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IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering

作者： Bin Ahmad, Abd. Manan Bin Ag.Nordin, Ag.Noorajis Saaim, Emrul Hamide Md. Bin Samaon, Den Fairol Bin Ibrahim, Mohd Danial Department of Software Engineering Faculty of Computer Science and Information System University of Technology Malaysia

An architecture design of the intelligent agent for speech recognition and translation is presented in this paper. The design involves the agent architecture and the method of the agent is used. The architecture design will show the relationship between the intelligent agent and speech recognition also translation. The intelligent agent for speech recognition is called S - AGENT and T- AGENT for translation. The purpose of the S - AGENT is to facilitate for transmitting the speech data via internet or network. The S - AGENT is acting as a data transmit control to ensure the transmitted speech data will secure delivered The task of the T - AGENT is different from the S - AGENT. The T - AGENT is acting as information retrieval. It will process the output from the speech recognition and translates the output based on its information memory database. If the information cannot be found on its memory, it will search the information required from the database dictionary provided. At the same time, it learns the information and saves the information to its memory for the future purposed © 2004IEEE.

关键词： Speech recognition

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An expert system for diagnosing plant diseases using certainty factor and backward chaining based on android 2

An expert system for diagnosing plant diseases using certain...

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2nd International Conference on Mechanical, Electronics, computer, and Industrial technology, MECnIT 2018

作者： Tambunan, Angel Mariana Siringoringo, Sari Ratnawati Aruan, Rani Aisyah, Putri Intan Sitanggang, Delima Department of Information System Faculty of Technology and Computer Sciences Universitas Prima Indonesia Indonesia

Rice is a producer of basic human needs that have a very important role. Every use of new seeds can cause disease in new plants and often the results obtained are not optimal because they do not know more precisely the type of disease that attacks the rice plants. Most rice farmers still use manual methods to find out the existence of the disease in the rice plants, namely by seeing it directly in the rice fields so that often rice farmers are too late to know these symptoms due to the lack of more knowledge and understanding of rice plants. To overcome the problems that occur it takes a system that has the knowledge like an expert. In this study, an expert system with an Android-based design was designed to make it easier for farmers to access information and can be used as a tool to diagnosediseases in rice plants so that they can be accessed wherever and whenever to overcome the problems faced by farmers. In this system applying the backward chaining method as the basis of the rules and the certainty factor method as weighting, so that by using the method that has been applied in testing various types of diseases and symptoms, the diagnosis of the type of blast disease is 68,83%. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Expert systems

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Handover procedure using probability based mobility patterns in 3G-WLAN interworking environment

Handover procedure using probability based mobility patterns...

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2012 International Conference on Recent Advances in Computing and Software systems, RACSS 2012

作者： Gupta, Vishal Swaroop, K Shanthi Department of Computer Science and Information System Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9781467302548

With the projected increase of 3G network traffic in near future, telecom operators are looking for the alternative means of satisfying the data needs of mobile subscribers without scaling the existing 3G network infrastructure. Over the last decade, IEEE 802.11 network (better called as Wi-Fi network) has emerged as one of the most competitive technology for it. It can function with the macro cellular network, as an adjunct network, to provide an alternative path with the best performance capability. On the other hand, several studies have reported the discovery of fundamental statistical features of human mobility. Studies show that human trajectories show a high degree of temporal and spatial regularity, thus following simple reproducible patterns. This paper proposes a probability based approach to find the mobility patterns of users and then use the information to effectively decrease the handover latency and increase throughput. © 2012 IEEE.

关键词： Wi-Fi

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Smart Career Guidance system 4

Smart Career Guidance System

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4th IEEE International Conference on Computing and information Sciences, ICCIS 2021

作者： Kamal, Areej Naushad, Batul Rafiq, Hadia Tahzeeb, Shahab NED University of Engineering and Technology Department of Computer and Information System Engineering Karachi Pakistan

ISBN: (纸本)9781665494410

We have developed a career guidance system that helps those students who are about to begin their higher education. Most of the time, students are not aware of what career path to follow or which academic major is in accordance with their interests. The system analyzes students' skills, abilities, and interests and recommends the five fields which are most suitable for them. This project helps students identify a specific domain that fits their skills and interests. Smart Career Guidance system is a web-based application built on the Django framework. We have deployed various Machine Learning techniques and algorithms to mimic a one-on-one meeting with an experienced career counselor. The data was collected in the form of a questionnaire that was based on Holland Occupational Themes and the Theory of Multiple Intelligences. A total of 392 graduates completed this online survey. SMOTE oversampling is used to evaluate the machine learning classifiers since the data is highly imbalanced. We tested XGBoost and Random Forest classifiers for recommending the best-suited career options which furnish AUC-ROC performance scores of 0.9952 and 0.9963 respectively. A fine-tuned version of the Random Forest Classifier has successfully attained an AUC-ROC performance score of 0.9976 which indicates the minimal false-positive rate. Ms. Areej Kamal, Ms. Hadia Rafiq and Ms. Batul Naushad have collaboratively conducted all activities of the project including data collection and cleaning, literature review, testing of ML models and development of the final solution. Mr. Shahab Tahzeeb directed and supervised all phases of the project with his immense knowledge and expertise. © 2021 IEEE.

关键词： Students

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Package Placement Application Based on Location Tracking on Android Platform 2

Package Placement Application Based on Location Tracking on ...

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2nd International Conference on Mechanical, Electronics, computer, and Industrial technology, MECnIT 2018

作者： El Islamy, Ibnu Reza Denzy Marsela, Riko Filanno, Rio Sitanggang, Delima Pasaribu, Hendra Turnip, Mardi Department of Information System Faculty of Technology and Computer Sciences Universitas Prima Indonesia Indonesia

JNE has a pickup service program to pick up a package of loyal online shop customers. This system still has limitations in terms of interaction with customers and JNE customer service. This causes less than the maximum number of customers or partners to get better service. Based on these problems starting with the manufacture of input-output design with the tracking location method will facilitate customers in the package pick-up process. This research will produce a package pickup application on the Android platform that is created using the Java programming language and uses the MySQL database so that JNE can do its business activities in a shorter and timely manner. With the use of this application can cause process time savings reaching package pick-up more efficiently, this application can be a better substitute for the previous system. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Pickups

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A Comparative Study of Transformers Embeddings for Question Answering in Arabic Private Documents 8

A Comparative Study of Transformers Embeddings for Question ...

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Al Daoud, Essam Samara, Ghassan Al Daoud, Laith Al Jaidi, Mohammad Zarqa University Computer Science Department Jordan Jordan University of Science and Technology Computer Information System Department Jordan

ISBN: (纸本)9798350373615

Question answering (QA) tasks in natural language processing (NLP) are tricky, particularly when used with Arabic private documents. This is due to the complexity of Arabic language and the lack of sufficient annotated datasets for Arabic QA. Recent research findings have demonstrated the effectiveness of transformer embeddings for English QA. Their efficiency for QA in Arabic private documents, however, has not been fully investigated. In this study, various transformer embeddings for QA in Arabic private documents are compared. The effectiveness of these embeddings is evaluated by assessing a golden dataset extracted from private documents. The results show that the accuracy of QA in Arabic private documents can be significantly improved by combining transformer embeddings with count vectorization. Furthermore, different transformer embeddings have distinct strengths and weaknesses. © 2023 IEEE.

关键词： Embeddings

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Advancing agriculture with machine learning:a new frontier in weed management

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Frontiers of Agricultural Science and Engineering 2025年第02期 288-307页

作者： Mohammad MEHDIZADEH Duraid K.A.AL-TAEY Anahita OMIDI Aljanabi Hadi Yasir ABBOOD Shavan ASKAR Soxibjon TOPILDIYEV Harikumar PALLATHADKA Renas Rajab ASAAD Department of Agronomy and Plant Breeding Faculty of Agriculture and Natural Resources University of Mohaghegh Ardabili Ilam Science and Technology Park Department of Horticulture College of Agriculture University of Al-Qasim Green Department of GIS and Remote Sensing Faculty of Geography University of Tehran Medical Laboratories Techniques Department Al-Mustaqbal University College Erbil polytechnic university Technical engineering college information system engineering department Department of Fundamental Economics Tashkent State University of Economics Manipur International University Department of Computer Science Nawroz University

Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods are often time-consuming, labor-intensive, and pose environmental *** have been the primary method of weed control due to their efficiency and cost-effectiveness. However, over-reliance on herbicides has led to environmental contamination, weed resistance, and potential health hazards. To address these issues, researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies. As technology advances, there is a growing interest in exploring innovative and sustainable weed management approaches. This review examines the potential of machine learning in chemical weed *** learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate *** learning models can classify weed species and optimize herbicide usage. Real-time monitoring enables timely intervention, preventing invasive species spread. Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices, reducing herbicide usage and minimizing environmental impact. Validation and refinement of these algorithms are needed for practical application.

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MV-DUO: multi-variate discrete unified optimization for psychological vital assessments

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Neural Computing and Applications 2024年第31期36卷 19777-19793页

作者： Pethuraj, Mohamed Shakeel Burhanuddin, M.A. Dzakiyullah, Nur Rachman Department of Information System Faculty of Computer and Engineering Universitas Alma Ata Yogyakarta Indonesia Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka Malaysia

Psychological vital assessments are required for monitoring health conditions and observing body reactions toward diseases and medications. Wearable sensors play a vital role in sensing body vitals and presenting them as signals for computer-based analysis. The problem relies on the signal decoding due to its input stream that turns out to be discrete/ continuous. Therefore for addressing the above specific issue, this article introduces MV-DUO (Multi-Variate Discrete Unified Optimization) method. This method addresses the above problem from a multi-variate perspective by sensing differential signals across healthy and unhealthy conditions. The healthy and unhealthy conditions are trained using neural learning by augmenting/ ceasing external vital data. The unification is performed using single-point artificial ecosystem-based optimization for identifying discrete sequences collaborated with continuous signals. The single-point reference is grouped based on the maximum continuity fitness observed under various sensing intervals. In this process, the non-grouped sequences are identified as unhealthy or discrete for which additional detection training and classification are required. Considerably the changes between successive sensing intervals are used for variations detection from unified high-fitness groups. Those grouped instances are used for training new vital changes observed at distinct intervals. This improves detection accuracy under controlled errors. For the varying sensing intervals, the proposed method achieves 14.13% high accuracy, 8.29% high grouping rate, 10.77% less error, and 10.07% less detection time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： computer aided analysis

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A Unified Measurement Solution of Software Trustworthiness Based on Social-to-Software Framework

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Journal of computer Science & technology 2018年第3期33卷 603-620页

作者： Xi Yang Gul Jabeen Ping Luo Xiao-Ling Zhu Mei-Hua Liu Key Laboratory for Information System Security School of Software Tsinghua University Beijing 100084 China School of Computer and Information Hefei University of Technology Hefei 230009 China Department of Foreign Languages and Literatures Tsinghua University Beijing 100084 China

As trust becomes increasingly important in software domain, software trustworthiness--as a complex high- composite concept, has developed into a big challenge people have to face, especially in the current open, dynamic and ever-changing Internet environment. Furthermore, how to recognize and define trust problem from its nature and how to measure software trustworthiness correctly and effectively play a key role in improving users＇ trust in choosing software. Based on trust theory in the field of humanities and sociology, this paper proposes a measurable S2S （Social-to-Software） software trustworthiness framework, introduces a generalized indicator loss to unify three parts of trustworthiness result, and presents a whole metric solution for software trustworthiness, including the advanced J-M model based on power function and time-loss rate for ability trustworthiness measurement, the fuzzy comprehensive evaluation advanced-model considering effect of multiple short boards for basic standard trustworthiness, and the identity trustworthiness measurement method based on the code homology detecting tools. Finally, it provides a case study to verify that the solution is applicable and effective.

关键词： software trustworthiness measurement solution loss social-to-software（S2S）framework generalized indicator

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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