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Person identification based on voice biometric using deep neural network

International Journal of information technology (Singapore) 2023年第2期15卷 789-795页

作者： AL-Shakarchy, Noor D. Obayes, Hadab Khalid Abdullah, Zahraa Najm Department of Computer Science Faculty of Computer Science and Information Technology Kerbala University Kerbala Iraq College of Education for Humanities Studies University of Babylon Babylon Iraq

Nowadays in all everyday transactions, technological progress has become an intrinsic characteristic that depends on such electronic applications as financial and banking transfers, health care, project management, and other crucial life aspects. The core of these applications is person Identification and/or verification steps which can be considered one of the complicated limitations. Accordingly, the employment of biometric attributes can yield promising outcomes in these fields. A One’s voice is a unique bio-feature whereby people can be authenticated and precludes others from assuming a one’s identity without their previous knowing or assent. This work proposes a model with a new architecture to identify the person by exploiting the unique individual characteristics available in one’s voice based on deep learning. An augmentation method is utilized to increase the samples in the available dataset. The available temporal information at an input audio file is analysed then feature maps from this information are extracted which represent the salient temporal feature (time-domain features). The decision is made based on tracking these voice features over time. Successful and promising results are achieved through this work, the accuracy is close to 99.81% (± 1.78%) and the values of loss function are close to 0.009 over VoxCeleb1 dataset for identifying 40 subjects. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Batch normalization layer (BN) One Dimensional Convolutional Neural Networks (1DCNN) Person identification PI Pooling layer (Pool) Rectified linear units (ReLU) Voice biometric

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Continuous and Discrete Similarity Coefficient for Identifying Essential Proteins Using Gene Expression Data

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Big Data Mining and Analytics 2023年第2期6卷 185-200页

作者： Jiancheng Zhong Zuohang Qu Ying Zhong Chao Tang Yi Pan College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences ShenzhenGuangzhou 518055China

Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential ***,gene expression data are prone to significant fluctuations due to noise interference in topological *** this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise *** then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression *** the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential *** strategy exhibited a high recognition rate and good *** validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy *** with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and *** also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene *** experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.

关键词： Protein-Protein Interaction(PPI)network continuous and discrete similarity coefficient essential proteins

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IoT-based Heartbeats Monitoring System 9

IoT-based Heartbeats Monitoring System

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Tamron, Mohamad Asyraf Noorza Bin Mohd Attarbashi, Zainab S. Abuzaraida, Mustafa Ali Atitallah, Nesrine Iftikhar, Saman Handayani, Dini Oktarina Dwi Basri, Atikah Balqis Binti International Islamic University Malaysia Kulliyyah of Information & Communication Technology Selangor Malaysia International Islamic University Malaysia Faculty of Information & Communication Technology Department of Computer Science Selangor Malaysia Misurata University Faculty of Information Technology Computer Science Department Libya Arab Open University Faculty of Computer Studies Saudi Arabia International Islamic University Malaysia Faculty of Information and Communication Technology Department of Information Systems Selangor Malaysia

ISBN: (纸本)9798350370126

The advent of the Internet of Things (loT) has opened up a realm of possibilities in developing globally accessible systems and devices. With the current state of internet speeds, these systems offer real-time control, making them more powerful than ever. Simultaneously, the healthcare sector has been witnessing notable advancements, but it also grapples with an increasing number of health issues, with heart problems standing out as a prominent concern in many countries. These problems manifest in various symptoms, some of which can lead to more serious conditions, such as irregular heartbeats. In response to these challenges and leveraging cutting-edge technology, researchers have embarked on endeavors to enhance healthcare devices and render them more readily available to medical professionals. This paper explores the merging of loT technology with healthcare, specifically focusing on the development of a system designed to monitor and record a patient's heartbeat. This is achieved through the deployment of a Heartbeat/SpO2 Sensor, and the data is seamlessly transmitted to a mobile application accessible remotely. The outcomes of this study demonstrate that the heartbeat data captured by Arduino is effectively transmitted to the cloud, meeting the system's predefined objectives. This represents a significant stride in harnessing loT's potential to enhance healthcare practices and patient monitoring. © 2023 IEEE.

关键词： Internet of things

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Intelligent-Based Extraction of Business Capabilities from Company Descriptions 7

Intelligent-Based Extraction of Business Capabilities from C...

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7th International Conference on information technology, information Systems and Electrical Engineering, ICITISEE 2023

作者： Fahmi, Faisal Hamid, Siti Hafizah Ab Yuadi, Imam Airlangga University Faculty of Political and Social Science Department of Library and Information Science Indonesia Universiti Malaya Faculty of Computer Science & Information Technology Department of Software Engineering Malaysia

ISBN: (纸本)9798350382266

In the era of Big Data, information can be generated, extracted, and utilized in diverse ways. In business, information about business capabilities can be a crucial aspect in understanding the strengths and competencies of organizations. This information can empower stakeholders to make informed decisions regarding partnerships, acquisitions, and market positioning. Additionally, by comparing capabilities across companies and industries, organizations can identify their competitive advantages, industry trends, potential disruptions, and effective resource allocations, leading to improved strategies and better market opportunities. Traditional methods for extracting business capabilities primarily rely on keyword matching or rule-based techniques, which often fail to capture the complex relationships between words in a sentence. This paper presents a comprehensive study on extracting business capabilities using dependency parsing techniques in natural language processing (NLP) to overcome limitations of the traditional methods and improve the accuracy of business capability extraction. Dependency parsing utilizes pre-trained language models to analyze the grammatical structure of sentences, identifying the dependencies between words and capturing their relationships. The feasibility and effectiveness of the proposed approach is demonstrated through experiments conducted on more than 40K real-world company descriptions crawled from Wikipedia. © 2023 IEEE.

关键词： Competition

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar Muhammad Rashid Mohammed S.Alshuhri Sheeraz Akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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SQ-ViT: A Multi-Scale Vision Transformer With Quaternion For Endoscopic Images Classification

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 828-838页

作者： Jin, Zhanjun Huang, Guoheng Zhang, Feng Yuan, Xiaochen Zhu, Dingzhou Tan, Zhe Pun, Chi-Man Zhong, Guo Guangdong University of Technology School of Computer Science and Technology Guangdong Guangzhou510000 China Sun Yat-sen University First Affiliated Hospital Department of Nephrology Department of Otorhinolaryngology Guangdong Guangzhou510000 China Macao Polytechnic University Faculty of Applied Sciences 999078 China University of Macao Department of Computer and Information Science 999078 China Guangdong University of Foreign Studies School of Information Science and Technology Guangdong Guangzhou510000 China

In the field of medical consumer electronics, endoscopic imaging technology especially electronic nasopharyngoscope imaging, often suffers from low resolution, which poses a difficulty for endoscopic images classification due to the loss of image details. Recent advancements in Vision Transformer (ViT) based methods have shown promise in addressing this problem. However, ViT relies heavily on global context information to maintain performance, and the limited pixel count in lowresolution images poses a challenge in capturing adequate global context information. To address these challenges, we propose the Sequential Quaternion Vision Transformer (SQ-ViT), which improves multi-scale feature utilization by feeding sampled features into the subsequent encoder layers. Specifically, we introduce the Multi-scale Visual Feature Fusion (MVFF) module, which segments the image into multiple superpixel blocks and refines the contour and color information of the processed image, which helps to enhance the representation of visual features. Additionally, visual information would be captured more effectively by our proposed Quaternion Interactive Encoder (QIE). Experiments demonstrate the effectiveness of SQ-ViT in improving multi-scale feature utilization and addressing challenges in low-resolution endoscopic imaging for endoscopic images classification. The source code will be released at https://***/jinzhanjun625/SQViT. © 1975-2011 IEEE.

关键词： Endoscopy

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Defect Exposure in Vegetables and Fruits using Machine Learning

Defect Exposure in Vegetables and Fruits using Machine Learn...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Spandana, S. Sathiya, M. Krishnan, Kesavan Kiran, Ajmeera MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Karpagam Institute of Technology Department of Information Technology Coimbatore India Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350348217

Defects to agricultural food items like fruits and vegetables, results in decrease of quality and quantity of agricultural consuming items. This directly affects the financial source of agriculturists and the human health. So, exposing to this kind of defects in fruits and vegetables at early stages of developing products leads to minimization in loss of production and quality. The regular or most used day to day approach for defect detection is continuously watching and observation of crop either by farmers or further by some professionals. But, it is very time-consuming method. We have used image processing and classification techniques in machine learning to identify and detect the diseases on agricultural products like vegetables and fruits. The main goal of the system being proposed here is to monitor and detect diseases in fruits and vegetables in their early stages to farmers for a healthy yield. Our System can deliver the most accurate results in a very short time while consuming minimal computational resources. © 2023 IEEE.

关键词： Vegetables

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AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

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Intelligent Automation & Soft Computing 2023年第4期36卷 1095-1104页

作者： Liaqat Ali Saif E.A.Alnawayseh Mohammed Salahat Taher M.Ghazal Mohsen A.A.Tomh Beenu Mago College of Engineering and Technology University of Science and Technology of FujairahFujairahUAE Electrical Engineering Department Faculty of EngineeringMutah UniversityJordan School of Information Technology Skyline University CollegeUniversity City Sharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600Malaysia Faculty of Computer Science NCBA&ELahore54660Pakistan School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE

The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent *** an outbreak occurs,each country must have a well-coordinated and preventative plan to address the *** and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily *** intelligent devices and applica-tions have become a vital part of our everyday lives,smart gadgets have also led to several physical and psychological health problems in modern ***,we used an artiﬁcial intelligence AI-based system for disease prediction using an Artiﬁcial Neural Network(ANN).The ANN improved the regularization of the classiﬁcation model,hence increasing its *** unconstrained opti-mization model reduced the classiﬁer’s cost function to obtain the lowest possible *** verify the performance of the intelligent system,we compared the out-comes of the suggested scheme with the results of previously proposed *** proposed intelligent system achieved an accuracy of 0.89,and the miss rate 0.11 was higher than in previously proposed models.

关键词： Intelligent model epidemics artiﬁcial intelligence machine learning techniques

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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