�ݺ�ߣshows by User: sansuthi

�ݺ�ߣshows by User: sansuthi / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: sansuthi / Tue, 10 Dec 2019 15:24:43 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: sansuthi Object Oriented Programming -- Dr Robert Harle /slideshow/object-oriented-programming-dr-robert-harle/204086383 oop-191210152443
This course absorbs what was “Programming Methods” and provides a more formal look at Object Oriented programming with an emphasis on Java Four Parts --- 1. Computer Fundamentals 2. Object-Oriented Concepts 3. The Java Platform 4. Design Patterns and OOP design examples]]>
This course absorbs what was “Programming Methods” and provides a more formal look at Object Oriented programming with an emphasis on Java Four Parts --- 1. Computer Fundamentals 2. Object-Oriented Concepts 3. The Java Platform 4. Design Patterns and OOP design examples]]> Tue, 10 Dec 2019 15:24:43 GMT /slideshow/object-oriented-programming-dr-robert-harle/204086383 sansuthi@slideshare.net(sansuthi) Object Oriented Programming -- Dr Robert Harle sansuthi This course absorbs what was “Programming Methods” and provides a more formal look at Object Oriented programming with an emphasis on Java Four Parts --- 1. Computer Fundamentals 2. Object-Oriented Concepts 3. The Java Platform 4. Design Patterns and OOP design examples <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/oop-191210152443-thumbnail.jpg?width=120&height=120&fit=bounds" /> This course absorbs what was “Programming Methods” and provides a more formal look at Object Oriented programming with an emphasis on Java Four Parts --- 1. Computer Fundamentals 2. Object-Oriented Concepts 3. The Java Platform 4. Design Patterns and OOP design examples

Object Oriented Programming -- Dr Robert Harle from suthi

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0 THE ROLE OF EDGE COMPUTING IN INTERNET OF THINGS /slideshow/the-role-of-edge-computing-in-internet-of-things/199385417 theroleofedgecomputingininternetofthings-pages-2-13-pages-1-10-191129160133
Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing.]]>
Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing.]]> Fri, 29 Nov 2019 16:01:32 GMT /slideshow/the-role-of-edge-computing-in-internet-of-things/199385417 sansuthi@slideshare.net(sansuthi) THE ROLE OF EDGE COMPUTING IN INTERNET OF THINGS sansuthi Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/theroleofedgecomputingininternetofthings-pages-2-13-pages-1-10-191129160133-thumbnail.jpg?width=120&height=120&fit=bounds" /> Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing.

THE ROLE OF EDGE COMPUTING IN INTERNET OF THINGS from suthi

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0 EDGE COMPUTING: VISION AND CHALLENGES /slideshow/edge-computing-vision-and-challenges/199380191 edge-computing-191129154742
The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction. Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing.]]>
The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction. Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing.]]> Fri, 29 Nov 2019 15:47:42 GMT /slideshow/edge-computing-vision-and-challenges/199380191 sansuthi@slideshare.net(sansuthi) EDGE COMPUTING: VISION AND CHALLENGES sansuthi The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction. Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/edge-computing-191129154742-thumbnail.jpg?width=120&height=120&fit=bounds" /> The proliferation of Internet of Things (IoT) and the success of rich cloud services have pushed the horizon of a new computing paradigm, edge computing, which calls for processing the data at the edge of the network. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. In this paper, we introduce the definition of edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing. Finally, we present several challenges and opportunities in the field of edge computing, and hope this paper will gain attention from the community and inspire more research in this direction. Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet is the edge between a mobile device and cloud. The rationale of edge computing is that computing should happen at the proximity of data sources. From our point of view, edge computing is interchangeable with fog computing, but edge computing focus more toward the things side, while fog computing focus more on the infrastructure side. Edge computing could have as big an impact on our society as has the cloud computing.

EDGE COMPUTING: VISION AND CHALLENGES from suthi

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0 Document Classification Using KNN with Fuzzy Bags of Word Representation /slideshow/document-classification-using-knn-with-fuzzy-bags-of-word-representation/196025946 f03240376s19-191121133928
Abstract — Text classification is used to classify the documents depending on the words, phrases and word combinations according to the declared syntaxes. There are many applications that are using text classification such as artificial intelligence, to maintain the data according to the category and in many other. Some keywords which are called topics are selected to classify the given document. Using these Topics the main idea of the document can be identified. Selecting the Topics is an important task to classify the document according to the category. In this proposed system keywords are extracted from documents using TF-IDF and Word Net. TF-IDF algorithm is mainly used to select the important words by which document can be classified. Word Net is mainly used to find similarity between these candidate words. The words which are having the maximum similarity are considered as Topics(keywords). In this experiment we used TF-IDF model to find the similar words so that to classify the document. Decision tree algorithm gives the better accuracy for text classification when compared to other algorithms fuzzy system to classify text written in natural language according to topic. It is necessary to use a fuzzy classifier for this task, due to the fact that a given text can cover several topics with different degrees. In this context, traditional classifiers are inappropriate, as they attempt to sort each text in a single class in a winner-takes-all fashion. The classifier we proposeautomatically learns its fuzzy rules from training examples. We have applied it to classify news articles, and the results we obtained are promising. The dimensionality of a vector is very important in text classification. We can decrease this dimensionality by using clustering based on fuzzy logic. Depending on the similarity we can classify the document and thus they can be formed into clusters according to their Topics. After formation of clusters one can easily access the documents and save the documents very easily. In this we can find the similarity and summarize the words called Topics which can be used to classify the Documents.]]>
Abstract — Text classification is used to classify the documents depending on the words, phrases and word combinations according to the declared syntaxes. There are many applications that are using text classification such as artificial intelligence, to maintain the data according to the category and in many other. Some keywords which are called topics are selected to classify the given document. Using these Topics the main idea of the document can be identified. Selecting the Topics is an important task to classify the document according to the category. In this proposed system keywords are extracted from documents using TF-IDF and Word Net. TF-IDF algorithm is mainly used to select the important words by which document can be classified. Word Net is mainly used to find similarity between these candidate words. The words which are having the maximum similarity are considered as Topics(keywords). In this experiment we used TF-IDF model to find the similar words so that to classify the document. Decision tree algorithm gives the better accuracy for text classification when compared to other algorithms fuzzy system to classify text written in natural language according to topic. It is necessary to use a fuzzy classifier for this task, due to the fact that a given text can cover several topics with different degrees. In this context, traditional classifiers are inappropriate, as they attempt to sort each text in a single class in a winner-takes-all fashion. The classifier we proposeautomatically learns its fuzzy rules from training examples. We have applied it to classify news articles, and the results we obtained are promising. The dimensionality of a vector is very important in text classification. We can decrease this dimensionality by using clustering based on fuzzy logic. Depending on the similarity we can classify the document and thus they can be formed into clusters according to their Topics. After formation of clusters one can easily access the documents and save the documents very easily. In this we can find the similarity and summarize the words called Topics which can be used to classify the Documents.]]> Thu, 21 Nov 2019 13:39:27 GMT /slideshow/document-classification-using-knn-with-fuzzy-bags-of-word-representation/196025946 sansuthi@slideshare.net(sansuthi) Document Classification Using KNN with Fuzzy Bags of Word Representation sansuthi Abstract — Text classification is used to classify the documents depending on the words, phrases and word combinations according to the declared syntaxes. There are many applications that are using text classification such as artificial intelligence, to maintain the data according to the category and in many other. Some keywords which are called topics are selected to classify the given document. Using these Topics the main idea of the document can be identified. Selecting the Topics is an important task to classify the document according to the category. In this proposed system keywords are extracted from documents using TF-IDF and Word Net. TF-IDF algorithm is mainly used to select the important words by which document can be classified. Word Net is mainly used to find similarity between these candidate words. The words which are having the maximum similarity are considered as Topics(keywords). In this experiment we used TF-IDF model to find the similar words so that to classify the document. Decision tree algorithm gives the better accuracy for text classification when compared to other algorithms fuzzy system to classify text written in natural language according to topic. It is necessary to use a fuzzy classifier for this task, due to the fact that a given text can cover several topics with different degrees. In this context, traditional classifiers are inappropriate, as they attempt to sort each text in a single class in a winner-takes-all fashion. The classifier we proposeautomatically learns its fuzzy rules from training examples. We have applied it to classify news articles, and the results we obtained are promising. The dimensionality of a vector is very important in text classification. We can decrease this dimensionality by using clustering based on fuzzy logic. Depending on the similarity we can classify the document and thus they can be formed into clusters according to their Topics. After formation of clusters one can easily access the documents and save the documents very easily. In this we can find the similarity and summarize the words called Topics which can be used to classify the Documents. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/f03240376s19-191121133928-thumbnail.jpg?width=120&height=120&fit=bounds" /> Abstract — Text classification is used to classify the documents depending on the words, phrases and word combinations according to the declared syntaxes. There are many applications that are using text classification such as artificial intelligence, to maintain the data according to the category and in many other. Some keywords which are called topics are selected to classify the given document. Using these Topics the main idea of the document can be identified. Selecting the Topics is an important task to classify the document according to the category. In this proposed system keywords are extracted from documents using TF-IDF and Word Net. TF-IDF algorithm is mainly used to select the important words by which document can be classified. Word Net is mainly used to find similarity between these candidate words. The words which are having the maximum similarity are considered as Topics(keywords). In this experiment we used TF-IDF model to find the similar words so that to classify the document. Decision tree algorithm gives the better accuracy for text classification when compared to other algorithms fuzzy system to classify text written in natural language according to topic. It is necessary to use a fuzzy classifier for this task, due to the fact that a given text can cover several topics with different degrees. In this context, traditional classifiers are inappropriate, as they attempt to sort each text in a single class in a winner-takes-all fashion. The classifier we proposeautomatically learns its fuzzy rules from training examples. We have applied it to classify news articles, and the results we obtained are promising. The dimensionality of a vector is very important in text classification. We can decrease this dimensionality by using clustering based on fuzzy logic. Depending on the similarity we can classify the document and thus they can be formed into clusters according to their Topics. After formation of clusters one can easily access the documents and save the documents very easily. In this we can find the similarity and summarize the words called Topics which can be used to classify the Documents.

Document Classification Using KNN with Fuzzy Bags of Word Representation from suthi

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0 AUTOMATA THEORY - SHORT NOTES /slideshow/automata-theory-187206717/187206717 alc-191026143958
Short Notes on Automata Theory Automata theory is the study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science and discrete mathematics (a subject of study in both mathematics and computer science). The word automata (the plural of automaton) comes from the Greek word αὐτόματα, which means "self-making".]]>
Short Notes on Automata Theory Automata theory is the study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science and discrete mathematics (a subject of study in both mathematics and computer science). The word automata (the plural of automaton) comes from the Greek word αὐτόματα, which means "self-making".]]> Sat, 26 Oct 2019 14:39:58 GMT /slideshow/automata-theory-187206717/187206717 sansuthi@slideshare.net(sansuthi) AUTOMATA THEORY - SHORT NOTES sansuthi Short Notes on Automata Theory Automata theory is the study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science and discrete mathematics (a subject of study in both mathematics and computer science). The word automata (the plural of automaton) comes from the Greek word αὐτόματα, which means "self-making". <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/alc-191026143958-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Notes on Automata Theory Automata theory is the study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science and discrete mathematics (a subject of study in both mathematics and computer science). The word automata (the plural of automaton) comes from the Greek word αὐτόματα, which means "self-making".

AUTOMATA THEORY - SHORT NOTES from suthi

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0 OBJECT ORIENTED PROGRAMMING LANGUAGE - SHORT NOTES /slideshow/object-oriented-programming-language/187205206 oopl-191026143338
Short Notes on OOP Object-oriented programming (OOP) is a programming paradigm based on the concept of "objects", which can contain data, in the form of fields (often known as attributes or properties), and code, in the form of procedures (often known as methods). A feature of objects is an object's procedures that can access and often modify the data fields of the object with which they are associated (objects have a notion of "this" or "self"). In OOP, computer programs are designed by making them out of objects that interact with one another. OOP languages are diverse, but the most popular ones are class-based, meaning that objects are instances of classes, which also determine their types.]]>
Short Notes on OOP Object-oriented programming (OOP) is a programming paradigm based on the concept of "objects", which can contain data, in the form of fields (often known as attributes or properties), and code, in the form of procedures (often known as methods). A feature of objects is an object's procedures that can access and often modify the data fields of the object with which they are associated (objects have a notion of "this" or "self"). In OOP, computer programs are designed by making them out of objects that interact with one another. OOP languages are diverse, but the most popular ones are class-based, meaning that objects are instances of classes, which also determine their types.]]> Sat, 26 Oct 2019 14:33:37 GMT /slideshow/object-oriented-programming-language/187205206 sansuthi@slideshare.net(sansuthi) OBJECT ORIENTED PROGRAMMING LANGUAGE - SHORT NOTES sansuthi Short Notes on OOP Object-oriented programming (OOP) is a programming paradigm based on the concept of "objects", which can contain data, in the form of fields (often known as attributes or properties), and code, in the form of procedures (often known as methods). A feature of objects is an object's procedures that can access and often modify the data fields of the object with which they are associated (objects have a notion of "this" or "self"). In OOP, computer programs are designed by making them out of objects that interact with one another. OOP languages are diverse, but the most popular ones are class-based, meaning that objects are instances of classes, which also determine their types. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/oopl-191026143338-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Notes on OOP Object-oriented programming (OOP) is a programming paradigm based on the concept of "objects", which can contain data, in the form of fields (often known as attributes or properties), and code, in the form of procedures (often known as methods). A feature of objects is an object's procedures that can access and often modify the data fields of the object with which they are associated (objects have a notion of "this" or "self"). In OOP, computer programs are designed by making them out of objects that interact with one another. OOP languages are diverse, but the most popular ones are class-based, meaning that objects are instances of classes, which also determine their types.

OBJECT ORIENTED PROGRAMMING LANGUAGE - SHORT NOTES from suthi

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0 PARALLEL ARCHITECTURE AND COMPUTING - SHORT NOTES /slideshow/parallel-architecture-and-computing/187204293 paa-191026142927
Short Notes on Parallel Computing Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time.]]>
Short Notes on Parallel Computing Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time.]]> Sat, 26 Oct 2019 14:29:27 GMT /slideshow/parallel-architecture-and-computing/187204293 sansuthi@slideshare.net(sansuthi) PARALLEL ARCHITECTURE AND COMPUTING - SHORT NOTES sansuthi Short Notes on Parallel Computing Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/paa-191026142927-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Notes on Parallel Computing Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time.

PARALLEL ARCHITECTURE AND COMPUTING - SHORT NOTES from suthi

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0 SOFTWARE QUALITY ASSURANCE AND TESTING - SHORT NOTES /slideshow/software-quality-assurance-and-testing-186676094/186676094 sqat-191025082928
Short Notes on Software Testing]]>
Short Notes on Software Testing]]> Fri, 25 Oct 2019 08:29:27 GMT /slideshow/software-quality-assurance-and-testing-186676094/186676094 sansuthi@slideshare.net(sansuthi) SOFTWARE QUALITY ASSURANCE AND TESTING - SHORT NOTES sansuthi Short Notes on Software Testing <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sqat-191025082928-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Notes on Software Testing

SOFTWARE QUALITY ASSURANCE AND TESTING - SHORT NOTES from suthi

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0 COMPUTER HARDWARE - SHORT NOTES /slideshow/computer-hardware-186673795/186673795 hw-191025082414
Short Notes on Computer Hardware & Troubleshooting]]>
Short Notes on Computer Hardware & Troubleshooting]]> Fri, 25 Oct 2019 08:24:14 GMT /slideshow/computer-hardware-186673795/186673795 sansuthi@slideshare.net(sansuthi) COMPUTER HARDWARE - SHORT NOTES sansuthi Short Notes on Computer Hardware & Troubleshooting <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hw-191025082414-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Notes on Computer Hardware & Troubleshooting

COMPUTER HARDWARE - SHORT NOTES from suthi

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0 DATA BASE MANAGEMENT SYSTEM - SHORT NOTES /slideshow/data-base-management-system-186671661/186671661 dbms-191025081933
Short Notes on DBMS]]>
Short Notes on DBMS]]> Fri, 25 Oct 2019 08:19:33 GMT /slideshow/data-base-management-system-186671661/186671661 sansuthi@slideshare.net(sansuthi) DATA BASE MANAGEMENT SYSTEM - SHORT NOTES sansuthi Short Notes on DBMS <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dbms-191025081933-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Notes on DBMS

DATA BASE MANAGEMENT SYSTEM - SHORT NOTES from suthi

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0 OPERATING SYSTEM - SHORT NOTES /slideshow/operating-system-186669635/186669635 os-191025081534
Short Notes on Operating System]]>
Short Notes on Operating System]]> Fri, 25 Oct 2019 08:15:34 GMT /slideshow/operating-system-186669635/186669635 sansuthi@slideshare.net(sansuthi) OPERATING SYSTEM - SHORT NOTES sansuthi Short Notes on Operating System <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/os-191025081534-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Notes on Operating System

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0 SOFTWARE ENGINEERING & ARCHITECTURE - SHORT NOTES /sansuthi/software-engineering-architecture se-191025081233
Short Notes on Software Engineering]]>
Short Notes on Software Engineering]]> Fri, 25 Oct 2019 08:12:33 GMT /sansuthi/software-engineering-architecture sansuthi@slideshare.net(sansuthi) SOFTWARE ENGINEERING & ARCHITECTURE - SHORT NOTES sansuthi Short Notes on Software Engineering <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/se-191025081233-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Notes on Software Engineering

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0 ALGORITHMS - SHORT NOTES /slideshow/algorithms-186319907/186319907 daa-191024161356
Short Notes on Design & Analysis of Algorithms - DAA]]>
Short Notes on Design & Analysis of Algorithms - DAA]]> Thu, 24 Oct 2019 16:13:56 GMT /slideshow/algorithms-186319907/186319907 sansuthi@slideshare.net(sansuthi) ALGORITHMS - SHORT NOTES sansuthi Short Notes on Design & Analysis of Algorithms - DAA <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/daa-191024161356-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Notes on Design & Analysis of Algorithms - DAA

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0 COMPUTER NETWORKS - SHORT NOTES /slideshow/computer-networks-186318578/186318578 cn-191024161006
Short Notes on Computer Networks]]>
Short Notes on Computer Networks]]> Thu, 24 Oct 2019 16:10:06 GMT /slideshow/computer-networks-186318578/186318578 sansuthi@slideshare.net(sansuthi) COMPUTER NETWORKS - SHORT NOTES sansuthi Short Notes on Computer Networks <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cn-191024161006-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Notes on Computer Networks

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0 DATA STRUCTURES - SHORT NOTES /slideshow/data-structures-186316537/186316537 ds-191024160425
Short Notes on Data Structures]]>
Short Notes on Data Structures]]> Thu, 24 Oct 2019 16:04:25 GMT /slideshow/data-structures-186316537/186316537 sansuthi@slideshare.net(sansuthi) DATA STRUCTURES - SHORT NOTES sansuthi Short Notes on Data Structures <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ds-191024160425-thumbnail.jpg?width=120&height=120&fit=bounds" /> Short Notes on Data Structures

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0 ARTIFICIAL INTELLIGENCE - SHORT NOTES /slideshow/artificial-intelligence-186315410/186315410 ai-191024160132
Artificial Intelligence]]>
Artificial Intelligence]]> Thu, 24 Oct 2019 16:01:32 GMT /slideshow/artificial-intelligence-186315410/186315410 sansuthi@slideshare.net(sansuthi) ARTIFICIAL INTELLIGENCE - SHORT NOTES sansuthi Artificial Intelligence <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ai-191024160132-thumbnail.jpg?width=120&height=120&fit=bounds" /> Artificial Intelligence

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0 LIGHT PEAK /slideshow/light-peak-186107111/186107111 59569561-intel-light-peak-white-paper-0910-191024043802
INTEL Light Peak Technology Overview]]>
INTEL Light Peak Technology Overview]]> Thu, 24 Oct 2019 04:38:02 GMT /slideshow/light-peak-186107111/186107111 sansuthi@slideshare.net(sansuthi) LIGHT PEAK sansuthi INTEL Light Peak Technology Overview <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/59569561-intel-light-peak-white-paper-0910-191024043802-thumbnail.jpg?width=120&height=120&fit=bounds" /> INTEL Light Peak Technology Overview

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0 Action Recognition using Nonnegative Action /slideshow/action-recognition-using-nonnegative-action/186103900 actionrecognitionusingnonnegativeaction-191024042900
Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection - Haoran Wang, Chunfeng Yuan, Weiming Hu, Haibin Ling, Wankou Yang, and Changyin Sun]]>
Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection - Haoran Wang, Chunfeng Yuan, Weiming Hu, Haibin Ling, Wankou Yang, and Changyin Sun]]> Thu, 24 Oct 2019 04:29:00 GMT /slideshow/action-recognition-using-nonnegative-action/186103900 sansuthi@slideshare.net(sansuthi) Action Recognition using Nonnegative Action sansuthi Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection - Haoran Wang, Chunfeng Yuan, Weiming Hu, Haibin Ling, Wankou Yang, and Changyin Sun <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/actionrecognitionusingnonnegativeaction-191024042900-thumbnail.jpg?width=120&height=120&fit=bounds" /> Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection - Haoran Wang, Chunfeng Yuan, Weiming Hu, Haibin Ling, Wankou Yang, and Changyin Sun

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0 C Programming Tutorial /slideshow/c-programming-tutorial-178729368/178729368 cprogrammingtutorial-191003062705
C is a general-purpose, procedural, imperative computer programming language developed in 1972 by Dennis M. Ritchie at the Bell Telephone Laboratories to develop the UNIX operating system. C is the most widely used computer language. It keeps fluctuating at number one scale of popularity along with Java programming language, which is also equally popular and most widely used among modern software programmers.]]>
C is a general-purpose, procedural, imperative computer programming language developed in 1972 by Dennis M. Ritchie at the Bell Telephone Laboratories to develop the UNIX operating system. C is the most widely used computer language. It keeps fluctuating at number one scale of popularity along with Java programming language, which is also equally popular and most widely used among modern software programmers.]]> Thu, 03 Oct 2019 06:27:05 GMT /slideshow/c-programming-tutorial-178729368/178729368 sansuthi@slideshare.net(sansuthi) C Programming Tutorial sansuthi C is a general-purpose, procedural, imperative computer programming language developed in 1972 by Dennis M. Ritchie at the Bell Telephone Laboratories to develop the UNIX operating system. C is the most widely used computer language. It keeps fluctuating at number one scale of popularity along with Java programming language, which is also equally popular and most widely used among modern software programmers. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cprogrammingtutorial-191003062705-thumbnail.jpg?width=120&height=120&fit=bounds" /> C is a general-purpose, procedural, imperative computer programming language developed in 1972 by Dennis M. Ritchie at the Bell Telephone Laboratories to develop the UNIX operating system. C is the most widely used computer language. It keeps fluctuating at number one scale of popularity along with Java programming language, which is also equally popular and most widely used among modern software programmers.

C Programming Tutorial from suthi

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0 Data structure - mcqs /sansuthi/data-structure-mcqs data-structure-mcqs-191001155304
MCQ on Data Structures]]>
MCQ on Data Structures]]> Tue, 01 Oct 2019 15:53:04 GMT /sansuthi/data-structure-mcqs sansuthi@slideshare.net(sansuthi) Data structure - mcqs sansuthi MCQ on Data Structures <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/data-structure-mcqs-191001155304-thumbnail.jpg?width=120&height=120&fit=bounds" /> MCQ on Data Structures

Data structure - mcqs from suthi

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0 https://cdn.slidesharecdn.com/profile-photo-sansuthi-48x48.jpg?cb=1629043217 https://cdn.slidesharecdn.com/ss_thumbnails/oop-191210152443-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/object-oriented-programming-dr-robert-harle/204086383 Object Oriented Progra... https://cdn.slidesharecdn.com/ss_thumbnails/theroleofedgecomputingininternetofthings-pages-2-13-pages-1-10-191129160133-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-role-of-edge-computing-in-internet-of-things/199385417 THE ROLE OF EDGE COMPU... https://cdn.slidesharecdn.com/ss_thumbnails/edge-computing-191129154742-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/edge-computing-vision-and-challenges/199380191 EDGE COMPUTING: VISION...