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Lip Detection and Recognition-A Review1

Lip Detection and Recognition-A Review1

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Networking and Communications (ICNWC), international conference on

作者： Saloni Sharma D. Malathi Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur India

It’s no secret that security systems rely heavily on image processing because of its versatility. Two-dimensional visuals, intricate algorithms, and instantaneous decision-making are all challenges that must be met by the system. It is possible to optimize the system at one of four stages: preprocessing, feature extraction, Lip detection, and Recognition. Using modern computing hardware and software, we can create a system that is both easy to use and exactly what we need. Unfortunately, as more characteristics are added, the complexity of implementing these algorithms grows. The process is improved through the development of novel approaches, tools, and strategies. machine learning and AI techniques have recently been applied to image processing applications. Standard methods of authentication, such as passwords and PINs, are becoming increasingly insecure. Physical and biological characteristics that are unique to each individual provide the best level of security. It is vulnerable to guessing and theft in business and public computer networks. Plastic cards, smart cards, and computer token cards all have non-security flaws in the form of forgery, loss, corruption, and inaccessibility. Identifying techniques based on biometrics have several applications in forensics, finance, and other fields. Voluntary action from the past has the drawbacks of being difficult to implement and not adaptable for covert uses, such as in surveillance applications. Lip image audit and verification during biometrics record keeping is prone to human error. Image quality of the lips is more easily obtained than fingerprint images. Only about five percent of the population has imperfect fingerprints and cannot be verified. Reasons include but are not limited to dry skin, diseased skin, elderly skin, wounded skin, calloused finger, oriental skin, bandaged finger, narrow finger, smeared sensor on reader, etc. Varying lighting conditions are widely recognized as one of the most crucial aspec

关键词： Visualization Lips software algorithms Fingers Lighting Fingerprint recognition Feature extraction

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Simulation of Mine Subsidence Estimation Comparison Model Based on Improved Genetic Algorithm

Simulation of Mine Subsidence Estimation Comparison Model Ba...

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Telecommunications, Electronics and Informatics (ICTEI), international conference on

作者： Qingtian Zeng Yinhong Wei Bian Li Hongwei Lou Zujiang Guo Yunnan Diqing Nonferrous Metals Co. Ltd Shangri-La Yunnan China Changsha Dimai Digital Technology Co. Ltd Changsha Hunan China

ISBN: (数字)9798350342963

ISBN: (纸本)9798350342970

In the stage of mining, after the ore is mined, the original stress equilibrium state of rock mass around the mining area is destroyed and the stress is redistributed, thus causing continuous movement, deformation and discontinuous destruction of rock stratum and surface, that is, mining subsidence. In this article, a mine subsidence estimation model based on improved genetic algorithm (GA) is proposed, and a new system for predicting surface subsidence and deformation in horizontal coal seam mining is established, which provides a new method for predicting surface subsidence and deformation in mine mining. This method takes the normalized mine monitoring parameters as the input data of the limit training machine, takes the normalized maximum subsidence as its target data, and trains with different kernel functions and different numbers of neurons in hidden layers to get the optimal estimation results. The simulation results show that the algorithm has the advantages of simple mathematical model, fast training and learning speed, global optimal solution and strong adaptability to new samples. It has been widely used in solving engineering problems and achieved good results.

关键词： Deformable models Training Deformation software algorithms Estimation Predictive models Rocks

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Few-shot learning Approaches to software Requirement Quality Prediction 23

Few-shot Learning Approaches to Software Requirement Quality...

引用

Proceedings of the 33rd Annual international conference on Computer Science and software engineering

作者： Yildirim Savas Cevik Mucahit Başar Ayşe Toronto Metropolitan University Toronto Ontario Canada

Computer-aided processes can be used to improve the quality of requirements in software projects. These processes, in principle, can be divided into two stages: requirement quality evaluation, essentially a classification task, and requirement correction sugges-tions, fundamentally a language generation problem. This study particularly focuses on the initial phase –requirement quality evalu-ation, for which we develop a machine learning infrastructure. Our approach aligns itself with the universally recognized assumption that ideal requirements should be uncomplicated, precise, atomic, and succinct. We have a two-step solution to evaluate the qual-ity of requirements. The first s tep e mploys f ew-shot techniques, addressing the issue of limited access to labeled data in practi-cal contexts. The second step involves adapting a language model with domain-specific data, given that language models trained on general-purpose texts might struggle to accurately capture patterns and meanings in specific d omains. O ur p ipeline p rimarily lever-ages contrastive learning objectives in both steps of our approach. Furthermore, we consider various robust baselines for compara-tive analysis. Numerical results illustrate that our quality detection model effectively discriminates between good quality and bad qual-ity requirements, thereby providing valuable input for subsequent requirement generation and suggestion models.

关键词： Few-shot learning Contrastive learning Domain Adaption software Requirement Specifications Transformer Models

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CNN EYE-an outlook for the visually impaired

CNN EYE-an outlook for the visually impaired

引用

international conference on Computer Vision and Internet of Things 2023 (ICCVIoT'23)

作者： P. Kiran K. S. Sampada S. Hemanth N. Anjan Kumar Computer science and engineering RNS Institute of Technology VTU Bangalore India

In an era of advancing technology, there remains a pressing need to address the challenges faced by visually impaired individuals, particularly in the realm of object recognition. This paper presents a comprehensive model aimed at developing an innovative and cost-effective solution for fruit and vegetable recognition, catering specifically to the visually impaired community. The proposed system integrates computer vision technology, leveraging the YOLOv5 algorithm, to enable real-time detection and identification of various fruits. The paper begins with a problem statement highlighting the difficulties faced by visually impaired individuals in identifying objects accurately. It introduces the concept of using machine learning, particularly convolutional neural networks (CNNs), for object recognition and highlights the significance of real-time detection. The YOLOv5 algorithm is chosen as the core technology due to its efficiency and ability to operate locally without relying on cloud resources. The proposed architecture is described, emphasizing the use of YOLOv5 as the one-stage object detection model. The paper outlines the steps to obtain results, starting with the installation of necessary dependencies, dataset preparation, and model training. Additionally, the hardware and software requirements are detailed, including the use of Raspberry Pi for real-time processing and a speaker for auditory feedback. The results indicate practical application of the YOLOv5 model, providing snapshots of the system's ability to detect and identify fruits, such as apples. These visual outputs are coupled with voice-based feedback for user accessibility. The proposed model underlines the affordability and accessibility, making it a promising tool to empower the visually impaired community.

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Digitization and Digitalization of Cost and Duration Estimation in Philippine Public School Building Projects Using Artificial Neural Network

Digitization and Digitalization of Cost and Duration Estimat...

引用

international conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Ermengard S. Aguinaldo Dante L. Silva School of Graduate Studies Mapúa University Manila City Philippines School of Civil Environmental and Geological Engineering Mapúa University Manila City Philippines

Digital technology and innovation integration in the construction sector have demonstrated their effectiveness in boosting workplace productivity. The construction business has benefited from the use of computer-aided design (CAD) software, which significantly decreased project cost and length while maximizing efficiency. Estimating cost and duration (CD) during the pre-construction phase of a project is critical due to the limited available data during this phase. Utilization of machine learning (ML) methods, such as artificial neural network (ANN), are now evident to solve complex problems even with minimal data available. At the pre-construction stage of a construction project, this study created a two-phase ANN model that can forecast the project cost and duration for public school buildings in the Philippines based on the data provided. The ANN model was initially composed of multiple sub-models that will form a final comprehensive model, wherein the output of the sub-models will become the input of the final model. The sub-models were based on the determined Work Breakdown Structure (WBS) of the project, namely earthworks, structural, finishing, plumbing and sanitary, electrical, and mechanical. Based on the excellent performance of the input parameters (IP), its structure gave a completer and more understandable ANN model. The contribution of the input parameters available at the preconstruction stage of a construction project to the overall project cost and overall project duration was also determined using sensitivity analysis (SA).

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Intelligent Operational Decision Support System for Sugar Factory Resource Efficiency

Intelligent Operational Decision Support System for Sugar Fa...

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international conference on Modern Electrical and Energy Systems (MEES)

作者： Nataliia Lutska Nataliia Zaiets Lidiia Vlasenko Stefan Junge Dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine Dept. of Packaging Technology Berlin University of Applied Sciences Berlin Germany

With the economic crisis that is looming over the world due to changes in the energy sector and a reduction in the supply of certain types of products, energy and resource efficiency issues are becoming more relevant for the food industry. Also important is the preservation of the environment and the implementation of Cleaner Production trends. Most enterprises follow the concept of Industry 4.0 and increase efficiency through the implementation of intelligent methods, in particular machine learning. The paper proposes a neural network model of sugar production. The model makes it possible to predict the main estimates of resource efficiency used to correct the technological modes of production by the operator-technologist. The structure of the operational decision support system has been developed and is used by personnel to model and predict changes in resource efficiency estimates and KPIs calculated on their basis. Its feature is the integration of methods for achieving resource efficiency, improving environmental performance and ensuring KPIs. To do this, at the initial stage, an analysis of the main flows that affect resource efficiency and an assessment of their importance was carried out. Ensuring Production Performance is confirmed by the simulation and the adequacy of the created neural network accuracy of more than 95%.

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Examining the Role of Natural Language Processing in Generating Topics from Web Content

Examining the Role of Natural Language Processing in Generat...

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Computing (ICOCO), IEEE international conference on

作者： Hamza H.M. Altarturi Muntadher Saadoon Nor Badrul Anuar Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia Department of Software Engineering Universiti Malaya Kuala Lumpur Malaysia

Natural Language Processing (NLP) is a major subfield of machine learning that focuses on the interaction between humans and computers through several techniques, including topic modeling, which extracts and generates topics from large amounts of unstructured data on the web. Topic modeling uncovers hidden patterns and contextual meanings in web pages, allowing for the categorization, clustering, recommendation of relevant content, detection of emerging trends, and improved information retrieval. The benefit of topic models relies on their performance to detect and generate topics; however, the evaluation of applying topic modeling on web content data remains lacking in current studies. The absence of examining such performance evaluations hinders understanding performance variability across different web pages, the development of refined models, and the ability to keep pace with emerging patterns in the dynamic online environment. This study presents a comprehensive performance evaluation of the benchmark topic models when applied to web content data. It presents a comparison of topic models using four topic coherence metrics, providing an in-depth performance analysis. The evaluation is conducted on a public dataset consisting of 2 million web pages, providing a substantial basis for assessing the strengths and limitations of these models in the context of web data. Additionally, this study examines the utilization of NLP techniques for topic generation within the context of web content and sheds light on the challenges and issues associated with generating topics from web content data, highlighting potential research prospects and the need for future work in the field of web topic modeling using natural language processing.

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Fake Currency Detection using Image Processing

Fake Currency Detection using Image Processing

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international conference on Computing Communication Control and Automation (ICCUBEA)

作者： Sagar Shinde Lalitkumar Wadhwa Shreenand Naik Rohan Kudale Nitin Sherje Karan Mohnani Electronics & Telecommunication Dr. D. Y. Patil Institute of Technology Pune India Mechanical Engineering Dr. D. Y. Patil Institute of Technology Pune India

Advancements in color printing have led to a rise in the number of counterfeit currency notes being created and duplicated on a massive scale. In the past, it was very difficult to do this, but now anyone with a laser printer can easily do it. Due to the rise of fake notes, the number of counterfeit currency notes has increased significantly in India. This is a major issue that the country is facing due to its various problems such as black money and corruption. A system that can detect the fake currency notes in a fraction of the time is being developed. The proposed system will use image processing techniques to verify the authenticity of Indian currency notes. This method will involve extracting various features from the notes. Some of these include the Bleed Line, Water marking, Fluorescence, Security thread, Micro Lettering, and Identification mark. The proposed system is using software known as MATLAB. The Support vector machine is a supervised learning model that is used for extracting the notes' features. It is also used for analyzing data related to regression and classification. We have also tested the other methods related to this proposed work, such as the graphical black Box algorithm. The proposed system has various advantages, such as its high-performance speed and simplicity. It will be able to determine if the currency notes are real or fake. Its accuracy is also measured using the confusion Matrix.

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Toxicity Detection Methodology of Adverse Outcome Pathways using Physicochemical Properties and machine learning Approaches

Toxicity Detection Methodology of Adverse Outcome Pathways u...

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Emerging Technology (INCET), international conference for

作者： Vishan Kumar Gupta Avdhesh Gupta Mukesh Kumar Singh Ashutosh Gupta Ankit Tomar Department of CSE Sir Padampat Singhania University Udaipur Rajasthan India Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad UP India Dapartment of CSE Graphic Era Deemed to be University Dehradun UK India

In order to quickly and effectively determine whether certain chemical compounds have the potential to interfere with bodily functions that could be harmful to a person's health. Authors have developed a prediction model for improved assessment of toxicity. Here, we have presented an in-silico computational method for the prediction of toxicity of small drug molecules using their various physicochemical characteristics (molecular descriptors) that can bind with various nuclear receptor (NR) and stress response (SR) signalling pathways. These signalling pathways are also known as adverse outcome pathways. The pharmaceutical data exploration laboratory (PaDEL) software is used for extracting the features of all drug molecules. Initially, the class imbalance is resolved using SMOTE algorithms for all the datasets and feature selection is performed by a correlation-based feature selection algorithm. It is found that the extended topochemical atom (ETA) descriptors, electro-topological state descriptors, Crippen's log P, and Molar refractivity (MR) are quite rich in chemical information to encode the structural features that contribute to the toxicities and these indices may be used in combination with other topological and physicochemical descriptors for the development of predictive QSAR model. We have proposed a methodology that can be applied on various datasets of adverse outcome pathways for the prediction of toxicity of small drug molecules. The accuracy that we have found by using our proposed methodology is outperformed in comparison to using the models in simple manners.

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An Overview of Enabling Artificial Intelligence in 3GPP 5G-Advanced Networks

An Overview of Enabling Artificial Intelligence in 3GPP 5G-A...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Miao Qu Xiaoyong Tang Yingcheng Zhang Zhe Chen Tao Zhang Yuelin Zhao Yingchun Li Eagle Lab Shenzhen TCL New Technology Co.Ltd Shenzhen China School of Software Engineering Beijing Jiaotong University Beijing China ZGC Institute of Ubiquitous-X Innovation and Applications Beijing China School of Information Science and Engineering Harbin Institute of Technology Weihai China

Artificial intelligence (AI) and machine learning (ML) are regarded as indispensable technologies in wireless communication networks. The third Generation Partnership Project (3GPP) has approved introducing the AI/ML technology for RAN (Radio Access Network) in Release 18, known as the first release of 5G-Advanced, which is used to progressively improve the system performance to address the dramatic increase of both massive Internet-of-Things (IoT) devices and the enormous amount of data. This paper presents a comprehensive overview of enabling AI/ML technology in 3GPP RAN scope, which gives an investigation of the functional framework for AI/ML for the New Radio (NR) Air Interface, and analyzes some critical aspects in the AI/ML related life cycle management (LCM). Furthermore, some potential considerations related to AI/ML-based over air interface are also presented.

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