling software

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A Comparative Analysis of Machine Learning Classifiers for Robust Heart Disease Prediction 17

A Comparative Analysis of Machine Learning Classifiers for R...

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17th IEEE India Council International Conference, INDICON 2020

作者： Kumar Thakkar, Hiren Shukla, Hrushikesh Patil, Sonali Bennett University Dept. of Computer Science Engineering Greater Noida India Smt. Indira Gandhi College of Engineering Dept. of Computer Science Engineering Mumbai India MindCraft Software Pvt. Ltd Consulting Services Mumbai India

ISBN: (纸本)9781728169163

In the past decade, Cardiovascular Diseases (CVD) have become a significant public health concern across the nations due to the treatment cost. Therefore, the low-cost cardiac health monitoring system is highly essential, which analyzes the collected data and makes robust predictions. The statistical data analysis has limitations to infer the knowledge from several parameters. On the contrary, Machine Learning (ML) models deal with numerous parameters and can successfully extract the patterns. In this paper, ML classifiers are designed, and comparative analysis is carried out for the robust heart disease prediction. Five ML classifiers such as Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) are implemented, and their performance is extensively evaluated on the Cleveland Heart Disease Data set. The comparative analysis across five performance metrics confirms the applicability of ML classifiers for heart disease predictions. © 2020 IEEE.

关键词： Forecasting

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A Knowledge Base Data Mining based on Parkinson's Disease 8

A Knowledge Base Data Mining based on Parkinson's Disease

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8th International Conference on System Modeling and Advancement in Research Trends, SMART 2019

作者： Hassan, Md. Redone Kadir, S.K. Obidul Islam, Md. Aminul Abujar, Sheikh Zannat, Raihana Ohidujjaman, O. Daffodil International University Asulia Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Asulia Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9781728132457

The approaches to detecting Parkinson's disease in the human body from voice data by using Classification techniques apply three different algorithms for finding the growth rate of this disease. Unified Parkinson's disease rating scale deals with motor fluctuations and changes over voice after a certain period and that can measure the people affected by this disease and the difference with healthy people. Hoehn Yahr scale measures the symptoms which are being working through the improvement of Parkinson's disease in the human body. Classifier algorithms used to detect the factors and symptoms which are involved in the advancement of this disease in the human body using voice data. From the distinctions of all algorithms measures the growth rate and find out which algorithm gives the best result for several approaches to diagnosis Parkinson's disease and chances of had this disease in the human body. © 2019 IEEE.

关键词： Decision trees

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