�ݺ�ߣshows by User: faimin

�ݺ�ߣshows by User: faimin / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: faimin / Fri, 21 Oct 2016 15:19:25 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: faimin Windows 7 /slideshow/windows-7-67505917/67505917 windowss7-161021151925
INTRODUCTION KEY FEATURES SECURITY EDITIONS ADVANTAGES]]>
INTRODUCTION KEY FEATURES SECURITY EDITIONS ADVANTAGES]]> Fri, 21 Oct 2016 15:19:25 GMT /slideshow/windows-7-67505917/67505917 faimin@slideshare.net(faimin) Windows 7 faimin INTRODUCTION KEY FEATURES SECURITY EDITIONS ADVANTAGES <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/windowss7-161021151925-thumbnail.jpg?width=120&height=120&fit=bounds" /> INTRODUCTION KEY FEATURES SECURITY EDITIONS ADVANTAGES

Windows 7 from Faimin Khan

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0 The Nist definition of cloud computing cloud computing Research Paper /slideshow/the-nist-definition-of-cloud-computing-cloud-computing-research-paper/67505658 thenistdefinitionofcloudcomputingcloudcomputingisamodelforenablingubiquitous-161021151428
CLOUD COMPUTING DEFINATIONS ADVANTAGES USES ]]>
CLOUD COMPUTING DEFINATIONS ADVANTAGES USES ]]> Fri, 21 Oct 2016 15:14:28 GMT /slideshow/the-nist-definition-of-cloud-computing-cloud-computing-research-paper/67505658 faimin@slideshare.net(faimin) The Nist definition of cloud computing cloud computing Research Paper faimin CLOUD COMPUTING DEFINATIONS ADVANTAGES USES <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thenistdefinitionofcloudcomputingcloudcomputingisamodelforenablingubiquitous-161021151428-thumbnail.jpg?width=120&height=120&fit=bounds" /> CLOUD COMPUTING DEFINATIONS ADVANTAGES USES

The Nist definition of cloud computing cloud computing Research Paper from Faimin Khan

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0 LEADERSHIP STYLES BASED ON EVENT. /slideshow/leadership-styles-based-on-event/67505552 assign2-161021151115
LEADERSHIP STYLES AND CHALLENGES IN OUR EVENT]]>
LEADERSHIP STYLES AND CHALLENGES IN OUR EVENT]]> Fri, 21 Oct 2016 15:11:15 GMT /slideshow/leadership-styles-based-on-event/67505552 faimin@slideshare.net(faimin) LEADERSHIP STYLES BASED ON EVENT. faimin LEADERSHIP STYLES AND CHALLENGES IN OUR EVENT <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/assign2-161021151115-thumbnail.jpg?width=120&height=120&fit=bounds" /> LEADERSHIP STYLES AND CHALLENGES IN OUR EVENT

LEADERSHIP STYLES BASED ON EVENT. from Faimin Khan

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0 VISIT TO AN ORPHANAGE(SOCIAL ACTIVITY) /slideshow/visit-to-an-orphanagesocial-activity/67505353 spmevent1-161021150537
With a view of engaging in a social cause on the eve of Engineer’s Day, we wish to visit to Anjuman-E-Mufidul Yatama Orphanage on the 6th of October, 2016. It is also part of our S. P. M. assignment; it is like a small project for us which focuses on social area. For this excursion, we have planned to distribute snacks, clothes, books along with some stationary things that can be useful for them, and conduct fun activities for the kids. A motivational lecture for the kids is also in our itinerary.]]>
With a view of engaging in a social cause on the eve of Engineer’s Day, we wish to visit to Anjuman-E-Mufidul Yatama Orphanage on the 6th of October, 2016. It is also part of our S. P. M. assignment; it is like a small project for us which focuses on social area. For this excursion, we have planned to distribute snacks, clothes, books along with some stationary things that can be useful for them, and conduct fun activities for the kids. A motivational lecture for the kids is also in our itinerary.]]> Fri, 21 Oct 2016 15:05:37 GMT /slideshow/visit-to-an-orphanagesocial-activity/67505353 faimin@slideshare.net(faimin) VISIT TO AN ORPHANAGE(SOCIAL ACTIVITY) faimin With a view of engaging in a social cause on the eve of Engineer’s Day, we wish to visit to Anjuman-E-Mufidul Yatama Orphanage on the 6th of October, 2016. It is also part of our S. P. M. assignment; it is like a small project for us which focuses on social area. For this excursion, we have planned to distribute snacks, clothes, books along with some stationary things that can be useful for them, and conduct fun activities for the kids. A motivational lecture for the kids is also in our itinerary. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/spmevent1-161021150537-thumbnail.jpg?width=120&height=120&fit=bounds" /> With a view of engaging in a social cause on the eve of Engineer’s Day, we wish to visit to Anjuman-E-Mufidul Yatama Orphanage on the 6th of October, 2016. It is also part of our S. P. M. assignment; it is like a small project for us which focuses on social area. For this excursion, we have planned to distribute snacks, clothes, books along with some stationary things that can be useful for them, and conduct fun activities for the kids. A motivational lecture for the kids is also in our itinerary.

VISIT TO AN ORPHANAGE(SOCIAL ACTIVITY) from Faimin Khan

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0 BigData in Health Care Systems with IOT /slideshow/bigdata-in-health-care-systems-with-iot/67505336 bigdatawithi11-161021150513
Nowadays Big Data is playing very important role in Day-to-Day life from Social Network to Educaion,From Banking to Business then Why not in healthcare. Mobile phones, sensors, patients, hospitals, researchers, providers and organizations are nowadays generating huge amounts of healthcare data. The real challenge in healthcare systems is how to find, collect, analyze and manage information to make people's lives healthier and easier By contributing not only to understand new diseases and therapies but also to predict outcomes at earlier stages and make real-time decisions.]]>
Nowadays Big Data is playing very important role in Day-to-Day life from Social Network to Educaion,From Banking to Business then Why not in healthcare. Mobile phones, sensors, patients, hospitals, researchers, providers and organizations are nowadays generating huge amounts of healthcare data. The real challenge in healthcare systems is how to find, collect, analyze and manage information to make people's lives healthier and easier By contributing not only to understand new diseases and therapies but also to predict outcomes at earlier stages and make real-time decisions.]]> Fri, 21 Oct 2016 15:05:13 GMT /slideshow/bigdata-in-health-care-systems-with-iot/67505336 faimin@slideshare.net(faimin) BigData in Health Care Systems with IOT faimin Nowadays Big Data is playing very important role in Day-to-Day life from Social Network to Educaion,From Banking to Business then Why not in healthcare. Mobile phones, sensors, patients, hospitals, researchers, providers and organizations are nowadays generating huge amounts of healthcare data. The real challenge in healthcare systems is how to find, collect, analyze and manage information to make people's lives healthier and easier By contributing not only to understand new diseases and therapies but also to predict outcomes at earlier stages and make real-time decisions��. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdatawithi11-161021150513-thumbnail.jpg?width=120&height=120&fit=bounds" /> Nowadays Big Data is playing very important role in Day-to-Day life from Social Network to Educaion,From Banking to Business then Why not in healthcare. Mobile phones, sensors, patients, hospitals, researchers, providers and organizations are nowadays generating huge amounts of healthcare data. The real challenge in healthcare systems is how to find, collect, analyze and manage information to make people's lives healthier and easier By contributing not only to understand new diseases and therapies but also to predict outcomes at earlier stages and make real-time decisions��.

BigData in Health Care Systems with IOT from Faimin Khan

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0 Biometric Signature Recognization /slideshow/biometric-signature-recognization/67504854 signaturerecognization-161021145222
INTRO: Nowadays, person identification (recognition) and verification is very important in security and resource access control. Biometrics is the science of automatic recognition of individual depending on their physiological and behavioral attributes. For centuries, handwritten signatures have been an integral part of validating business transaction contracts and agreements. Among the different forms of biometric recognition systems such as fingerprint, iris, face, voice, palm etc., signature will be most widely used. SIGNATURE RECOGNITION Signature Recognition is the procedure of determining to whom a particular signature belongs to. Depending on acquiring of signature images, there are two types of signature recognition systems: Online Signature Recognition Offline Signature Recognition STEPS IMAGE ACQUSITION Collection of signatures from 50 persons on blank paper. The collected signatures are scanned to get images in JPG format to create database. PREPROCESSING Image pre-processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications. The techniques for preprocessing used are RGB to Gray Scale Conversion Binarization Thinning Bounding Box FEATURE EXTRACTION Features are the characters to be extracted from the processed image. It has used two feature techniques Global Features Grid Features DWT After applying DWT to all 9 blocks, each block is divided into horizontal, vertical and diagonal components. From each components two features mainly horizontal and vertical projection positions are extracted. Total 54 (9 x 3 x 2) features are extracted. Grid features extracted from each block are Horizontal Projection Position Vertical Projection Position Algorithm for Training phase Description: Retrieval of a signature image from a database Input: Training sample images. Output: Construction of Back Propagation Neural Network. Begin Read the training samples images Step1: Pre-processing Convert the image into gray scale image. Convert the gray scale image into binary image. Apply thinning process. Apply bounding box. Step 2: Features Extracted. Step 3: Back propagation neural network training. end // end of proposed algorithm ]]>
INTRO: Nowadays, person identification (recognition) and verification is very important in security and resource access control. Biometrics is the science of automatic recognition of individual depending on their physiological and behavioral attributes. For centuries, handwritten signatures have been an integral part of validating business transaction contracts and agreements. Among the different forms of biometric recognition systems such as fingerprint, iris, face, voice, palm etc., signature will be most widely used. SIGNATURE RECOGNITION Signature Recognition is the procedure of determining to whom a particular signature belongs to. Depending on acquiring of signature images, there are two types of signature recognition systems: Online Signature Recognition Offline Signature Recognition STEPS IMAGE ACQUSITION Collection of signatures from 50 persons on blank paper. The collected signatures are scanned to get images in JPG format to create database. PREPROCESSING Image pre-processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications. The techniques for preprocessing used are RGB to Gray Scale Conversion Binarization Thinning Bounding Box FEATURE EXTRACTION Features are the characters to be extracted from the processed image. It has used two feature techniques Global Features Grid Features DWT After applying DWT to all 9 blocks, each block is divided into horizontal, vertical and diagonal components. From each components two features mainly horizontal and vertical projection positions are extracted. Total 54 (9 x 3 x 2) features are extracted. Grid features extracted from each block are Horizontal Projection Position Vertical Projection Position Algorithm for Training phase Description: Retrieval of a signature image from a database Input: Training sample images. Output: Construction of Back Propagation Neural Network. Begin Read the training samples images Step1: Pre-processing Convert the image into gray scale image. Convert the gray scale image into binary image. Apply thinning process. Apply bounding box. Step 2: Features Extracted. Step 3: Back propagation neural network training. end // end of proposed algorithm ]]> Fri, 21 Oct 2016 14:52:21 GMT /slideshow/biometric-signature-recognization/67504854 faimin@slideshare.net(faimin) Biometric Signature Recognization faimin INTRO: Nowadays, person identification (recognition) and verification is very important in security and resource access control. Biometrics is the science of automatic recognition of individual depending on their physiological and behavioral attributes. For centuries, handwritten signatures have been an integral part of validating business transaction contracts and agreements. Among the different forms of biometric recognition systems such as fingerprint, iris, face, voice, palm etc., signature will be most widely used. SIGNATURE RECOGNITION Signature Recognition is the procedure of determining to whom a particular signature belongs to. Depending on acquiring of signature images, there are two types of signature recognition systems: Online Signature Recognition Offline Signature Recognition STEPS IMAGE ACQUSITION Collection of signatures from 50 persons on blank paper. The collected signatures are scanned to get images in JPG format to create database. PREPROCESSING Image pre-processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications. The techniques for preprocessing used are RGB to Gray Scale Conversion Binarization Thinning Bounding Box FEATURE EXTRACTION Features are the characters to be extracted from the processed image. It has used two feature techniques Global Features Grid Features DWT After applying DWT to all 9 blocks, each block is divided into horizontal, vertical and diagonal components. From each components two features mainly horizontal and vertical projection positions are extracted. Total 54 (9 x 3 x 2) features are extracted. Grid features extracted from each block are Horizontal Projection Position Vertical Projection Position Algorithm for Training phase Description: Retrieval of a signature image from a database Input: Training sample images. Output: Construction of Back Propagation Neural Network. Begin Read the training samples images Step1: Pre-processing Convert the image into gray scale image. Convert the gray scale image into binary image. Apply thinning process. Apply bounding box. Step 2: Features Extracted. Step 3: Back propagation neural network training. end // end of proposed algorithm <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/signaturerecognization-161021145222-thumbnail.jpg?width=120&height=120&fit=bounds" /> INTRO: Nowadays, person identification (recognition) and verification is very important in security and resource access control. Biometrics is the science of automatic recognition of individual depending on their physiological and behavioral attributes. For centuries, handwritten signatures have been an integral part of validating business transaction contracts and agreements. Among the different forms of biometric recognition systems such as fingerprint, iris, face, voice, palm etc., signature will be most widely used. SIGNATURE RECOGNITION Signature Recognition is the procedure of determining to whom a particular signature belongs to. Depending on acquiring of signature images, there are two types of signature recognition systems: Online Signature Recognition Offline Signature Recognition STEPS IMAGE ACQUSITION Collection of signatures from 50 persons on blank paper. The collected signatures are scanned to get images in JPG format to create database. PREPROCESSING Image pre-processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications. The techniques for preprocessing used are RGB to Gray Scale Conversion Binarization Thinning Bounding Box FEATURE EXTRACTION Features are the characters to be extracted from the processed image. It has used two feature techniques Global Features Grid Features DWT After applying DWT to all 9 blocks, each block is divided into horizontal, vertical and diagonal components. From each components two features mainly horizontal and vertical projection positions are extracted. Total 54 (9 x 3 x 2) features are extracted. Grid features extracted from each block are Horizontal Projection Position Vertical Projection Position Algorithm for Training phase Description: Retrieval of a signature image from a database Input: Training sample images. Output: Construction of Back Propagation Neural Network. Begin Read the training samples images Step1: Pre-processing Convert the image into gray scale image. Convert the gray scale image into binary image. Apply thinning process. Apply bounding box. Step 2: Features Extracted. Step 3: Back propagation neural network training. end // end of proposed algorithm

Biometric Signature Recognization from Faimin Khan

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0 Air Cargo transport /slideshow/air-cargo-transport/67504300 72aircargotransport-161021144122
STRIPS STanford Research Institute Problem Solver A restricted language for planning that describes actions and descriptions of objects in a system Example Action: Buy(x) Precondition: At(p), Sells(p, x) Effect: Have(x) Only positive literals in states: Poor  Unknown Closed world assumption: Unmentioned literals are false Effect P  ¬Q: Add P and delete Q notations CARGO: C1 AND C2 PLANES: P1 AND P2 AIRPORTS: CLE AND LAS PROBLEM Init(At(C1, CLE) ^At(C2, LAS) ^ At(P1, CLE) ^At(P2, LAS) ^ Cargo(C1) ^Cargo(C2) ^Plane(P1) ^Plane(P2) ^ Airport(CLE) ^Airport(LAS)) Goal( At(C1, LAS) At(C2, CLE)) ACTIONS Action( Load(c, p, a), Precond: At(c, a) ^At(p, a) ^Cargo(c) ^ Plane(p) ^ Airport(a) Effect: ¬ At(c, a) ^In(c, p)) Action( Fly( p, from, to), Precond: At(p, from) ^ Plane(p) ^Airport(from) ^ Airport(to) Effect: ¬ At(p, from) ^ At(p, to)) Action( Unload(c, p, a), Precond: In(c, p) ^ At( p, a) ^Cargo(c) ^Plane(p) ^ Airport(a) Effect: At(c, a) ^ ¬ In(c, p)) ]]>
STRIPS STanford Research Institute Problem Solver A restricted language for planning that describes actions and descriptions of objects in a system Example Action: Buy(x) Precondition: At(p), Sells(p, x) Effect: Have(x) Only positive literals in states: Poor  Unknown Closed world assumption: Unmentioned literals are false Effect P  ¬Q: Add P and delete Q notations CARGO: C1 AND C2 PLANES: P1 AND P2 AIRPORTS: CLE AND LAS PROBLEM Init(At(C1, CLE) ^At(C2, LAS) ^ At(P1, CLE) ^At(P2, LAS) ^ Cargo(C1) ^Cargo(C2) ^Plane(P1) ^Plane(P2) ^ Airport(CLE) ^Airport(LAS)) Goal( At(C1, LAS) At(C2, CLE)) ACTIONS Action( Load(c, p, a), Precond: At(c, a) ^At(p, a) ^Cargo(c) ^ Plane(p) ^ Airport(a) Effect: ¬ At(c, a) ^In(c, p)) Action( Fly( p, from, to), Precond: At(p, from) ^ Plane(p) ^Airport(from) ^ Airport(to) Effect: ¬ At(p, from) ^ At(p, to)) Action( Unload(c, p, a), Precond: In(c, p) ^ At( p, a) ^Cargo(c) ^Plane(p) ^ Airport(a) Effect: At(c, a) ^ ¬ In(c, p)) ]]> Fri, 21 Oct 2016 14:41:22 GMT /slideshow/air-cargo-transport/67504300 faimin@slideshare.net(faimin) Air Cargo transport faimin STRIPS STanford Research Institute Problem Solver A restricted language for planning that describes actions and descriptions of objects in a system Example Action: Buy(x) Precondition: At(p), Sells(p, x) Effect: Have(x) Only positive literals in states: Poor  Unknown Closed world assumption: Unmentioned literals are false Effect P  ¬Q: Add P and delete Q notations CARGO: C1 AND C2 PLANES: P1 AND P2 AIRPORTS: CLE AND LAS PROBLEM Init(At(C1, CLE) ^At(C2, LAS) ^ At(P1, CLE) ^At(P2, LAS) ^ Cargo(C1) ^Cargo(C2) ^Plane(P1) ^Plane(P2) ^ Airport(CLE) ^Airport(LAS)) Goal( At(C1, LAS) At(C2, CLE)) ACTIONS Action( Load(c, p, a), Precond: At(c, a) ^At(p, a) ^Cargo(c) ^ Plane(p) ^ Airport(a) Effect: ¬ At(c, a) ^In(c, p)) Action( Fly( p, from, to), Precond: At(p, from) ^ Plane(p) ^Airport(from) ^ Airport(to) Effect: ¬ At(p, from) ^ At(p, to)) Action( Unload(c, p, a), Precond: In(c, p) ^ At( p, a) ^Cargo(c) ^Plane(p) ^ Airport(a) Effect: At(c, a) ^ ¬ In(c, p)) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/72aircargotransport-161021144122-thumbnail.jpg?width=120&height=120&fit=bounds" /> STRIPS STanford Research Institute Problem Solver A restricted language for planning that describes actions and descriptions of objects in a system Example Action: Buy(x) Precondition: At(p), Sells(p, x) Effect: Have(x) Only positive literals in states: Poor  Unknown Closed world assumption: Unmentioned literals are false Effect P  ¬Q: Add P and delete Q notations CARGO: C1 AND C2 PLANES: P1 AND P2 AIRPORTS: CLE AND LAS PROBLEM Init(At(C1, CLE) ^At(C2, LAS) ^ At(P1, CLE) ^At(P2, LAS) ^ Cargo(C1) ^Cargo(C2) ^Plane(P1) ^Plane(P2) ^ Airport(CLE) ^Airport(LAS)) Goal( At(C1, LAS) At(C2, CLE)) ACTIONS Action( Load(c, p, a), Precond: At(c, a) ^At(p, a) ^Cargo(c) ^ Plane(p) ^ Airport(a) Effect: ¬ At(c, a) ^In(c, p)) Action( Fly( p, from, to), Precond: At(p, from) ^ Plane(p) ^Airport(from) ^ Airport(to) Effect: ¬ At(p, from) ^ At(p, to)) Action( Unload(c, p, a), Precond: In(c, p) ^ At( p, a) ^Cargo(c) ^Plane(p) ^ Airport(a) Effect: At(c, a) ^ ¬ In(c, p))

Air Cargo transport from Faimin Khan

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0 https://cdn.slidesharecdn.com/profile-photo-faimin-48x48.jpg?cb=1523647616 @bout mie ... i can sae, wiD ranDom Ppl eM sHy,wId Ma fRienD's Em cRazzie!! . . iF eM Ur BesTiez .. i WiLl mAke u LauGh in mA preSeNce, bUt u'll deFinaTely cRy in Ma AbseNce... I hate people talking to me when i have my headphones on!! I <3 MUSIC !! ____________.♥. ____________.♥♫♥.____________.♥. * ____________.♥♫♥♫.______-.♥♫♥. * ____________.♥♫♥♫♥._-.♥♫♥♫. * _____________.♥♫♥♫♥♫♥♫♥♫♥. * ______-.♥♫♥♫♥♫♥♫♥♫♥♫♥. * _.♥♫♥♫♥♫♥♥♫♥♫♥♫♥♥♫♥♫♥. * . * ______-.♥♫♥♫♥♫♥♫♥♫♥♫♥. * . * . * .. ___________.♥♫♥♫♥♫♥♫♥♫♥. * . * https://cdn.slidesharecdn.com/ss_thumbnails/windowss7-161021151925-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/windows-7-67505917/67505917 Windows 7 https://cdn.slidesharecdn.com/ss_thumbnails/thenistdefinitionofcloudcomputingcloudcomputingisamodelforenablingubiquitous-161021151428-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/the-nist-definition-of-cloud-computing-cloud-computing-research-paper/67505658 The Nist definition of... https://cdn.slidesharecdn.com/ss_thumbnails/assign2-161021151115-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/leadership-styles-based-on-event/67505552 LEADERSHIP STYLES BASE...