Anisha Kundu (Author) & Akshat Gupta (Co-Author)
In recent times, machine learning has become one of the key aspects of data handling. After years of research by the scientists, neuroscientists, and psychologists, numerous feasible technologies are available; some credit may go to the commercial and law enforcement applications as well. This paper proposes a technique for biometric recognition, which analyzes the geometry of the hand to find and isolate the vein patterns from near-infrared palm and wrist images and extract features based on repeated line tracking algorithm and maximum curvature algorithm.
AI in Talent Acquisition - Talent Connect 2017Przemek Berendt
Artificial intelligence is disrupting talent acquisition in several ways: (1) AI can automate tasks like screening resumes, conducting video interviews, and scheduling appointments; (2) Advanced AI uses techniques like deep learning and neural networks to personalize candidate outreach through targeted messaging and chatbots; (3) As AI assumes more roles, recruiters' jobs will change to become AI trainers, career engineers, explainers, and sustainers who ensure proper AI functioning. The document discusses these changes and recommends recruiters stay updated on technologies, identify skills gaps, experiment with AI tools, and adopt AI to help in their daily work.
This document provides an overview of artificial intelligence, including its history, importance, applications, and future. It discusses topics such as expert systems, robotics, game playing, medicine, natural language processing, pattern recognition, and the Turing test. Applications of AI mentioned include cognitive science, visual perception, navigation, speech recognition, and machine translation. The future of AI is discussed in areas like personal robots and other innovations enabled by advancing technologies behind pattern recognition and natural language processing.
Artificial intelligence (AI) is intelligence exhibited by machines. The document traces the history of AI from its origins in 1943 to its applications today. It discusses early developments like McCullough and Pitts' artificial neurons and Turing's work. Applications discussed include expert systems, natural language processing, computer vision, and robotics. The future of AI is uncertain but it is likely to continue advancing and being applied in more areas like virtual assistants, video games, and self-driving cars. The document concludes that AI has improved understanding of intelligence while revealing new challenges to address.
Artificial intelligence (AI) is a branch of computer science concerned with intelligent programs and machines. AI allows machines to learn from experience and perform human-like tasks through technologies like deep learning and natural language processing. Some applications of AI include healthcare, automation, robotics, banking, manufacturing, and retail. Key components of AI include search, pattern recognition, logic generation, common sense reasoning, learning from experience, and neural networks. However, the development and use of AI also raises ethical issues regarding exploitation, harm, and intellectual property.
Artificial intelligence (AI) is the ability of machines to mimic human intelligence and behavior. The document discusses the history and foundations of AI, including attempts to define intelligence and understand how the human brain works. It outlines four approaches to AI: systems that act humanly by passing the Turing test, systems that think humanly by modeling cognitive processes, and systems that act or think rationally. The document also discusses intelligent agents, knowledge-based systems, and applications of AI such as game playing and machine translation.
The legacy of industrial based networks is now evolving into the data infrastructure layer of the, 'Internet Of Things. With billions of devices about to be connected to the internet, there are signs of a convergence. A convergence between industrial and consumer, physical and virtual, man and machine. Everyday activities in the real world will have the potential to be augmented, improved and integrated into our digital lives by means of a smooth, flawless user experience. Welcome to the Internet Of Things.
The document provides an introduction to a lecture on artificial intelligence. It discusses the course content, which will cover intelligence, intelligent machines, and the birth and domains of AI. The domains discussed include hospitals using AI for scheduling and providing medical information, music composition and analysis, robotics, games, and banking applications such as managing operations and investments. The lecture will be given by Dr. Mazhar Ali Dootio from the computer science department.
The document discusses the Internet of Things (IoT) and the role of data in IoT systems. It covers the IoT ecosystem, including consumer and industrial applications. It then describes the IoT data flow from data capture by sensors, transmission through radio networks, storage and analysis in the cloud or data centers, and use by applications. Finally, it discusses some specific radio network technologies used for IoT, such as Sigfox, LoRa, and Narrowband IoT.
AI in Talent Acquisition - Talent Connect 2017Przemek Berendt
Artificial intelligence is disrupting talent acquisition in several ways: (1) AI can automate tasks like screening resumes, conducting video interviews, and scheduling appointments; (2) Advanced AI uses techniques like deep learning and neural networks to personalize candidate outreach through targeted messaging and chatbots; (3) As AI assumes more roles, recruiters' jobs will change to become AI trainers, career engineers, explainers, and sustainers who ensure proper AI functioning. The document discusses these changes and recommends recruiters stay updated on technologies, identify skills gaps, experiment with AI tools, and adopt AI to help in their daily work.
This document provides an overview of artificial intelligence, including its history, importance, applications, and future. It discusses topics such as expert systems, robotics, game playing, medicine, natural language processing, pattern recognition, and the Turing test. Applications of AI mentioned include cognitive science, visual perception, navigation, speech recognition, and machine translation. The future of AI is discussed in areas like personal robots and other innovations enabled by advancing technologies behind pattern recognition and natural language processing.
Artificial intelligence (AI) is intelligence exhibited by machines. The document traces the history of AI from its origins in 1943 to its applications today. It discusses early developments like McCullough and Pitts' artificial neurons and Turing's work. Applications discussed include expert systems, natural language processing, computer vision, and robotics. The future of AI is uncertain but it is likely to continue advancing and being applied in more areas like virtual assistants, video games, and self-driving cars. The document concludes that AI has improved understanding of intelligence while revealing new challenges to address.
Artificial intelligence (AI) is a branch of computer science concerned with intelligent programs and machines. AI allows machines to learn from experience and perform human-like tasks through technologies like deep learning and natural language processing. Some applications of AI include healthcare, automation, robotics, banking, manufacturing, and retail. Key components of AI include search, pattern recognition, logic generation, common sense reasoning, learning from experience, and neural networks. However, the development and use of AI also raises ethical issues regarding exploitation, harm, and intellectual property.
Artificial intelligence (AI) is the ability of machines to mimic human intelligence and behavior. The document discusses the history and foundations of AI, including attempts to define intelligence and understand how the human brain works. It outlines four approaches to AI: systems that act humanly by passing the Turing test, systems that think humanly by modeling cognitive processes, and systems that act or think rationally. The document also discusses intelligent agents, knowledge-based systems, and applications of AI such as game playing and machine translation.
The legacy of industrial based networks is now evolving into the data infrastructure layer of the, 'Internet Of Things. With billions of devices about to be connected to the internet, there are signs of a convergence. A convergence between industrial and consumer, physical and virtual, man and machine. Everyday activities in the real world will have the potential to be augmented, improved and integrated into our digital lives by means of a smooth, flawless user experience. Welcome to the Internet Of Things.
The document provides an introduction to a lecture on artificial intelligence. It discusses the course content, which will cover intelligence, intelligent machines, and the birth and domains of AI. The domains discussed include hospitals using AI for scheduling and providing medical information, music composition and analysis, robotics, games, and banking applications such as managing operations and investments. The lecture will be given by Dr. Mazhar Ali Dootio from the computer science department.
The document discusses the Internet of Things (IoT) and the role of data in IoT systems. It covers the IoT ecosystem, including consumer and industrial applications. It then describes the IoT data flow from data capture by sensors, transmission through radio networks, storage and analysis in the cloud or data centers, and use by applications. Finally, it discusses some specific radio network technologies used for IoT, such as Sigfox, LoRa, and Narrowband IoT.
1) The document introduces data science and its core disciplines, including statistics, machine learning, predictive modeling, and database management.
2) It explains that data science uses scientific methods and algorithms to extract knowledge and insights from both structured and unstructured data.
3) The roles of data scientists are discussed, noting that they have skills in programming, statistics, analytics, business analysis, and machine learning.
IoT in Healthcare: How Internet of Things (IoT) is Revolutionizing the Medica...PritiranjanMaharana1
IoT is reinventing the healthcare industry by upgrading the treatment process. It enables healthcare professionals to become more proactive and deliver an advanced set of patient care.
This post overlooks the benefits of IoT and its impact on the healthcare industry.
The document outlines the typical data science project lifecycle and necessary data scientist skill set. It describes the main stages as business requirements, data acquisition, data preparation, hypothesis/modeling, evaluation/interpretation, deployment, and optimization. For each stage, it provides brief explanations of common tasks. It also maps out key skill areas for data scientists including programming, domain knowledge, data collection/wrangling, statistics, machine learning, visualization, and communication. Finally, it compares course offerings at major universities based on these skill areas.
Artificial Intelligence: Case studies (what can you build)Rudradeb Mitra
The document discusses different types of artificial intelligence algorithms like deep learning using neural networks and reinforcement learning. It provides examples of both short term and mid term projects that can be built using existing AI tools, from basic chatbots to predictive maintenance and customer behavior analysis. Long term challenges are also mentioned, like developing more intuitive algorithms through reinforcement learning and ensuring the safe and responsible development of advanced artificial intelligence.
Virtual reality in health care by Rabeendra Basnetरविन्द्र बस्नेत
Virtual Reality in Healthcare in terms of preventive, curative and restorative and rehabilitative purpose in the physical, virtual, Ambient and Augmented Reality through computer generation enviroments.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
The document provides an overview of data science including its history and introduction. It discusses how data science emerged in the late 1990s and early 2000s, with Jim Gray coining the term "data-driven science" in 2007. It defines a data scientist as a new breed of analytical expert who uses technical skills to solve complex problems and explore which issues need addressing. Data scientists build machine learning applications and their toolbox includes skills like data visualization, machine learning, deep learning, and data preparation. The document also compares data science to related fields of big data and data analytics.
NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases.
https://tech.rakuten.co.jp/
This document summarizes a presentation about Internet of Things (IoT) protocols. It discusses how the IoT is projected to connect 100 billion objects by 2020 and defines the IoT according to different companies. It then analyzes common IoT protocols like AMQP, MQTT, XMPP, and DDS, explaining what types of applications each is best suited for. Finally, it discusses how to choose a protocol based on requirements like performance, connectivity, and use cases like smart grids, vehicles, healthcare and more.
Artificial intelligence (AI) is intelligence demonstrated by machines, unlike natural intelligence displayed by humans and animals. AI involves developing intelligent machines that can think and act like humans. There are various types of AI based on the level of intelligence, including narrow AI, general AI, and super AI. AI is being applied across many domains such as healthcare, transportation, marketing, finance, and more. Examples of AI applications include manufacturing robots, self-driving cars, smart assistants, and more. Frameworks for developing AI include TensorFlow, PyTorch, and Keras.
Data is being referred to as the "new oil" because:
1. While oil is a finite resource, data is being produced in exponentially increasing quantities each day.
2. Like oil, data is a valuable resource that needs to be extracted and analyzed to derive useful insights and value.
3. Various sectors can benefit from analyzing and utilizing data, just as the economy benefits from oil exploration and production.
Differences Between Machine Learning Ml Artificial Intelligence Ai And Deep L...SlideTeam
"You can download this product from SlideTeam.net"
Differences between Machine Learning ML Artificial Intelligence AI and Deep Learning DL is for the mid level managers to give information about what is AI, what is Machine Learning, what is deep learning, Machine learning process. You can also know the difference between Machine learning and Deep learning to understand AI, ML, and DL in a better way for business growth. https://bit.ly/325zI9o
Introduction To Artificial Intelligence PowerPoint Presentation SlidesSlideTeam
Introduction to Artificial Intelligence is for the mid level managers giving information about what is AI, AI levels, types of AI, where AI is used. You can also know the difference between AI vs Machine learning vs Deep learning to understand expert system in a better way for business growth. https://bit.ly/3er7KWI
Artificial intelligence (AI) is a branch of computer science dealing with intelligent behavior in machines. It has a long history dating back to 1943, with early milestones like Samuel's checker program in the 1950s. AI aims to create human-like intelligence through techniques like perception, reasoning, and learning. While computers have advantages in speed and memory, they still lack human-level understanding. AI has many applications including expert systems, natural language processing, computer vision, and robotics. Popular programming languages for developing AI include Lisp, Python, Prolog, Java, and C++. The future of AI is uncertain but most believe it will continue advancing to handle more complex problems.
This document discusses privacy, security, and ethics in data science. It covers topics such as anonymizing data and computations, seeking security for personal data, and the unethical surprises that can occur in data science work. It also discusses how to respect privacy by securely storing data, adding layers of protection like encryption, and using techniques like distributed computing and differential privacy to better protect sensitive information. The document cautions that biases in data can propagate biases in models, and highlights the importance of addressing issues like social bias, redaction of sensitive info, and debiasing models to help ensure ethical practices in this field.
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
This document discusses the roles of data science and data scientists. It states that data science involves specialized skills in statistics, mathematics, programming, and computer science. A data scientist explores different data sources to discover hidden insights that can provide competitive advantages or address business problems. They are inquisitive individuals who can analyze data from multiple angles and recommend ways to apply findings to business challenges.
Automated Invoice Processing Using Image Recognition in Business Information ...ectijjournal
In recent years, businesses have increasingly relied on digitalization to streamline their operations and improve efficiency. One critical area that often requires manual intervention is invoice processing, which can be time-consuming and prone to errors. This paper aims to explore the application of image processing techniques in automating invoice processing within business information systems.The study will focus on developing an image recognition system capable of extracting relevant information from invoice images, such as vendor details, invoice numbers, item descriptions, and total amounts. Through the use of computer vision algorithms and machine learning techniques, the system will be taught to accurately identify and extract data from a wide range of invoice layouts and formats. The study will explore different image preprocessing methods to enhance the quality of invoice images and increase the precision of text extraction.Additionally, the study will explore different machine learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to develop an effective classification and extraction system.Furthermore, the research will evaluate the performance of the proposed system by comparing it with traditional manual invoice processing methods in terms of speed, accuracy, and costeffectiveness. To validate the system's performance and resilience, a thorough series of experiments will be carried out using real-world invoice datasets. The results of this research will hold substantial implications for businesses, as they will lay the groundwork for integrating automated invoice processing systems into their information systems.By reducing manual intervention and minimizing errors, businesses can achieve higher efficiency and cost savings. Furthermore, this research will make a valuable contribution to the wider domain of image processing and its practical applications in business information systems.
MB2208A- Business Analytics- unit-4.pptxssuser28b150
This document provides an overview of predictive analytics, including:
- Predictive analytics uses historical data and machine learning techniques to predict future outcomes. It focuses on forecasting rather than just describing past events.
- Common predictive analytics applications include customer churn prediction, demand forecasting, risk assessment, and equipment maintenance scheduling.
- There are two main types of predictive models: logic-driven models based on known relationships between variables, and data-driven models using statistics and machine learning.
- The predictive analytics process involves collecting and cleaning data, selecting a modeling technique, building and validating the model, and deploying it to make predictions.
1) The document introduces data science and its core disciplines, including statistics, machine learning, predictive modeling, and database management.
2) It explains that data science uses scientific methods and algorithms to extract knowledge and insights from both structured and unstructured data.
3) The roles of data scientists are discussed, noting that they have skills in programming, statistics, analytics, business analysis, and machine learning.
IoT in Healthcare: How Internet of Things (IoT) is Revolutionizing the Medica...PritiranjanMaharana1
IoT is reinventing the healthcare industry by upgrading the treatment process. It enables healthcare professionals to become more proactive and deliver an advanced set of patient care.
This post overlooks the benefits of IoT and its impact on the healthcare industry.
The document outlines the typical data science project lifecycle and necessary data scientist skill set. It describes the main stages as business requirements, data acquisition, data preparation, hypothesis/modeling, evaluation/interpretation, deployment, and optimization. For each stage, it provides brief explanations of common tasks. It also maps out key skill areas for data scientists including programming, domain knowledge, data collection/wrangling, statistics, machine learning, visualization, and communication. Finally, it compares course offerings at major universities based on these skill areas.
Artificial Intelligence: Case studies (what can you build)Rudradeb Mitra
The document discusses different types of artificial intelligence algorithms like deep learning using neural networks and reinforcement learning. It provides examples of both short term and mid term projects that can be built using existing AI tools, from basic chatbots to predictive maintenance and customer behavior analysis. Long term challenges are also mentioned, like developing more intuitive algorithms through reinforcement learning and ensuring the safe and responsible development of advanced artificial intelligence.
Virtual reality in health care by Rabeendra Basnetरविन्द्र बस्नेत
Virtual Reality in Healthcare in terms of preventive, curative and restorative and rehabilitative purpose in the physical, virtual, Ambient and Augmented Reality through computer generation enviroments.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
The document provides an overview of data science including its history and introduction. It discusses how data science emerged in the late 1990s and early 2000s, with Jim Gray coining the term "data-driven science" in 2007. It defines a data scientist as a new breed of analytical expert who uses technical skills to solve complex problems and explore which issues need addressing. Data scientists build machine learning applications and their toolbox includes skills like data visualization, machine learning, deep learning, and data preparation. The document also compares data science to related fields of big data and data analytics.
NVIDIA compute GPUs and software toolkits are key drivers behind major advancements in machine learning. Of particular interest is a technique called "deep learning", which utilizes what are known as Convolution Neural Networks (CNNs) having landslide success in computer vision and widespread adoption in a variety of fields such as autonomous vehicles, cyber security, and healthcare. In this talk is presented a high level introduction to deep learning where we discuss core concepts, success stories, and relevant use cases. Additionally, we will provide an overview of essential frameworks and workflows for deep learning. Finally, we explore emerging domains for GPU computing such as large-scale graph analytics, in-memory databases.
https://tech.rakuten.co.jp/
This document summarizes a presentation about Internet of Things (IoT) protocols. It discusses how the IoT is projected to connect 100 billion objects by 2020 and defines the IoT according to different companies. It then analyzes common IoT protocols like AMQP, MQTT, XMPP, and DDS, explaining what types of applications each is best suited for. Finally, it discusses how to choose a protocol based on requirements like performance, connectivity, and use cases like smart grids, vehicles, healthcare and more.
Artificial intelligence (AI) is intelligence demonstrated by machines, unlike natural intelligence displayed by humans and animals. AI involves developing intelligent machines that can think and act like humans. There are various types of AI based on the level of intelligence, including narrow AI, general AI, and super AI. AI is being applied across many domains such as healthcare, transportation, marketing, finance, and more. Examples of AI applications include manufacturing robots, self-driving cars, smart assistants, and more. Frameworks for developing AI include TensorFlow, PyTorch, and Keras.
Data is being referred to as the "new oil" because:
1. While oil is a finite resource, data is being produced in exponentially increasing quantities each day.
2. Like oil, data is a valuable resource that needs to be extracted and analyzed to derive useful insights and value.
3. Various sectors can benefit from analyzing and utilizing data, just as the economy benefits from oil exploration and production.
Differences Between Machine Learning Ml Artificial Intelligence Ai And Deep L...SlideTeam
"You can download this product from SlideTeam.net"
Differences between Machine Learning ML Artificial Intelligence AI and Deep Learning DL is for the mid level managers to give information about what is AI, what is Machine Learning, what is deep learning, Machine learning process. You can also know the difference between Machine learning and Deep learning to understand AI, ML, and DL in a better way for business growth. https://bit.ly/325zI9o
Introduction To Artificial Intelligence PowerPoint Presentation SlidesSlideTeam
Introduction to Artificial Intelligence is for the mid level managers giving information about what is AI, AI levels, types of AI, where AI is used. You can also know the difference between AI vs Machine learning vs Deep learning to understand expert system in a better way for business growth. https://bit.ly/3er7KWI
Artificial intelligence (AI) is a branch of computer science dealing with intelligent behavior in machines. It has a long history dating back to 1943, with early milestones like Samuel's checker program in the 1950s. AI aims to create human-like intelligence through techniques like perception, reasoning, and learning. While computers have advantages in speed and memory, they still lack human-level understanding. AI has many applications including expert systems, natural language processing, computer vision, and robotics. Popular programming languages for developing AI include Lisp, Python, Prolog, Java, and C++. The future of AI is uncertain but most believe it will continue advancing to handle more complex problems.
This document discusses privacy, security, and ethics in data science. It covers topics such as anonymizing data and computations, seeking security for personal data, and the unethical surprises that can occur in data science work. It also discusses how to respect privacy by securely storing data, adding layers of protection like encryption, and using techniques like distributed computing and differential privacy to better protect sensitive information. The document cautions that biases in data can propagate biases in models, and highlights the importance of addressing issues like social bias, redaction of sensitive info, and debiasing models to help ensure ethical practices in this field.
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
This document discusses the roles of data science and data scientists. It states that data science involves specialized skills in statistics, mathematics, programming, and computer science. A data scientist explores different data sources to discover hidden insights that can provide competitive advantages or address business problems. They are inquisitive individuals who can analyze data from multiple angles and recommend ways to apply findings to business challenges.
Automated Invoice Processing Using Image Recognition in Business Information ...ectijjournal
In recent years, businesses have increasingly relied on digitalization to streamline their operations and improve efficiency. One critical area that often requires manual intervention is invoice processing, which can be time-consuming and prone to errors. This paper aims to explore the application of image processing techniques in automating invoice processing within business information systems.The study will focus on developing an image recognition system capable of extracting relevant information from invoice images, such as vendor details, invoice numbers, item descriptions, and total amounts. Through the use of computer vision algorithms and machine learning techniques, the system will be taught to accurately identify and extract data from a wide range of invoice layouts and formats. The study will explore different image preprocessing methods to enhance the quality of invoice images and increase the precision of text extraction.Additionally, the study will explore different machine learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to develop an effective classification and extraction system.Furthermore, the research will evaluate the performance of the proposed system by comparing it with traditional manual invoice processing methods in terms of speed, accuracy, and costeffectiveness. To validate the system's performance and resilience, a thorough series of experiments will be carried out using real-world invoice datasets. The results of this research will hold substantial implications for businesses, as they will lay the groundwork for integrating automated invoice processing systems into their information systems.By reducing manual intervention and minimizing errors, businesses can achieve higher efficiency and cost savings. Furthermore, this research will make a valuable contribution to the wider domain of image processing and its practical applications in business information systems.
MB2208A- Business Analytics- unit-4.pptxssuser28b150
This document provides an overview of predictive analytics, including:
- Predictive analytics uses historical data and machine learning techniques to predict future outcomes. It focuses on forecasting rather than just describing past events.
- Common predictive analytics applications include customer churn prediction, demand forecasting, risk assessment, and equipment maintenance scheduling.
- There are two main types of predictive models: logic-driven models based on known relationships between variables, and data-driven models using statistics and machine learning.
- The predictive analytics process involves collecting and cleaning data, selecting a modeling technique, building and validating the model, and deploying it to make predictions.
Attendance management system using face recognitionIAESIJAI
Traditional attendance systems consist of registers marked by teachers, leading to human error and a lot of maintenance. Time consumption is a key point in this system. We wanted to revolutionize the digital tools available in today's time i.e., facial recognition. This project has revolutionized to overcome the problems of the traditional system. Face recognition and marking the present is our project. A database of all students in the class is kept in single folder, and attendance is marked if each student's face matches with one of the stored faces. Otherwise, the face is ignored and not marked for attendance. In our project, face detection (machine learning) is used.
Machine learning is a method of data analysis that allows computer systems to automatically learn and improve from experience without being explicitly programmed. It works by building models from data to make predictions or decisions without relying on rule-based programming. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. There are several types of machine learning algorithms including supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. Machine learning has many applications and is used across various industries like healthcare, retail, finance, government and transportation to extract insights from data.
Implementation of Automatic Attendance Management System Using Harcascade and...IRJET Journal
This document proposes an automatic attendance management system using facial recognition algorithms. It aims to reduce human error and resources required for manual attendance recording. The system uses a camera to capture faces at the entrance and matches them to employee photos stored in a database using Haar cascade detection and local binary pattern recognition. If a match is found, the employee is marked present and their attendance updated in real time to an Excel sheet for administrators to view. The system is intended to help organizations more efficiently track attendance compared to traditional paper-based methods.
IRJET- Survey Paper on Vision based Hand Gesture RecognitionIRJET Journal
This document presents a survey of previous research on vision-based hand gesture recognition. It discusses various methods that have been used, including discrete wavelet transforms, skin color segmentation, orientation histograms, and neural networks. The document proposes a new methodology using webcam image capture, static and dynamic gesture definition, image processing techniques like localization, enhancement, segmentation, and morphological filtering, and a convolutional neural network for classification. The goal is to develop a more efficient and accurate system for hand gesture recognition and human-computer interaction.
This document summarizes several computer vision and soft computing techniques that have been used for offline signature verification and forgery detection in previous research studies. It discusses techniques like fuzzy logic, artificial neural networks, feature extraction methods, and related systems developed by other researchers that have used approaches like neural networks trained on local and global features to classify signatures. The document also provides a brief introduction to concepts like computer vision technology, the need for automated signature verification systems, and defines terms like soft computing and discusses some common soft computing techniques.
Advanced Authentication Scheme using Multimodal Biometric SchemeEditor IJCATR
This document presents a study on using multimodal biometrics with palm and fingerprint recognition to improve identification accuracy. The authors first discuss existing unimodal biometrics and limitations. They then describe the typical steps in a multimodal system: image capture, preprocessing, feature extraction, fusion, and matching. For this study, minutiae extraction is used to extract fingerprint features while local binary patterns extract palm features. Wavelet fusion is applied to the extracted features before support vector machine matching. The authors aim to demonstrate that combining palm and fingerprint biometrics can achieve better performance than single biometrics alone.
Understanding The Pattern Of RecognitionRahul Bedi
Pattern recognition is identifying patterns and regularities in data through algorithms and mathematical models. It’s a field that has revolutionized the way we process and make decisions based on data. Contact EnFuse Solutions today and discover how pattern recognition can transform your business. For more information visit here: https://www.enfuse-solutions.com/
What is popular in the manufacturing industry today? I think it’s going to be digital conversion, Industry 4.0, artificial intelligence...
Let’s take a look at how AI is changing manufacturing.
A Smart Receptionist Implementing Facial Recognition and Voice InteractionCSCJournals
The purpose of this research is to implement a smart receptionist system with facial recognition and voice interaction using deep learning. The facial recognition component is implemented using real time image processing techniques, and it can be used to learn new faces as well as detect and recognize existing faces. The first time a customer uses this system, it will take the person’s facial data to create a unique user facial model, and this model will be triggered if the person comes the second time. The recognition is done in real time and after which voice interaction will be applied. Voice interaction is used to provide a life-like human communication and improve user experience. Our proposed smart receptionist system could be integrated into the self check-in kiosks deployed in hospitals or smart buildings to streamline the user recognition process and provide customized user interactions. This system could also be used in smart home environment where smart cameras have been deployed and voice assistants are in place.
Employment Performance Management Using Machine LearningIRJET Journal
This document discusses using machine learning techniques to analyze employee performance. Specifically, it proposes using a support vector machine (SVM) algorithm to identify employee performance based on factors like quality, timeliness, and cost. The document reviews related literature on using both traditional and data-driven approaches to performance assessment. It then outlines the proposed system for building a software tool to manage employee performance data using SVM. Key steps in the SVM algorithm are described. The document concludes that improving individual performance can boost business results and SVM is effective for differentiating between two groups of data.
The document describes a gesture recognition system that uses computer vision techniques. It discusses different approaches to hand gesture recognition including vision-based, glove-based, and depth-based techniques. The proposed system uses computer vision and media pipe libraries to track hand landmarks and recognize gestures in real-time. It then uses those gestures to control functions like a virtual mouse, change volume, and zoom in/out. The system aims to provide natural human-computer interaction through contactless hand gesture recognition.
This document discusses how machine learning can help detect fraud. It explains that machine learning models are trained on historical transaction data to learn patterns and detect anomalies. Common machine learning algorithms used for fraud detection include logistic regression, decision trees, random forests, and neural networks. While machine learning is effective for fraud detection, it also has some limitations such as a lack of interpretability and needing sufficient data to identify patterns. An example is provided of a global bank that implemented a machine learning solution to reduce check fraud losses by speeding up verification.
This document describes a proposed sign language interpreter system that uses machine learning and computer vision techniques. It aims to enable deaf and mute users to communicate through computers and the internet by recognizing static hand gestures from camera input and translating them to text. The proposed system extracts features from captured images of signs and uses a support vector machine model to classify the gestures by comparing to a dataset of labeled images. If implemented, this system could help overcome communication barriers for deaf users in an increasingly digital world.
Automated attendance system using Face recognitionIRJET Journal
This document describes an automated attendance system using face recognition. The system uses image capture to take photos of students entering the classroom. It then uses the Viola-Jones algorithm for face detection and PCA for feature selection and SVM for classification to recognize students' faces and mark their attendance automatically. When compared to traditional attendance methods, this system saves time and helps monitor students. It discusses related work using RFID, fingerprints, and iris recognition for attendance systems. It outlines the proposed system's modules for image capture, face detection, preprocessing, database development, and postprocessing. Finally, it discusses results, conclusions, and opportunities for future work to improve recognition rates under various conditions.
Daily Human Activity Recognition using Adaboost Classifiers on Wisdm Datasetijtsrd
Human activity recognition is an important area of machine learning research as it has much utilization in different areas such as sports training, security, entertainment, ambient assisted living, and health monitoring and management. Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human. Nowadays mobile phone is well equipped with advanced processor, more memory, powerful battery and built in sensors. This provides an opportunity to open up new areas of data mining for activity recognition of human's daily living. In the paper, the benchmark dataset is considered for this work is acquired from the WISDM laboratory, which is available in public domain. We tested experiment using AdaBoost.M1 algorithm with Decision Stump, Hoeffding Tree, Random Tree, J48, Random Forest and REP Tree to classify six activities of daily life by using Weka tool. Then we also see the test output from weka experimenter for these six classifiers. We found the using Adaboost,M1 with Random Forest, J.48 and REP Tree improves overall accuracy. We showed that the difference in accuracy for Random Forest, REP Tree and J48 algorithms compared to Decision Stump, and Hoeffding Tree is statistically significant. We also show that the accuracy of these algorithms compared to Decision Stump, and Hoeffding Tree is high, so we can say that these two algorithms achieved a statistically significantly better result than the Decision Stump, and Hoeffding Tree and Random Tree baseline. Khin Khin Oo "Daily Human Activity Recognition using Adaboost Classifiers on Wisdm Dataset" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28073.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28073/daily-human-activity-recognition-using-adaboost-classifiers-on-wisdm-dataset/khin-khin-oo
Machine Learning: Machine learning is a subset of AI that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data. It includes supervised learning, unsupervised learning, and reinforcement learning.
Deep Learning: Deep learning is a subfield of machine learning that involves artificial neural networks inspired by the structure and function of the human brain. Deep learning has achieved remarkable results in tasks like image and speech recognition.
Natural Language Processing (NLP): NLP is the study of how to enable computers to understand, interpret, and generate human language. It's used in chatbots, language translation, sentiment analysis, and more.
Computer Vision: Computer vision involves giving machines the ability to interpret and understand visual information from the world, enabling them to "see" and make decisions based on images or video.
Robotics: AI is integral to the field of robotics, where it's used to create intelligent robots capable of interacting with the physical world, performing tasks, and even making autonomous decisions.
Autonomous Systems: AI powers autonomous systems, from self-driving cars to drones and industrial robots. These systems use sensors and AI algorithms to make real-time decisions without human intervention.
Expert Systems: Expert systems are AI programs that mimic the decision-making abilities of a human expert in a specific domain. They are used for tasks like medical diagnosis and financial planning.
AI Ethics: As AI becomes more prominent, ethical considerations around its use and potential biases in algorithms have become a significant concern. Ensuring that AI is used responsibly and ethically is a growing field of study.
AI in Industry: AI is used across various industries, from healthcare and finance to entertainment and agriculture. It can optimize processes, provide insights from large datasets, and create new applications.
AI in Research: AI is used in scientific research for tasks like drug discovery, climate modeling, and data analysis. AI can process and analyze vast amounts of data, enabling new discoveries.
AI in Education: AI is used in educational technology for personalized learning, intelligent tutoring systems, and assessing student performance.
AI in Natural Language Generation: AI can generate human-like text, which is used in content generation, journalism, and even creative writing.
AI has the potential to revolutionize how we live and work, making systems more efficient, aiding in decision-making, and opening up new possibilities. However, it also comes with challenges, such as ensuring its responsible and ethical use and addressing concerns about job displacement. As AI continues to advance, it will likely have a profound impact on various aspects of society and industry.
Face Recognition Smart Attendance System: (InClass System)IRJET Journal
- The document describes a face recognition system called "InClass" to automate student attendance tracking. It aims to address issues with traditional manual attendance systems like being inaccurate, time-consuming, and difficult to maintain.
- The InClass system uses a CNN face detector to detect and identify students' faces from images captured with a camera. It can handle variations in lighting, angles, and occlusions. Matching faces to a database allows for automated attendance marking.
- The system aims to simplify the attendance process, reduce time and errors compared to existing biometric systems, and make attendance records easily accessible and storable digitally rather than on paper.
Facial Recognition based Attendance System: A SurveyIRJET Journal
The document describes a facial recognition-based attendance system. It discusses how traditional attendance methods are inefficient and error-prone. The proposed system utilizes facial recognition technology, machine learning algorithms, and a database to accurately identify and track attendance in real-time. It analyzes different facial recognition and machine learning methods like OpenCV, cosine similarity, and distance metrics to efficiently and securely match facial templates. The system aims to provide a customized, user-friendly and robust alternative to traditional attendance tracking methods in educational institutions and organizations.
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Integration of Machine Learning in attendance and payroll
1. Integration of Machine Learning in Attendance and Payroll
Anisha Kundu1
Akshat Gupta2
ABSTRACT
In recent times, machine learning has become one of the key aspects of data handling. After
years of research by the scientists, neuroscientists and psychologists, numerous feasible
technologies are available; some credit may go to the commercial and law enforcement
applications as well. This paper proposes a technique for biometric recognition, which
analyze the geometry of the hand to find and isolate the vein patterns from near infrared palm
and wrist images; and extract features based on repeated line tracking algorithm and
maximum curvature algorithm. In plain words, it is a computer application for automatically
identifying a person from its palm and wrist vein images. This technique could be used to
create an automated attendance management system, which implicitly detects the employee
when he/she enters the office gate and marks their attendance by recognizing them. The line
tracking algorithm tracks the dark line pattern at random points and repeats the process until
all the pixel points are collected. Whereas, the maximum curvature algorithm identifies the
curved like structure at the centre of the vein to draw the pattern using the position of the
structure. The extracted patterns are then matched using robust template matching technique,
where pixel-to-pixel comparison is made. When compared to traditional attendance marking
this system saves the time and also helps to monitor the employees.
Once the attendance of the employees is collected, this data can be used to analyse other
aspects such as: measuring the employee engagement, studying the workforce patterns of a
particular department or region, analysing employee churn and turnover. All these come
under the evolving application field of analytics for Human Resource Management, called,
the Human Resource Predictive Analytics. It helps in optimizing the performances and
produce better return on investment for an organization using the concept of decision making
based on data collection and predictive models, for effective and efficient management of
human resources. Expert system or knowledge-based systems, which were earlier used for
such decision making, demonstrated few limitations. Hence in this paper, we will propose
Intelligent Human Resource Information System (i-HRIS) which applies Intelligent Decision
Support System (IDSS) and Knowledge Discovery in Database (KDD) to improve the
structured, semi-structured and unstructured decision-making process. With a set of Artificial
Intelligent tools such as knowledge-based reasoning and machine learning, the Intelligent
Decision Support System stores and processes information. The concept of Machine Learning
is used to discover useful information from past data and experience to support decision
making process by applying hybrid intelligent techniques.
1. Introduction
With the advancement of machine learning, Human Resource Management System is now
able to eliminate repetitive tasks, reduce employee attrition and improve employee
engagement. By using various algorithms, we are able to simulate the behavior of human
2. and to re-imagine the experience of the employees. Artificial Intelligence helps in drawing
out the insights and inferences, which might remain uncovered at all from general
manpower. It has bought this good news for Human Resources and has given it a chance to
catch up with the digital transformation. In summation, the proposed framework consists
of input subsystems, decision making subsystems and output subsystems; for a better
management of human resources.
It has been quite a decade that the idea of computers learning autonomously has been
around. So why machine learning has gained so much ground and what has changed in
recent years? Various possible reasons could be:
Increased computing power; improved performance, driven by gaming, graphic processing
units (GPUs), at the level of parallel computation of simple operations, commonly used by
deep learning algorithms. In-memory databases, together with the wide adoption of multi-
core architectures, has paved the way for extremely efficient implementations of machine
learning algorithms.
Big data, being another reason. Huge data sets provided by various sources, are the basis
for training machines. For an example, the ability to tag individual faces on pictures (with
names) on social media has led to the largest database of faces in the world. These social
media, such as Instagram, Facebook etc. can train machines to learn in terms of visual
recognition. (artificial-intelligence-in-hr-and-payroll-embracing-disruption, n.d.)
On the basis of machine learning algorithms, devices can be trained to predict and interpret
sophisticated situations in the future.
Due to the vast number of free, high-quality, open-source software packages making
machine learning accessible to a large audience of data scientists and developers, machine
learning is becoming easier to apply.
Machines can recognize objects, read and understand text, write, listen and talk. This shows
how machine learning can bring intelligence to business environments.
One of the most important, yet one of the most cumbersome and time-consuming daily
activities, of any company is the Payroll administration. Keeping this process to bare
minimum in time as well as cost, means savings and enhanced efficiency to the business,
as it does not generate direct revenues. Hence why not create a software to take care of this
process, so that you can focus on important daily core tasks.
The Payroll system manages wage calculation, allowances, absences, expenses, benefits,
tax deductions etc. with very little input from you, such as wage details and work hours of
an employee. A payroll automation system consolidates employee data and regulatory
rules, hence avoid inaccurate financial statements and penalties. (4-trends-in-payroll-
management, n.d.). It is estimated that automated payroll system helps reduce costs on
paycheck errors by around 80%.
Other advantages of having an automated payroll system are:
3. i. To create customized reports, where you can define rows and columns, fields and
other rules; and generate recurring reports scheduled with smart alerts.
ii. To add security, without relying on a third party and exposing sensitive employee
data. Automated payroll system nowadays can be easily managed internally, by the
authorized staff.
iii. To create a smarter rostering process. Through machine learning, rostering will be
able to take into account certain external inputs like weather, events or concerts,
public holidays and other seasonal variations and then predict the staffing
requirements.
iv. To have auto-approval of leaves, reimbursements etc. through the use of machine
learning. With only exceptions being surfaced to managers for approval, spending
countless hours on unnecessary manual approvals could be eliminated.
v. To have a continuous compliance over any potential legislative changes. Payroll
systems will be able to track legislative changes, using machine learning and
artificial intelligence; and analyze the impact of those changes on a business’
payroll. It enables business owners to be on top of payroll legislation by notifying
them of potential issues.
We chose a technique for biometric recognition, which analyze the geometry of the hand
to find and isolate the vein patterns from near infrared palm and wrist images; and match
them with the registered vein pattern images, say from our employee database, to identify
individuals, genuinely.
Since no physical contact is needed to obtain the palm or wrist vein image, so it is better
than fingerprint and iris scanning, and will not cause any displeasure to the subject. It is
hard to forge vein patterns, unlike fingerprints which nowadays can be easily captured and
reproduced by any quick drying adhesives. Vein patters are unique as different people have
different vein patterns and it undergoes less change following the growth of age. Unlike
fingerprint or facial acquirement, the states of skin, temperature and humidity have little
effect on the vein image.
Under skin patterns are captured by a special camera, with lighting of near-infrared lights.
Infrared lights are absorbed by haemoglobin, resulting in dark shadow pattern (Kono,
“Near-infrared finger vein patterns for personal identification) (Lin C.-L. a.-C., 2004)
(Kono, A new method for the identification of individuals by using of vein pattern matching
of a finger, 2000) of the veins or blood vessels. Some irregular shading and noise might
also generate, along the vein patterns, caused by the varying thickness of muscles and
bones, and avoidance of continuity. Therefore, the captured images need to be processed
to eliminate those irregularities and obtain a clear view of veins. While executing complex
differential operations and optimization of the line extraction, the computational costs gets
exponentially high. Even the processing time of an image in such a case may get prolonged.
In Miura et. al (N Miura, 2004) by using the repeated line tracking algorithm, to extract
vein patterns of an unclear image can be processed, but since the number of times the
tracking point moves on thin veins tends to be statistically very small, it may not be
4. adequate to extract thin veins. Hence Miura et. al (Miura, 2007) proposes a method where
the curvature of the image profiles are checked and only the centrelines of veins are
emphasized. The positions which gives the local maximal of the curvatures of a cross-
sectional profile of a vein image, helps detect the centrelines of veins, and a robust method
is used to detect the maximum curvature against temporal fluctuations in the width and
brightness of vein.
After enhancing the image using various pre-processing tools such as segmentation,
normalization, edge detection etc., we need to extract the features of the image using feature
extraction algorithms, none other than “repeated line tracking algorithm” and “maximum
curvature points”. Finally, we match the images based on a robust template- matching
technique.
Once image is captured, processed and stored, we apply the HRIS (Human Resource
Information System), KDD (Knowledge Discovery in Database) and IDSS (Intelligence
Decision Support System). When an automated system was developed and employee data
were used for the first time in the late 1960s (M., 2014), the concept of HRIS came into
play. KDD is a widely used term in intelligent data processing. Fayyad (U., 1997) defines
KDD as a nontrivial process of identifying potentially useful, valid, novel, and ultimately
logical patterns in a data set. IDSS is a new type of DSS that is integrated with AI
techniques. This system is a combination of basic function models of DSS and knowledge
reasoning techniques of AI. It solves complex, imprecise and ill-structured problems
(Ribeiro R., 2006).
2. Literature Review
Due to the convenience of image acquisition, several vein features in the hand have been
well studied, such as finger veins, hand veins and hand-dorsal veins. In particular, the palm
veins have gained more attention from researchers due to their more abundant texture
information and easy acquisition. Recently, studies of palm veins have focused on feature
extraction methods that acquire the salient features more efficiently. (Chen, 2007) (Zeman,
2004)
Materials and Conditions
A CIE vein database containing 1200 infrared palm images and 1200 infrared wrist images,
each of 1280×960 resolution and of a 24-bit bitmap. The images are named in the form
representing the person number, palm or wrist, left or right, series number followed by the
picture number. The images are already pre-processed, with a proper lighting and contrast,
well maintained distance from the camera and sample, less distorted. Each person folder
has sub- folders for left and right hand, which further contains sub-folders for each series.
Images have the information in the names, for example: in Figure 2.1, it says
“P_o001_L_S1_Nr1” is the 1st image of the 1st series of Left palm of 1st person.
5. Fig. 2.1: Image name: P_o001_L_S2_Nr3 Fig. 2.2: Image name: P_o001_R_S2_Nr3
Basic Concepts
a. Image reading:
The process of selecting, storing, displaying the image and reading the input image for
processing. A 1280x960 resolution vein image, Image1(x, y), is selected from the CIE vein
database.
b. Pre- processing:
It takes both input and output as intensity images. Helps in eliminating the unnecessary
distortion and also improving some important features of images, which may be required
in future for further processing.
Fig 2.3: Brief view of image segmentation
2. Pre Processing
Complimentary
Image
Thinning
Connecting broken lines
Local Thresholding
1.Image Reading
Global
Thresholding
Edge
Detection
Cropping
Image
Selecting ROI
3. Image
Normalization
4. Post Processing
Smoothing
image
Erosion
Dilation
5. Binary Image
6. c. Global thresholding:
Here the pixels of the image are dominated into two mode, background and foreground. A
threshold value helps in separating the pixels, acts as a decision factor. If the intensity point
is greater than threshold, then it is accepted as the foreground object point else if lesser than
equals to the threshold, is the background point. It is implemented on the image as the
brightness of the image is more than that of the background.
𝐼𝑚𝑎𝑔𝑒2(𝑥, 𝑦) = {
0, 𝑖𝑓 𝐼𝑚𝑎𝑔𝑒1(𝑥, 𝑦) < 𝑡
1, 𝑖𝑓 𝐼𝑚𝑎𝑔𝑒1(𝑥, 𝑦) ≥ 𝑡
d. Edge detection:
In this approach segmentation of images are done based on intensity change, using first or
second order derivatives. It is used to find out the boundary of the object. (Image3(x, y)).
The gradient is denoted as,
∇𝐼𝑚𝑎𝑔𝑒1 ≅ 𝑔𝑟𝑎𝑑(𝐼𝑚𝑎𝑔𝑒1) ≅ [
𝐼𝑚𝑎𝑔𝑒2 𝑥
𝐼𝑚𝑎𝑔𝑒2 𝑦
] = [
𝛿 𝐼𝑚𝑎𝑔𝑒1
𝛿 𝑥
𝛿 𝐼𝑚𝑎𝑔𝑒1
𝛿 𝑥
]
The magnitude M(x,y) of vector ∇𝐼𝑚𝑎𝑔𝑒1 ≅ 𝑔𝑟𝑎𝑑(𝐼𝑚𝑎𝑔𝑒 1) is defined as,
𝑀(𝑥, 𝑦) = 𝑚𝑎𝑔(∇𝐼𝑚𝑎𝑔𝑒1) = √𝐼𝑚𝑎𝑔𝑒22 𝑥 + 𝐼𝑚𝑎𝑔𝑒22 𝑦
The gradient image is denoted as,
∝ (𝑥, 𝑦) = tan−1
[
𝐼𝑚𝑎𝑔𝑒2
𝑦
𝐼𝑚𝑎𝑔𝑒2 𝑥
]
e. Sobel edge detection:
It is edge detection technique that uses the following filter masks,
[
1 0 −1
2 0 −2
1 0 −1
] and [
1 2 1
0 0 0
−1 −2 −1
]
f. Region of Interest (ROI):
It is a required focused region or boundaries of an object over a sample space of dataset
that fulfills the aim of an experiment. (Image4(x, y))
g. Smoothing of image:
Using low pass filters, noise is reduced by smoothing the image. Some of the low pass
filters are based on average values (mean filters) and others median values (median filters).
7. h. Gaussian low- pass filter:
Gaussian low pass filter or Gaussian blur is a smoothing technique that gives Gaussian
function as impulse response. (Image5(x, y)). In 2D the equation:
𝐺𝑙𝑝𝑓(𝑥, 𝑦) =
1
2𝜋𝛾2
𝑒
−(
𝑥2+𝑦2
2𝜎2 )
Where, σ is the standard deviation
i. Normalization of image:
Normalization of image is a technique, which uses histogram stretching or contrast
stretching, where the range of pixel intensity value changes.
Image5 (x,y):
𝐼𝑚𝑎𝑔𝑒5(𝑥, 𝑦) = 𝑀 𝑛 + √(𝐼𝑚𝑎𝑔𝑒5(𝑥, 𝑦) − 𝑀)2 ∗
𝑉𝑛
𝑉
Else:
𝐼𝑚𝑎𝑔𝑒6(𝑥, 𝑦) = 𝑀 𝑛 + √(𝐼𝑚𝑎𝑔𝑒5(𝑥, 𝑦) − 𝑀)2 ∗
𝑉𝑛
𝑉
Where, M is the image mean, V is the image variance Mn is set to 100 and Vn is set 225.
j. Morphological operations:
A technique that analyses and processes on geometrical structures, called “structuring
element”. This small template is positioned and compared at every possible location in
image with the corresponding neighboring pixels based on the operations “hit” or “fit”.
k. Erosion:
The minute details are removed by “erosion” from the image, with reducing the size of
ROI. The boundaries can be determined by subtracting the eroded image from the original
one.
l. Dilation:
“Dilation” has the complimentary effect of erosion. It adds up a layer in between the inner
and outer layer of pixels. Hence, it helps in filling up the void or holes.
m. Local thresholding:
This is corresponding to an image, where the pixels are dominated into two mode,
background and foreground. A threshold value that helps in separating the pixels acts as a
8. decision factor. If the intensity point lesser than threshold, accepted as the object point else
if greater than equals to the threshold is the background point.
𝐼𝑚𝑎𝑔𝑒9(𝑥, 𝑦) = {
0, 𝑖𝑓 𝐼𝑚𝑎𝑔𝑒10(𝑥, 𝑦) ≥ 𝑡
1, 𝑖𝑓 𝐼𝑚𝑎𝑔𝑒10(𝑥, 𝑦) < 𝑡
n. Hit- or miss- transform:
To obtain how the object are related to their surrounding in a binary image, the hit- or miss-
transform is used. It requires two identical or similar structuring elements that analyses the
inner and outer objects in the image.
Resulting images in all the stages of image segmentation are sequentially mapped into the
following:
a. Figure 2.4 original image: Image1(x, y),
b. Figure 2.5 global thresholding: Image2(x, y),
c. Figure 2.6 edge detection: Image3(x, y),
d. Figure 2.7 selection of ROI: Image4(x,y),
e. Figure 2.8 smoothening of the image using Gaussian low pass filter: Image5(x,y),
f. Figure 2.9 normalization of the image: Image6(x,y),
g. Figure 2.10 morphological dilation: Image7(x,y),
h. Figure 2.11 local thresholding image: Image8(x,y),
i. Figure 2.12 thinning: Image9(x,y),
j. Figure 2.13 connecting broken lines using dilation: Image10(x,y),
k. Figure 2.14 complimentary binary image: Image11(x,y).
Fig. 2.4: Original Image Fig. 2.5: Global Thresholding
9. Fig. 2.6: Edge Detection Fig. 2.7: Selection of ROI
Fig. 2.8: Smoothening using Gaussian low pass filter Fig. 2.9: Normalization
Fig. 2.10: Morphological Dilation Fig. 2.11: Local Thresholding
Fig. 2.12: Thinning Fig. 2.13: Connecting broken lines
10. Fig. 2.14: Complimentary Binary Image
Once the image is extracted, it is matched with the images present in the registered
database, to get the final output.
Fig 2.15: Personal identification methods, using vein patterns from palm and wrist.
In HRM, there are many occurrences where HR decision depends on a variety of factors,
such as knowledge, human experience and judgment. These factors can be cause of
inaccurate, inconsistent, unfair and unanticipated decisions. For this reason, data mining
techniques may be used for its reasoning characteristics. Expert system incorporates human
expert knowledge in its knowledge-based component. It is able to mimic the decision
ability of human expert to help them with their routine tasks, even their absence. Figure
Image Reading Pre-Processing Normalization
Post-Processing
Matching
Registration
Vein Extraction
Binary Image
Output
DB
11. 2.16 shows that an IDSS incorporates many AI techniques in its working steps on the basis
of problem nature.
Fig. 2.16: Working steps of IDSS
3. Research Methodology
Repeated Line Tracking Algorithm with Template Matching
Feature Extraction Algorithm consists of the following 5 steps:
a) Determine the start point and the moving-direction attributes for line tracking.
b) Determine the motion of the tracking point and the direction of the dark line.
c) Update the locus space as many time as the points been tracked.
d) Repeat Step a to Step c, N times.
e) Obtain the vein pattern from the locus space.
INTELLIGENT
DECISION SUPPORT
SYSTEM
SITUATION
ASSESSMENT
KDD
New knowledge
pattern
DECISION
MODELING
Automatic Neural
Network, Fuzzy
Logic, Expert
System
Suggested Decision
EXPECTANCY
FORECASTING
Automatic Neural
Network
Forecasted data
12. Matching: For matching technique, the vein pattern data is converted into matching data
and are compared with the registered data. The two widely used line shaped pattern
matching techniques are: Structural matching and Template matching are widely used line
shaped pattern matching. In case of structural matching we use: line ending and bifurcation.
When there are less feature points then we use template matching, which is based on
comparison of pixel values, suitable to segment vein images.
Maximum Curvature Points with Template Matching
Feature Extraction Algorithm includes 3 steps:
a. Extract the Veins’ center positions.
b. Connect the obtained center positions.
c. Label the image.
Matching: Similarly, the obtained pattern is converted into matching data and then matched
with the registered data. To provide any HR related report and to suggest solutions of
structured, semi-structured and unstructured HR problems and making it available to users,
the intelligence based HRIS model consists of three segments: input subsystem, decision
making subsystem, and output subsystems. Figure 3 illustrates the proposed i-HRIS model
for HR functionalities. The description of the suggested model is presented below:
0
Fig. 3: i-HRIS model for HR functionalities
INPUT SUB-SYSTEM
EXTERNAL SOURCES
EXTERNAL SOURCES
HRIS
INTERNAL SOURCES
TRANSCATION PROCESSING
SYSTEM
DATABASE
MANAGEMENT
SYSTEM
PERSONAL
DATABASE
PAYROLL DB
PERFORMANCE
EVALUATION DB
KNOWLEDGE
MANAGEMENT
SYSTEM
DATA MART Data
Warehouse
DECISION PROCESSING SUB-
SYSTEM
OUTPUT SUB-SYSTEM
Strategic
HR
Planning
Module
Payroll Module
Performance
Evaluation
Module
Compensations
and Benefit
Module
Employee
Service Module
13. i. Input Subsystems:
HRIS input subsystems consist of Transaction Processing Subsystem (TPS) and HR
Intelligence Subsystem. The input subsystems section takes HR related data into
operational database that transform input data into the mandatory format for storage. This
part also includes software or other external databases that transform. So once an employee
punched into the organization, TPS gathers it as data into database, and transform it into
useful information such as the employee’s salary, post titles, employee work history etc.
HR intelligent subsystem acts an interface and obtains intelligent data from commercial
databases, related to stakeholders, financial institutions, labour union etc., for a smooth
generation of monthly payment.
ii. Decision Making Subsystems:
For decision making process, a KDD can help to extract knowledge from old data and
decisions by different instruments and techniques including Big-Data Mining, OLAP, and
AI techniques.
iii. Output Subsystems:
The output subsystem consists a variety of models that could provide reports or solutions
or flexible suggestions for HR problems and help solving complex, imprecise and ill-
structured problems by decision making subsystem.
Performance Evaluation Module: This module is very complex, with many rules for each
criterion with different priority, hence fuzzy rule base decision making approach is
considered, which offers a total performance index, considering all criteria, for an
employee.
Compensations and Benefits Module: In this module, HR IDSS may use ANN, for
statistical and financial models to estimate the amount of compensation and benefits
(insurance, pension, profit-sharing, and stock option, and other benefits).
Payroll Interface Module: This module will be developed using financial or accounting
models for timely and accurate structured decision of payments and may incorporate the
information necessary to calculate attendance, any leaves of absences (paid or unpaid),
vacation time, and any other events that interrupted service. It will include information on
salary, wages, and benefits.
4. Analysis and discussion on future trends
With the evolution of technology world, there are more trends and demand in the field of
identity management. The need for a more accurate and secure way of identifying an
individual, gives rise to all these trends and demands and to gain competitive advantage,
one must adopt to these. Few of the future trends in payroll technology, are discussed
below.
14. Mobile Biometric Technology
Biometric human identification can’t always be performed in a controlled office
environment. It might be required, at times, to go in public venues. To speed up the
identification process effectively and efficiently under these situations, mobile biometrics
can be used. The biometric functionality can be achieved on a mobile device either through
its built-in biometric sensors or by attaching portable biometric hardware to it via a USB
cable or through a Wi-Fi connection.
Cloud Based Biometric Solutions
This trend is mainly driven by mobile biometric technology. Instead of saving the biometric
data locally, sending it to the cloud is a safer solution, plus, pairing the mobile biometric
device with a cloud based biometric solution can speed up the identification process even
more.
Biometric Single Sign on (SSO)
As many companies are adopting biometric single sign on over traditional passwords to
secure their networks from data breaches and to minimize password management costs,
this stands as the most popular debates in the current scenario, whether biometrics will
replace passwords. Passwords are vulnerable because: they can be guessed, forgotten,
shared or swapped. On the other hand, biometrics are unique, hard to spoof, and one cannot
lose or share them.
In cases where employees in an organization must log into multiple databases and have
different passwords for each of them which needs to be reset periodically, it can be very
frustrating and may lead to decrease in productivity as well. Hence, with the
implementation of a complete biometric single sign on, the employees no longer have to
remember passwords without threatening the security of the network. (Recent trends in
biometric technology, n.d.)
5. Conclusion
To overcome translation, rotation and scale variance in contact-free palm-vein images, we
propose a robust palm-vein recognition approach. We use the entire palm region for vein
recognition, which not only gains more vein information and reduces complexity but also
decreases restriction of hand posture during registration and authentication. Hence, aids in
genuine registration of attendance of the person. Finally, the obtained biometric data is
stored in the database, processed to fetch the details of any particular employee and
analyzed to track the work history, compensation allowance, performance bonus etc. to
calculate the salary of the employee.
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