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Measures of Organizational Process Performance in the Capability Maturity Model Integration 24

Measures of Organizational Process Performance in the Capabi...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Al-Ghuwairi, Abdel-Rahman Al-Fraihat, Dimah Sharrab, Yousef Kittaneh, Ahmed Ali, Ahmed Alsarhan, Ayoub Alkhalidy, Muhsen The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Software Engineering Jordan Isra University Faculty of Information Technology Dept. of Software Engineering Jordan Isra University Faculty of Information Technology Dept. of Artificial Intelligence and Data Science Jordan The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Cyber Security Jordan The Hashemite University Faculty of Prince Al-Hussein Bin Abdallah Ii for Information Technology Dept. of Information Technology Jordan

ISBN: (纸本)9798350384307

The Capability Maturity Model Integration (CMMI) is widely utilized by organizations to facilitate process improvement. Among its 22 process areas, this paper focuses on the Organizational Process Performance (OPP) area, which assesses the effectiveness of a process. Specifically, this study examines one goal (establishing performance baselines and models) and two practices (establishing quality and process performance objectives and selecting processes within OPP. To measure these practices, the Goal Questions Metrics (GQM) approach is employed. data was collected using a questionnaire, and the defined measures are validated quantitatively. 250 responses were used for analysis. The results obtained indicate that implementing the defined measures in companies would be beneficial for achieving high-quality processes in OPP and advancing the maturity level of CMMI. © 2023 IEEE.

关键词： Goal Questions Metrics (GQM) Software Quality, Organizational Process Performance, Capability Maturity Model Integration, CMMI

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Simplified Micropayment Mechanism to Eliminate the Risk of Double Payment in E-Commerce

Simplified Micropayment Mechanism to Eliminate the Risk of D...

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Advances in Intelligent Computing and Applications (AICAPS), International Conference on

作者： Narendra Kumar Chahar Krishan Pal Singh Muzzammil Hussain Dept. of Data Science and Analytics Central University of Rajasthan Ajmer India Dept. of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India Dept. of Computer Science and Engineering Central University of Rajasthan Ajmer India

In recent years, the emergence of the Internet and E-commerce has steered significant growth in digital transactions. Businesses today need mobile wallets, credit and debit cards, and e-cash to digitize payments. Digital payment systems are in transition and promise amazing advancements, but they also pose many risks and as the number of online transactions is increasing tremendously, we need a security system that follows all security norms. In this paper, we study digital transaction systems and evaluate various components of E-commerce plat-forms to address the security of these services. We evaluate the attributes that affect the security of digital payment processes and identify several barriers that hinder their performance to propose a simplified payment mechanism for micro-payments that eliminates the double payment problem.

关键词： Micropayments Internet Security Electronic commerce Business

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An Extensive Analysis of Machine Learning and Deep Learning Based Banana Leaf Disease Detection Techniques 10

An Extensive Analysis of Machine Learning and Deep Learning ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Anitha, P. Venkata Ratnam, K. Rayudu, K.M. Naidu, Kamparapu V.V. Satya Trinadh Lalitha, B. Bhaskar, P. Dept.of Cse Andhra Pradesh Ongole India Dept.of Cse Andhra Pradesh India Dept. of Data Science Ongole India Andhra Pradesh Madanapalle517325 India Jntua College of Engineering Head of Cse Chittoor District Andhra Pradesh Kalikiri India Dept.of Cse Andhra Pradesh Ongole India

ISBN: (纸本)9798350384369

Infection of plants is a major limitation and a difficult effort. Diseases affecting plants have the potential to harm fruit and foliage, causing the agriculture sector to suffer large financial losses. Infections of plants are primarily evident on the foliage and stems of the plants. Disease categorization and identification by hand takes a lot of effort and requires the participation of professionals. But quickly, this procedure may be aided by the installation of a system that is computerized. An autonomous maladies identification and categorization system is presented by many existing researchers, which can aid those who cultivate banana crops and make a contribution to the nation's economy thanks to advancements in Machine Learning (ML) and Deep Learning (DL) strategies. The principal objective of this work is to conduct a thorough examination of existing works that have used ML and DL approaches in the agricultural sector, specifically concerning the development of banana plants. As a result, it can be useful for upcoming investigators to determine the caliber and nature of prior studies. The researchers looked at issues about banana plantations. Additionally, the researchers have examined the ML-based models that have been put into use, the data-gathering resources, and the thorough outcomes attained for every study. © 2024 IEEE.

关键词： Fruits

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Navigation Assistance for the Visually Impaired: An Image Segmentation-based Web Application

Navigation Assistance for the Visually Impaired: An Image Se...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Sejal D'Mello Vivek Prajapati Hirenkumar Vyas Naveen Arora Arpit Mishra Dept. of Artificial Intelligence and Data Science Vidyavardhini's College of Engineering and Technology Mumbai India

The objective of this paper is to create a mobile app that can aid visually impaired people in navigating outdoor surroundings through floor segmentation technique. The app uses phone's camera to capture live video of the user's environment, which will be analyzed to identify and segment the floor area. As a result, the user will be able to obtain audio information regarding their location and distance from nearby objects or obstacles. This proposed system is a practical and low-cost solution that can be effortlessly incorporated into existing smartphone technology, providing an easy-to-use and accessible tool for the visually impaired to navigate through outdoor environments.

关键词： Image segmentation Visualization Navigation Face recognition Visual impairment Cameras Mobile applications

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Android Malware Detection

Android Malware Detection

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Communication & Computing (WCONF), World Conference on

作者： N Gagan S Sai Kumar M Keerthana Samarth S Vaidya Vanshika Rastogi Dept. Artificial Intelligence and Data Science (of East Point College of Engineering and Technology) Bangalore India

Since android smartphones are so popular, malware writers have found them to be lucrative targets, which poses a serious risk to user security and privacy. To address this problem, several malware detection strategies have been put out recently. In this review paper, we examine an efficient method for detecting malware on Android that makes use of machine learning techniques. Our method divides Android applications into benign and harmful categories using a combination of feature extraction methods and machine learning algorithms. Overall, our suggested method offers a dependable and efficient alternative for detecting Android malware, giving consumers a more dependable and safe environment for mobile computing.

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Simulation of User Arrangement in Virtual Reality for Design Metaverse Network : Focus On Statistical Distribution 14

Simulation of User Arrangement in Virtual Reality for Design...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Oh, Sangwon Jeong, Il-Kwon Aliyu, Ibrahim Yu, Cho-Rong Um, Tai-Won Kim, Jinsul Chonnam National University Dept. of Ict Convergence System Engineering Gwangju Korea Republic of Electronics and Telecommunications Research Institute Daejeon Korea Republic of Chonnam National University Graduate School of Data Science Gwangju Korea Republic of

ISBN: (纸本)9798350313277

Several external factors, including COVID-19, have prompted a trend towards converting existing systems and services into non-contact alternatives. Furthermore, beyond non-face-to-face services for individuals and small groups, such as phone calls and small meetings, efforts are underway to transform non-face-to-face services for large groups, such as classes and concerts. In particular, current virtual reality platforms like metaverse are insufficient to provide non-face-to-face services to large groups, guaranteeing Quality of Service (QoS) for users. Calculating the expected number of users for such services is vital to prepare and design the network infrastructure and resources accordingly. This paper proposes a technique to generate and arrange virtual users who can simulate a real-world environment to design a metaverse network that can accommodate a large number of people. Various large-scale service arrangement techniques are outlined, utilising statistical distributions. Based on the arrangement of users in the XR service, we proposed ways to choose the appropriate servers and channels during the XDN design phase, along with the use of XDN's microservices. © 2023 IEEE.

关键词： metaverse simulation statistical distribution user distribution virtual reality

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Clear Text Visibility: A CNN-LSTM Approach for Frame of Interest Identification in Book Flipping Videos

Clear Text Visibility: A CNN-LSTM Approach for Frame of Inte...

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Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Gaurav Buddhawar Dharin Dave Krupa Jariwala Chiranjoy Chattopadhyay Dept. of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Surat India Dept. of Computer Science and Engineering (AI & ML) Pimpri Chinchwad College of Engineering Pune India School of Computing and Data Sciences FLAME University Pune India

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

Book flipping videos present a distinctive challenge for information extraction, requiring the identification of frames with clear text visibility during dynamic page turns. This paper introduces a novel approach to frame classification in book flipping videos, leveraging the combined effect of Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs). In particular, a CNN-LSTM model is employed, where a pre-trained CNN captures spatial features, and LSTMs extend the model’s capability to distinguish temporal dependencies critical for page turn detection. Unlike conventional methods, this approach processes entire video sequences, enabling the model to learn intricate Frame of Interest (FoI) detection features indicative of page turns and text visibility. The proposed model offers improved interpretability, effectively classifying frames without compromising transparency. The research opens avenues for automated book digitization, digital library creation, and educational technology applications, enhancing information extraction and reliability from book flipping videos.

关键词： Digital transformation Video sequences Educational technology Information retrieval Feature extraction Libraries Convolutional neural networks Reliability Long short term memory Videos

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Artificial Intelligence (AI) Enabled Music Player System for User Facial Recognition

Artificial Intelligence (AI) Enabled Music Player System for...

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Emerging Technology (INCET), International Conference for

作者： A. Rehash Rushmi Pavitra K Anushree A V R Akshayalakshmi K Vijayalakshmi Dept. of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai TamilNadu Department of Artificial Intelligence & Data Science Sri Sairam Engineering College Chennai TamilNadu

Artificial Intelligence (AI) refers to the use of advanced computational algorithms and technologies to replicate or simulate human intelligence in machines or computer systems. Human expression is incredibly important in determining a person’s current condition and mood. AI is utilized to analyze human facial expressions and extract emotions based on features such as cheeks, forehead, eyes, and smiles. In addition, “songs” refers to an expressive medium that has always been the best option for analyzing and comprehending human emotions. The proposed ’smart music player' is a programmed system that functions under the assumption that we can tell someone’s mood by the expression on his/her face. This model that recognizes facial micro-expressions with multicultural facial expression details, recommends music in accordance with corresponding mood. It is developed using a combination of song’s features and micro-expression recognition technology of convolutional neural network. To do this, group facial expressions into seven distinct emotional groups, including happy, sad, angry, neutral, surprise and disgust. The primary objective of this study paper is to present an overview of a useful music player and social companion that automatically creates a playlist that will brighten your day based on your emotional condition, along with recommendations for future studies in the field of recommendation systems.

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Phoenix: IoT Trust Evaluation Using Game Theory with Second Chance Protocol

Phoenix: IoT Trust Evaluation Using Game Theory with Second ...

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International Conference on Internet of Things: Systems, Management and Security

作者： Himan Namdari Cihan Tunc Ram Dantu Dept. of Data Science Worcester Polytechnic Institute Worcester MA USA Dept. of Computer Science & Engineering University of North Texas Denton TX USA

Internet of Things (IoT) devices are a big part of our daily lives for their use in smart environments and open fields. However, IoT devices, characterized by limited computational capabilities and energy constraints, are prone to malfunctions and cyberattacks. This vulnerability raises substantial trust issues, particularly concerning the reliability of the data they provide. This paper proposes a hierarchical trust evaluation methodology by incorporating game theory and Bayesian inference designed for IoT devices to address the trust evaluation issue. We also explored the impact of historical behavior, such as nodes’ age and trust ratio improving our trust evaluation results. Furthermore, we proposed a technique named "second chance protocol" to identify a transitory malfunctioning node and introduce it back to the network once it regains trustworthiness, resulting in resource optimization and enhancing average trust in the model. We generated synthetic data to evaluate the proposed approaches with randomly distributed noise levels simulating failures or compromised IoT devices. The results of our experimental analysis demonstrate that our approach improves the overall trust ratio of the system concerning traditional approaches. The proposed second chance protocol further enhances the trust average and device utilization by accurately identifying malfunctioning nodes. Our result with different noise levels achieves five to ten times better than an alternative study. Finally, our model achieved a detection rate of 97.99%, 89%, and 79% for 10%, 20%, and 30% noise levels.

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Machine Learning Algorithms for Automated Synthesis of Biocompatible Nanomaterials

Machine Learning Algorithms for Automated Synthesis of Bioco...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Chakravarthi, M Ayyappa Sharma, Sonu Sahu, Amit Murthy, Tanuja Arulananth, T.S. Jain, Smriti KKR & KSR Institute of Technology and Sciences Dept. of CSE - Data Science AP Guntur India IES College of Technology Department of Electronics & Communication Engineering M.P Bhopal462044 India GGITS Cyber Security & Blockchain Dept of IOT M.P Jabalpur India Dayananda Sagar College of Engineering Department of EEE Karnataka Bangalore India MLR Institute of Technology Dept. of ECE Telangana Hyderabad India Indore Institute of Science & Technology Department of CSE M.P Indore India

ISBN: (纸本)9798331539948

This study introduces a novel approach, the 'Machine Learning Algorithms for Automated Synthesis of Biocompatible Nanomaterials,' aimed at revolutionizing the synthesis processes in nanotechnology. The proposed method leverages three powerful machine learning algorithms - Random Forest, Bayesian Optimization, and Deep Neural Networks - to optimize reaction conditions and predict the properties of biocompatible nanomaterials. In the pursuit of automated synthesis, the Random Forest algorithm acts as an ensemble learning technique, combining multiple decision trees to make robust predictions. Bayesian Optimization, a sequential model-based optimization approach, efficiently navigates the parameter space to identify optimal synthesis conditions. Deep Neural Networks, capable of capturing intricate relationships within data, are employed to model complex patterns in the synthesis process. To validate the effectiveness of the proposed method, we conducted comprehensive experiments and compared its performance with six traditional methods: Linear Regression, Polynomial Regression, Support Vector Machine, Decision Tree, K-Nearest Neighbors, and Principal Component Regression. Key performance metrics, including Mean Squared Error (MSE), R-squared (R2), and Mean Absolute Error (MAE), were employed for the evaluation. Our results demonstrate that the proposed method consistently outperforms traditional methods across all metrics. It exhibits lower MSE and MAE values and a higher R2 value, indicating superior predictive accuracy. © 2024 IEEE.

关键词： Nanoclay

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