International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient Framework for Predicting and Recommending M-Commerce Patterns Ba...Editor IJCATR
1) The document proposes a framework for predicting and recommending mobile commerce patterns using a graph diffusion method.
2) It constructs a graph based on items purchased by mobile users and ranks the items based on transactions to analyze user behavior and recommend ranked items.
3) This framework is said to produce more efficient and accurate item recommendations than previous frameworks by overcoming problems with high false positive rates and efficiency.
Designing an Agent for Information Extraction from Persian E-shopsTELKOMNIKA JOURNAL
E-shops are among the most conventional applications of Electronic Commerce. In these shops,
the buyers search for their goods through key words or classifications and read the product description
provided by the sellers. Though, when the number of items is high, this gets to be difficult for the users. On
the one hand, there are too many e-shops, and browsing in these shops to find the best and most
appropriate goods is a difficult and time-consuming process. On the other hand, product descriptions are
not the same in different websites, and there are different product forms. This study investigates about
products and sellers in various websites based on the conditions and user requirements through software
agents which present the extracted information in the form of a table to the users which enables them to
compare prices and each seller’s conditions without spending too much time for browsing. Using this
method increases precision and recall indices comparing to a conventional user browsing
This paper presents a method based on principle of content based image retrieval for online shopping based on color, HSV aiming at efficient retrieval of images from the large database for online shopping specially for fashion shopping. Here, HSV modeling is used for creating our application with a huge image database, which compares image source with the destination components. In this paper, a technique is used for finding items by image search, which is convenient for buyers in order to allow them to see the products. The reason for using image search for items instead of text searches is that item searching by keywords or text has some issues such as errors in search items, expansion in search and inaccuracy in search results. This paper is an attempt to help users to choose the best options among many products and decide exactly what they want with the fast and easy search by image retrieval. This technology is providing a new search mode, searching by image, which will help buyers for finding the same or similar image retrieval in the database store. The image searching results have been made customers buy products quickly. This feature is implemented to identify and extract features of prominent object present in an image. Using different statistical measures, similarity measures are calculated and evaluated. Image retrieval based on color is a trivial task. Identifying objects of prominence in an image and retrieving image with similar features is a complex task. Finding prominent object in an image is difficult in a background image and is the challenging task in retrieving images. We calculated and change the region of interest in order to increase speed of operation as well as accuracy by masking the background content. The Implementation results proved that proposed method is effective in recalling the images of same pattern or texture.
IRJET- E-Commerce Recommendation based on Users Rating DataIRJET Journal
This document proposes a recommendation system for e-commerce called SBT-Rec that is based on structural balance theory. It aims to address challenges with sparse user rating data where traditional collaborative filtering may not find similar users or items. SBT-Rec first identifies a target user's "enemies" or opposite preferences, then determines "possible friends" according to the rule that "an enemy of my enemy is my friend". It recommends items preferred by these possible friends. It also identifies "possibly similar items" for a target user's preferred items using the same rule. The document outlines the SBT-Rec algorithm and describes its architecture which collects data, performs recommendation, and provides a user interface.
Bipartite Recommender Algorithm for RBTS - IESL 2016 Final ReviewedAsoka Korale
This document proposes a novel recommender algorithm that uses association rules to discover characteristics intrinsic to content for making recommendations. It operates without using metadata to characterize content, but instead employs the idea that certain items can represent a larger set. Association rule discovery is used to show that ring back tone songs that group together exhibit similarities at a fundamental level. This allows the songs to be broadly categorized using selected songs as representative categories. The algorithm provides a more accurate match between customers and content preferences by analyzing song content at an intrinsic level and recommending songs based on inherent properties, without relying solely on metadata.
IRJET - Visual E-Commerce Application using Deep LearningIRJET Journal
This document discusses developing a visual e-commerce application using deep learning for object recognition. It proposes a system with three phases: 1) scanning real-time objects using an Android app and YOLO algorithm for object detection, 2) detecting the objects, and 3) recommending similar products to the user. The goal is to make e-commerce searching more user-friendly by allowing image-based searches rather than relying solely on text. This could help illiterate users and improve the shopping experience overall. The document reviews related work on object recognition techniques and visual search engines to provide context.
IRJET- 3D Virtual Reality for Shopping MallIRJET Journal
This document describes a 3D virtual reality shopping mall that aims to provide an immersive online shopping experience. It discusses how the virtual mall was developed using VRML and ASP.NET to allow users to navigate a 3D environment and shop at virtual stores from the comfort of their home. The system logs users into an avatar and allows them to walk around the mall, examine 360-degree views of products, add items to a cart, and make purchases. The document outlines the development methodology, system architecture, user and product profiles, marketing strategies, and concludes that this type of virtual reality shopping could provide a more engaging online shopping experience than traditional e-commerce websites.
The document discusses using data mining techniques in e-commerce. It provides an introduction to data mining and e-commerce, describing common data mining tasks like classification, clustering, and association rule mining. The document outlines the basic data mining process and some popular data mining tools. It explains how data mining can be used in e-commerce for applications like customer profiling, personalization, basket analysis, sales forecasting, and market segmentation. The advantages of using data mining in e-commerce are also summarized.
An Efficient Framework for Predicting and Recommending M-Commerce Patterns Ba...Editor IJCATR
1) The document proposes a framework for predicting and recommending mobile commerce patterns using a graph diffusion method.
2) It constructs a graph based on items purchased by mobile users and ranks the items based on transactions to analyze user behavior and recommend ranked items.
3) This framework is said to produce more efficient and accurate item recommendations than previous frameworks by overcoming problems with high false positive rates and efficiency.
Designing an Agent for Information Extraction from Persian E-shopsTELKOMNIKA JOURNAL
E-shops are among the most conventional applications of Electronic Commerce. In these shops,
the buyers search for their goods through key words or classifications and read the product description
provided by the sellers. Though, when the number of items is high, this gets to be difficult for the users. On
the one hand, there are too many e-shops, and browsing in these shops to find the best and most
appropriate goods is a difficult and time-consuming process. On the other hand, product descriptions are
not the same in different websites, and there are different product forms. This study investigates about
products and sellers in various websites based on the conditions and user requirements through software
agents which present the extracted information in the form of a table to the users which enables them to
compare prices and each seller’s conditions without spending too much time for browsing. Using this
method increases precision and recall indices comparing to a conventional user browsing
This paper presents a method based on principle of content based image retrieval for online shopping based on color, HSV aiming at efficient retrieval of images from the large database for online shopping specially for fashion shopping. Here, HSV modeling is used for creating our application with a huge image database, which compares image source with the destination components. In this paper, a technique is used for finding items by image search, which is convenient for buyers in order to allow them to see the products. The reason for using image search for items instead of text searches is that item searching by keywords or text has some issues such as errors in search items, expansion in search and inaccuracy in search results. This paper is an attempt to help users to choose the best options among many products and decide exactly what they want with the fast and easy search by image retrieval. This technology is providing a new search mode, searching by image, which will help buyers for finding the same or similar image retrieval in the database store. The image searching results have been made customers buy products quickly. This feature is implemented to identify and extract features of prominent object present in an image. Using different statistical measures, similarity measures are calculated and evaluated. Image retrieval based on color is a trivial task. Identifying objects of prominence in an image and retrieving image with similar features is a complex task. Finding prominent object in an image is difficult in a background image and is the challenging task in retrieving images. We calculated and change the region of interest in order to increase speed of operation as well as accuracy by masking the background content. The Implementation results proved that proposed method is effective in recalling the images of same pattern or texture.
IRJET- E-Commerce Recommendation based on Users Rating DataIRJET Journal
This document proposes a recommendation system for e-commerce called SBT-Rec that is based on structural balance theory. It aims to address challenges with sparse user rating data where traditional collaborative filtering may not find similar users or items. SBT-Rec first identifies a target user's "enemies" or opposite preferences, then determines "possible friends" according to the rule that "an enemy of my enemy is my friend". It recommends items preferred by these possible friends. It also identifies "possibly similar items" for a target user's preferred items using the same rule. The document outlines the SBT-Rec algorithm and describes its architecture which collects data, performs recommendation, and provides a user interface.
Bipartite Recommender Algorithm for RBTS - IESL 2016 Final ReviewedAsoka Korale
This document proposes a novel recommender algorithm that uses association rules to discover characteristics intrinsic to content for making recommendations. It operates without using metadata to characterize content, but instead employs the idea that certain items can represent a larger set. Association rule discovery is used to show that ring back tone songs that group together exhibit similarities at a fundamental level. This allows the songs to be broadly categorized using selected songs as representative categories. The algorithm provides a more accurate match between customers and content preferences by analyzing song content at an intrinsic level and recommending songs based on inherent properties, without relying solely on metadata.
IRJET - Visual E-Commerce Application using Deep LearningIRJET Journal
This document discusses developing a visual e-commerce application using deep learning for object recognition. It proposes a system with three phases: 1) scanning real-time objects using an Android app and YOLO algorithm for object detection, 2) detecting the objects, and 3) recommending similar products to the user. The goal is to make e-commerce searching more user-friendly by allowing image-based searches rather than relying solely on text. This could help illiterate users and improve the shopping experience overall. The document reviews related work on object recognition techniques and visual search engines to provide context.
IRJET- 3D Virtual Reality for Shopping MallIRJET Journal
This document describes a 3D virtual reality shopping mall that aims to provide an immersive online shopping experience. It discusses how the virtual mall was developed using VRML and ASP.NET to allow users to navigate a 3D environment and shop at virtual stores from the comfort of their home. The system logs users into an avatar and allows them to walk around the mall, examine 360-degree views of products, add items to a cart, and make purchases. The document outlines the development methodology, system architecture, user and product profiles, marketing strategies, and concludes that this type of virtual reality shopping could provide a more engaging online shopping experience than traditional e-commerce websites.
The document discusses using data mining techniques in e-commerce. It provides an introduction to data mining and e-commerce, describing common data mining tasks like classification, clustering, and association rule mining. The document outlines the basic data mining process and some popular data mining tools. It explains how data mining can be used in e-commerce for applications like customer profiling, personalization, basket analysis, sales forecasting, and market segmentation. The advantages of using data mining in e-commerce are also summarized.
BUSINESS DIARY - An Interactive and Intelligent Platform for SME’srahulmonikasharma
There are currently many online trading platforms in the Internet. However, they have various drawbacks and are not welcome by sellers who just want a simple and yet intelligent and user-friendly platform for reaching buyers. Traditional methods are costly and not suitable for small and medium enterprises. This project focuses on creating such a platform that allows sellers to reach buyers efficiently. With advent of several successful e-commerce web portals like amazon.com, ebay.com, flipkart.com etc., world is witnessing an explosion of service providers as well as service consumers. The approach is to attain beneficial flow for end users who are looking for services which match their custom requirements rather that best (and hence more costly) service.This idea is focused on the development of web application to facilitate such a need with an aim to providing an intelligent user-interface to both the sellers and the buyers.
A novel approach to dynamic profiling of E-customers considering click stream...IJECEIAES
In this paper, we present an approach for mining change in customer’s behavior for the purpose of maintaining robust profiling model over time. Most of previous studies leave important questions unanswered: In developing B2C e-commerce strategies, how do managers implicitly load customer’s profiles based on their satisfaction over the online store characteristics? And: What kind of feedback segments do they have? Our proposed approach does not force customers to explicitly express their preference information over the online service but rather capture their preference from their online activities. The challenge does not only lay in analyzing how customer’s classifier model change and when it does so but also to adapt it to the customer’s click stream data using a new decision tree generation algorithm which takes as inputs new set of variables; categorical, continuous and fuzzy variables. Customer’s online reviews rates are considered as classes. Experiments show that this work performed well in identifying relevant customer’s stream data to judge the chinese e-commerce website “Tmall”. The extracted values of the website’s features are also useful to identifying the satisfaction level when the customer’s rate is not available.
A Hybrid Procreative –Discriminative Based Hashing Methodrahulmonikasharma
Hashing method is the one of the main method for searching same and different images based on hash code.For capturing similarities between textual, visual and cross media information; a hashing approaches have been proven. To address these challenges, in this paper we propose semantic level cross media hashing (SCMH) and deep belief network (DBN) is for a co-relation between different modalities.
Linking Behavioral Patterns to Personal Attributes through Data Re-Miningertekg
Download Link >https://ertekprojects.com/gurdal-ertek-publications/blog/linking-behavioral-patterns-to-personal-attributes-through-data-re-mining/
A fundamental challenge in behavioral informatics is the development of methodologies and systems that can achieve its goals and tasks, including be-havior pattern analysis. This study presents such a methodology, that can be con-verted into a decision support system, by the appropriate integration of existing tools for association mining and graph visualization. The methodology enables the linking of behavioral patterns to personal attributes, through the re-mining of colored association graphs that represent item associations. The methodology is described and mathematically formalized, and is demonstrated in a case study related with retail industry.
Customer Clustering Based on Customer Purchasing Sequence DataIJERA Editor
Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer purchasing sequence data. This paper considers the customer clustering problem in the context of customer purchasing sequence data. However, two major aspects distinguish this paper from past research: (1) in our model, a customer sequence contains itemsets, which is a more realistic configuration than previous models, which assume a customer sequence would merely consist of items; and (2) in our model, a customer may belong to multiple clusters or no cluster, whereas in existing models a customer is limited to only one cluster. The second difference implies that each cluster discovered using our model represents a crucial type of customer behavior and that a customer can exhibit several types of behavior simultaneously. Finally, extensive experiments are conducted through a retail data set, and the results show that the clusters obtained by our model can provide more accurate descriptions of customer purchasing behaviors.
This document outlines a study on consumer perceptions, buying behavior, and loyalty towards internet service providers (ISPs) in India's National Capital Region. The study aims to examine customer perceptions of major ISPs, identify factors influencing purchasing decisions, and analyze differences in customer segments. Primary data will be collected through an online survey of NCR customers and analyzed using SPSS, Excel, ANOVA, chi-squared, and t-tests to quantify the relationships between ISP perceptions and customer behavior.
This document summarizes and compares various algorithms used to implement video surveillance systems, including pixel matching, image matching, and clustering algorithms. It first provides background on video surveillance systems and their need for automatic abnormal motion detection. It then reviews several specific algorithms: pixel matching, agglomerative clustering, reciprocal nearest neighbor pairing, sub-pixel mapping, patch matching, tone mapping, and k-means clustering. For each algorithm, it provides a brief overview of the approach and complexity. The document also discusses image matching algorithms like classic image checking, pixel-based identity checking, and pixel-based similarity checking. Overall, the document analyzes algorithms that can be used to detect and classify motion in video surveillance systems.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document analyzes the performance of the AODV routing protocol under wormhole attacks in a mobile ad hoc network (MANET) simulation using NS2. It first provides background on MANETs, AODV, and wormhole attacks. It then describes the NS2 simulation setup of 16 nodes using the AODV protocol with and without two malicious nodes creating a wormhole. The results show that without a wormhole, packets are successfully delivered to the destination, but with the wormhole, zero throughput is achieved as the malicious nodes drop all packets, preventing delivery to the destination. Therefore, wormhole attacks significantly disrupt routing in AODV-based MANETs.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document presents a new topology for a cascaded multilevel inverter powered by a photovoltaic system. The proposed system uses a high frequency transformer to generate the DC bus voltage for an auxiliary inverter from the main inverter's DC bus. This reduces the number of isolated DC sources needed by half, lowering costs. A natural balancing of voltages between the main and auxiliary inverters is achieved through the transformer turns ratio, simplifying control. The system was simulated using static loads to validate the control scheme.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses the design and implementation of a modified Booth multiplier on an FPGA. It begins with an introduction to fixed-width multipliers and the truncation error that occurs. It then describes how the partial product matrix of a Booth multiplier can be modified to reduce this error. The rest of the document details the implementation, including the modified Booth encoder and decoder, generation of partial products, shifting of partial products, two's complement arithmetic, addition of partial products, and comparison of the modified Booth multiplier to a standard multiplier in terms of complexity, power consumption, and delay.
This document summarizes research on congestion and fairness issues in wireless mesh networks. The researchers found that:
1) Wireless mesh networks using CSMA/CA MAC protocols can experience "starvation", where one-hop flows receive most bandwidth while competing multi-hop flows receive almost nothing.
2) Through experiments on an operational urban mesh network, they confirmed starvation occurs and isolated that only a one-hop TCP flow coupled with a two-hop TCP flow is needed to induce it.
3) They developed an analytical model to understand the causes of starvation as the interaction of MAC-layer biases, congestion control loops, and penalties of switching between network states.
4) Their model suggests a "
This document proposes implementing a product Reed-Solomon code on an FPGA chip for a NAND flash memory controller to correct errors. It discusses using a (255,223) product Reed-Solomon code with two shortened RS codes arranged column-wise and one conventional RS code arranged row-wise. This structure allows correcting multiple random and burst errors. The proposed coding scheme is tested on an FPGA simulator and can correct up to 16 symbol errors, providing lower decoding complexity than BCH codes commonly used for NAND flash memories.
This document summarizes the categorization of clay deposits in the Federal Capital Territory of Abuja, Nigeria. Samples were collected from three locations - Sheda, Abaji, and Karimu - and tested to determine their chemical composition and properties. The chemical analysis showed that all samples contained high percentages of silica and alumina, classifying them as alumino-silicates. Their properties were also measured, such as specific gravity, density, porosity, and were found to be within internationally accepted ranges. The refractoriness of over 1300°C indicates the samples could be used as insulating materials.
The document describes the design and simulation of a rectangular microstrip patch antenna and an I-slotted microstrip patch antenna for wireless communication. The rectangular antenna was designed to operate at 5.3 GHz but had a narrow bandwidth of 88 MHz and gain of 7.1 dBi. An I-slot was then cut into the patch to enhance the bandwidth and gain. The I-slotted antenna achieved a 20.45% increased bandwidth of 106 MHz and higher gain of 7.24 dBi at 5.3 GHz. Simulation results showed the I-slotted antenna had improved performance over the rectangular patch in terms of bandwidth, gain, voltage standing wave ratio, and efficiency. The enhanced antenna could potentially be useful for various
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses the production and characterization of nano-ZnO doped aluminium composites. Nano ZnO powder was synthesized using solution combustion synthesis and characterized using XRD and SEM/EDX. ZnO/Al powder blends with varying ZnO content were fabricated into blocks using powder metallurgy. The microhardness, wear resistance, and corrosion resistance of the blocks were then evaluated. Results showed that microhardness, wear resistance, and corrosion resistance increased with up to 1 wt% ZnO addition. The optimal microhardness and microstructure occurred at 1 wt% ZnO, while the sample with 5 wt% ZnO exhibited best wear resistance.
BUSINESS DIARY - An Interactive and Intelligent Platform for SME’srahulmonikasharma
There are currently many online trading platforms in the Internet. However, they have various drawbacks and are not welcome by sellers who just want a simple and yet intelligent and user-friendly platform for reaching buyers. Traditional methods are costly and not suitable for small and medium enterprises. This project focuses on creating such a platform that allows sellers to reach buyers efficiently. With advent of several successful e-commerce web portals like amazon.com, ebay.com, flipkart.com etc., world is witnessing an explosion of service providers as well as service consumers. The approach is to attain beneficial flow for end users who are looking for services which match their custom requirements rather that best (and hence more costly) service.This idea is focused on the development of web application to facilitate such a need with an aim to providing an intelligent user-interface to both the sellers and the buyers.
A novel approach to dynamic profiling of E-customers considering click stream...IJECEIAES
In this paper, we present an approach for mining change in customer’s behavior for the purpose of maintaining robust profiling model over time. Most of previous studies leave important questions unanswered: In developing B2C e-commerce strategies, how do managers implicitly load customer’s profiles based on their satisfaction over the online store characteristics? And: What kind of feedback segments do they have? Our proposed approach does not force customers to explicitly express their preference information over the online service but rather capture their preference from their online activities. The challenge does not only lay in analyzing how customer’s classifier model change and when it does so but also to adapt it to the customer’s click stream data using a new decision tree generation algorithm which takes as inputs new set of variables; categorical, continuous and fuzzy variables. Customer’s online reviews rates are considered as classes. Experiments show that this work performed well in identifying relevant customer’s stream data to judge the chinese e-commerce website “Tmall”. The extracted values of the website’s features are also useful to identifying the satisfaction level when the customer’s rate is not available.
A Hybrid Procreative –Discriminative Based Hashing Methodrahulmonikasharma
Hashing method is the one of the main method for searching same and different images based on hash code.For capturing similarities between textual, visual and cross media information; a hashing approaches have been proven. To address these challenges, in this paper we propose semantic level cross media hashing (SCMH) and deep belief network (DBN) is for a co-relation between different modalities.
Linking Behavioral Patterns to Personal Attributes through Data Re-Miningertekg
Download Link >https://ertekprojects.com/gurdal-ertek-publications/blog/linking-behavioral-patterns-to-personal-attributes-through-data-re-mining/
A fundamental challenge in behavioral informatics is the development of methodologies and systems that can achieve its goals and tasks, including be-havior pattern analysis. This study presents such a methodology, that can be con-verted into a decision support system, by the appropriate integration of existing tools for association mining and graph visualization. The methodology enables the linking of behavioral patterns to personal attributes, through the re-mining of colored association graphs that represent item associations. The methodology is described and mathematically formalized, and is demonstrated in a case study related with retail industry.
Customer Clustering Based on Customer Purchasing Sequence DataIJERA Editor
Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer purchasing sequence data. This paper considers the customer clustering problem in the context of customer purchasing sequence data. However, two major aspects distinguish this paper from past research: (1) in our model, a customer sequence contains itemsets, which is a more realistic configuration than previous models, which assume a customer sequence would merely consist of items; and (2) in our model, a customer may belong to multiple clusters or no cluster, whereas in existing models a customer is limited to only one cluster. The second difference implies that each cluster discovered using our model represents a crucial type of customer behavior and that a customer can exhibit several types of behavior simultaneously. Finally, extensive experiments are conducted through a retail data set, and the results show that the clusters obtained by our model can provide more accurate descriptions of customer purchasing behaviors.
This document outlines a study on consumer perceptions, buying behavior, and loyalty towards internet service providers (ISPs) in India's National Capital Region. The study aims to examine customer perceptions of major ISPs, identify factors influencing purchasing decisions, and analyze differences in customer segments. Primary data will be collected through an online survey of NCR customers and analyzed using SPSS, Excel, ANOVA, chi-squared, and t-tests to quantify the relationships between ISP perceptions and customer behavior.
This document summarizes and compares various algorithms used to implement video surveillance systems, including pixel matching, image matching, and clustering algorithms. It first provides background on video surveillance systems and their need for automatic abnormal motion detection. It then reviews several specific algorithms: pixel matching, agglomerative clustering, reciprocal nearest neighbor pairing, sub-pixel mapping, patch matching, tone mapping, and k-means clustering. For each algorithm, it provides a brief overview of the approach and complexity. The document also discusses image matching algorithms like classic image checking, pixel-based identity checking, and pixel-based similarity checking. Overall, the document analyzes algorithms that can be used to detect and classify motion in video surveillance systems.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document analyzes the performance of the AODV routing protocol under wormhole attacks in a mobile ad hoc network (MANET) simulation using NS2. It first provides background on MANETs, AODV, and wormhole attacks. It then describes the NS2 simulation setup of 16 nodes using the AODV protocol with and without two malicious nodes creating a wormhole. The results show that without a wormhole, packets are successfully delivered to the destination, but with the wormhole, zero throughput is achieved as the malicious nodes drop all packets, preventing delivery to the destination. Therefore, wormhole attacks significantly disrupt routing in AODV-based MANETs.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document presents a new topology for a cascaded multilevel inverter powered by a photovoltaic system. The proposed system uses a high frequency transformer to generate the DC bus voltage for an auxiliary inverter from the main inverter's DC bus. This reduces the number of isolated DC sources needed by half, lowering costs. A natural balancing of voltages between the main and auxiliary inverters is achieved through the transformer turns ratio, simplifying control. The system was simulated using static loads to validate the control scheme.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses the design and implementation of a modified Booth multiplier on an FPGA. It begins with an introduction to fixed-width multipliers and the truncation error that occurs. It then describes how the partial product matrix of a Booth multiplier can be modified to reduce this error. The rest of the document details the implementation, including the modified Booth encoder and decoder, generation of partial products, shifting of partial products, two's complement arithmetic, addition of partial products, and comparison of the modified Booth multiplier to a standard multiplier in terms of complexity, power consumption, and delay.
This document summarizes research on congestion and fairness issues in wireless mesh networks. The researchers found that:
1) Wireless mesh networks using CSMA/CA MAC protocols can experience "starvation", where one-hop flows receive most bandwidth while competing multi-hop flows receive almost nothing.
2) Through experiments on an operational urban mesh network, they confirmed starvation occurs and isolated that only a one-hop TCP flow coupled with a two-hop TCP flow is needed to induce it.
3) They developed an analytical model to understand the causes of starvation as the interaction of MAC-layer biases, congestion control loops, and penalties of switching between network states.
4) Their model suggests a "
This document proposes implementing a product Reed-Solomon code on an FPGA chip for a NAND flash memory controller to correct errors. It discusses using a (255,223) product Reed-Solomon code with two shortened RS codes arranged column-wise and one conventional RS code arranged row-wise. This structure allows correcting multiple random and burst errors. The proposed coding scheme is tested on an FPGA simulator and can correct up to 16 symbol errors, providing lower decoding complexity than BCH codes commonly used for NAND flash memories.
This document summarizes the categorization of clay deposits in the Federal Capital Territory of Abuja, Nigeria. Samples were collected from three locations - Sheda, Abaji, and Karimu - and tested to determine their chemical composition and properties. The chemical analysis showed that all samples contained high percentages of silica and alumina, classifying them as alumino-silicates. Their properties were also measured, such as specific gravity, density, porosity, and were found to be within internationally accepted ranges. The refractoriness of over 1300°C indicates the samples could be used as insulating materials.
The document describes the design and simulation of a rectangular microstrip patch antenna and an I-slotted microstrip patch antenna for wireless communication. The rectangular antenna was designed to operate at 5.3 GHz but had a narrow bandwidth of 88 MHz and gain of 7.1 dBi. An I-slot was then cut into the patch to enhance the bandwidth and gain. The I-slotted antenna achieved a 20.45% increased bandwidth of 106 MHz and higher gain of 7.24 dBi at 5.3 GHz. Simulation results showed the I-slotted antenna had improved performance over the rectangular patch in terms of bandwidth, gain, voltage standing wave ratio, and efficiency. The enhanced antenna could potentially be useful for various
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses the production and characterization of nano-ZnO doped aluminium composites. Nano ZnO powder was synthesized using solution combustion synthesis and characterized using XRD and SEM/EDX. ZnO/Al powder blends with varying ZnO content were fabricated into blocks using powder metallurgy. The microhardness, wear resistance, and corrosion resistance of the blocks were then evaluated. Results showed that microhardness, wear resistance, and corrosion resistance increased with up to 1 wt% ZnO addition. The optimal microhardness and microstructure occurred at 1 wt% ZnO, while the sample with 5 wt% ZnO exhibited best wear resistance.
This document discusses several circuit-level techniques for reducing leakage current in cache memories to lower power consumption. It describes gated-Vdd, which uses an extra transistor to gate the supply voltage in unused sections of the cache, reducing leakage through stacking effect. It also covers data retention gated-ground cache, which employs a similar extra transistor between ground and the bitline to gate the supply virtually. Additionally, it discusses drowsy cache, which slightly reduces the supply voltage and threshold voltage of unused sections. Finally, asymmetric SRAM cell design is presented, which uses high-Vth transistors for non-critical paths to lower leakage. Evaluation of these techniques shows trade-offs between leakage reduction and performance impacts like increased read time
This document discusses various techniques for enhancing thermal images, including converting images to grayscale, histogram equalization, filtering, morphology, and fast Fourier transforms (FFT). It provides examples of enhancing thermal images using these techniques and compares the results. Histogram equalization, linear filtering, and morphology were shown to improve image clarity and contrast. FFT transforms the image domain and can be used to obtain a restored image. The techniques allow for extracting useful information from thermal images for applications like quality control, diagnostics, and research.
This document discusses using geographic information systems (GIS) to optimize transportation routes. It provides background on how GIS can be used to determine optimal routes between origins and destinations to minimize travel time and distance. The document then reviews several past studies that used GIS techniques like shortest path algorithms, genetic algorithms, and network analysis to optimize bus routes and emergency vehicle routes. It concludes that GIS is a powerful tool for transportation planning and analysis by allowing transportation networks and schedules to be visualized and optimal routes to be determined quickly and accurately.
La Asociación de Ejecutivos de Ventas y Mercadeo de Puerto Rico (SME) ofrece varios beneficios a sus miembros, incluyendo acceso a eventos educativos, oportunidades de networking, y descuentos en certificaciones y conferencias. Los beneficios proveen más de $11,000 en valor a los miembros regulares. La membresía regular cuesta $450 al año más $100 de cuota de iniciación.
IRJET-User Profile based Behavior Identificaton using Data Mining TechniqueIRJET Journal
This document presents a model for analyzing customer behavior on online shopping sites using data mining techniques. Clickstream data is collected from customers and analyzed to predict shopping behaviors and provide recommendations. The Naive Bayes algorithm is used to classify customers into categories based on likely purchased and viewed product categories. Recommendations are then provided to customers in their predicted interested categories. The model aims to increase sales by understanding customer interests and loyalty to specific product types.
The rapid advance of wireless communication technology and the increasing popularity of powerful portable devices, mobile users not only can access worldwide information from anywhere at any time but also use their mobile devices to make business transactions easily.
IRJET- Real Time Product Price Monitoring & Analysis Application for E-Commer...IRJET Journal
The document proposes a real-time product price monitoring and analysis application for e-commerce websites. It discusses how e-commerce sites can dynamically adjust prices in response to demand changes. The proposed system would allow users to monitor prices of desired products on different websites and notify them when prices meet a specified threshold. It would generate analytical charts and diagrams to help users make informed purchase decisions. Future work may include an automatic purchasing system and business models to increase monetization for large-scale monitoring. The system aims to help regular buyers obtain products at optimal prices.
IRJET- Customer Buying Prediction using Machine-Learning Techniques: A SurveyIRJET Journal
1) The document discusses using machine learning techniques to predict customer purchasing and churn based on their personal and behavioral data.
2) It reviews several machine learning algorithms that have been used for prediction, including random forest, logistic regression, naive bayes, and support vector machines.
3) Deep learning techniques are also discussed, including the use of convolutional neural networks to reveal hidden patterns in customer data and predict purchases and churn.
DEMOGRAPHIC DIVISION OF A MART BY APPLYING CLUSTERING TECHNIQUESIRJET Journal
This document discusses demographic segmentation of customers at a mart by applying clustering techniques. It begins with an abstract that outlines the goal of employing advanced techniques like machine learning to target customer needs and increase sales. The introduction provides context on the increasing competitiveness of business and need for customer segmentation. The literature review summarizes several papers on topics like using machine learning for customer segmentation, comparing clustering algorithms on retail data, and dividing bank customers into clusters. The implementation section outlines the steps taken - data collection, cleaning, applying K-Means and agglomerative clustering, and exploratory data analysis. The proposed system aims to recognize the current customer situation, consolidate prior work, discover customer-attribute relationships, perform unsupervised clustering analysis and model evaluation,
IMAGE CONTENT IN LOCATION-BASED SHOPPING RECOMMENDER SYSTEMS FOR MOBILE USERSacijjournal
This paper shows how image content can be used to realize a shopping recommender system for intuitively supporting mobile users in decision making. A mobile user equipped with a camera enabled smart phone combined with Global Positioning System (GPS) capabilities would benefit in using a recommender system for mobile users. This recommender system is queried by image sent by a smart phone together with the smart phone’s GPS coordinates then the system returns a recommended retail shop together with its GPS coordinates, the image similar to the query image and other items on special
offer. This recommender system shows a drastic reduction if not elimination of usage of text by mobile users using mobile devices when accessing the system. This paper presents the proposed recommender system and the simulated results of the recommender system. In summary the main contribution of this
paper is to show how image retrieval, image content and camera enabled smart mobile device with GPS capabilities can be used to realize a location-based shopping recommender system for mobile users.
A Machine Learning Approach to Predict the Consumer Purchasing Behavior on E-...IRJET Journal
This document presents research on using machine learning algorithms to predict consumer purchasing behavior on e-commerce websites during the COVID-19 pandemic. The researchers collected data on consumer purchases from online sources and analyzed features like category, brand, and price to train linear regression, logistic regression, support vector machine, K-nearest neighbors, random forest, and naive Bayes models. The models were evaluated on their ability to predict if a consumer would view, add an item to their cart, or purchase an item based on its attributes. The results found that different algorithms provided varying predictions of consumer behavior.
1) The document discusses the Cyclic Model Analysis (CMA) technique for sequential pattern mining which aims to predict customer purchasing behavior.
2) CMA calculates the Trend Distribution Function from sequential patterns to model purchasing trends over time. It then uses Generalized Periodicity Detection and Trend Modeling to identify periodic patterns and construct an approximating model.
3) The Cyclic Model Analysis algorithm is applied to further analyze the patterns, dividing the domain into segments where the distribution function is increasing or decreasing and applying the other techniques recursively to fully model the cyclic behavior.
Identifying Malicious Reviews Using NLP and Bayesian Technique on Ecommerce H...IRJET Journal
This document summarizes a research paper that proposes a method called PSGD (Partially Supervised Graph-based Detection) to identify spammer groups on e-commerce platforms. The method first labels some known spammer groups as positive examples. It then uses these examples along with unlabeled groups and extracted negative examples to train a Naive Bayesian classifier to identify spammer groups. The proposed PSGD method is evaluated on real data from Amazon.cn and is shown to outperform existing spammer group detection techniques.
Mining the Web Data for Classifying and Predicting Users’ RequestsIJECEIAES
Consumers are the most important asset of any organization. The commercial activity of an organization booms with the presence of a loyal customer who is visibly content with the product and services being offered. In a dynamic market, understanding variations in client‟s behavior can help executives establish operative promotional campaigns. A good number of new consumers are frequently picked up by traders during promotions. Though, several of these engrossed consumers are one-time deal seekers, the promotions undeniably leave a positive impact on sales. It is crucial for traders to identify who can be converted to loyal consumer and then have them patronize products and services to reduce the promotion cost and increase the return on investments. This study integrates a classifier that allows prediction of the type of purchase that a customer would make, as well as the number of visits that he/she would make during a year. The proposed model also creates outlines of users and brands or items used by them. These outlines may not be useful only for this particular prediction task, but could also be used for other important tasks in e-commerce, such as client segmentation, product recommendation and client base growth for brands.
A novel approach to optimizing customer profiles in relation to business metricsIAESIJAI
Business is very closely related to customers. Each user owns the data, and the data is used to identify cross-selling opportunities for each customer. For example, the type of product or service purchased, the frequency of purchases, geographic location, and so on. By doing so, you can gain the ability to manage and analyze customer data, allowing you to create new opportunities in industries that were previously difficult to enter. The purpose of optimizing user profiles is to determine minimum or maximum business value and improve efficiency by determining user needs. In this study, multivariate adaptive regression spline (MARS) is a statistical model used to explain the relationship between the response variable and the predictor variable. Robust is used to find variable relationships to make predictions. To improve classification performance, the model is validated using a confusion matrix. The results show an accuracy value of 84.5%, with better time management (period management) reflected in the number of hours spent by merchants as well as discounts during that time period, which has a significant impact on any business. In addition, the distance between customers and merchants is also important, as customers prefer merchants who are closer to them to save time and transportation costs.
An impact of knowledge mining on satisfaction of consumers in super bazaarsIAEME Publication
This document summarizes research on using knowledge mining techniques to study customer satisfaction levels in super bazaars. It first introduces the importance of customer satisfaction for super bazaars and defines knowledge mining. It then describes various knowledge mining techniques that can be applied, including classification, regression, time series analysis, clustering, and association rule mining. The document proposes a model for conducting customer satisfaction surveys, applying knowledge mining techniques to the data, and using the results to enhance customer satisfaction. The goal of the research is to better understand customer preferences and behaviors to improve business performance for super bazaars.
A secure architecture for m commerce users using biometerics and pin distribu...pradip patel
In Coastal area plants do not grow properly because of the seawater. So to overcome these difficulties, the use of this technique can provide a proper plant growth. The seawater combines a solar desalination system with an environment for cultivating crops in which transpiration is minimized. To provide fresh water we use sunlight, seawater and cooled humid air to supply more sustainable environment condition for cultivation of crops in arid coastal region. This project tries to describe simulation the seawater considering condition of the arid region in district like Kutch (Gujarat) and in many countries like Iran, Oman. With desalination of seawater, it aims to provide sustainable local production of food by combining a growing environment in which water usage is minimized by solar energy. The technique is adapted for farms in arid coastal region that are suffering from salt infected soils and shortages of potable ground water. This technique may produce around 90-95% of total fresh water.
The document discusses analyzing clickstream data to understand customer behavior on e-commerce websites. It aims to identify factors that influence customers to abandon items in their carts without purchasing. The objectives are to analyze best selling products, understand browsing patterns, assess product availability, and identify customer buying trends. Feature selection and k-means clustering will be used to analyze the clickstream data and gain insights. The analysis seeks to improve the business by optimizing the customer and product experience.
PATTERN DISCOVERY FOR MULTIPLE DATA SOURCES BASED ON ITEM RANKIJDKP
Retail company’s data may be geographically spread in different locations due to huge amount of data and
rapid growth in transactions. But for decision making, knowledge workers need integrated data of all sites.
Therefore the main challenge is to get generalized patterns or knowledge from the transactional data
which is spread at various locations. Transporting data from those locations to server site increases the
cost of transportation of data and at the same time finding patterns from huge data on the server increases
the time and space complexity. Thus multi-database mining plays a vital role to extract knowledge from
different data sources. Thus the technique proposed finds the patterns on various sites and instead of
transporting the data, only the patterns from various locations get transported to the server to find final
deliverable pattern. The technique uses the ranking algorithm to rank the items based on their profit, date
of expiry and stock available at each location. Then association rule mining (ARM) is used to extract
patterns based on ranking of items. Finally all the patterns discovered from various locations are merged
using pattern merger algorithm. Proposed algorithm is implemented and experimental results are taken
for both classical association rule mining on integrated data and for datasets at various sources. Finally
all patterns are combined to discover actionable patterns using pattern merger algorithm given in section
CSHURI – Modified HURI algorithm for Customer Segmentation and Transaction Pr...IJCSEIT Journal
Association rule mining (ARM) is the process of generating rules based on the correlation between the set
of items that the customers purchase.Of late, data mining researchers have improved upon the quality of
association rule mining for business development by incorporating factors like value (utility), quantity of
items sold (weight) and profit. The rules mined without considering utility values (profit margin) will lead
to a probable loss of profitable rules.
The advantage of wealth of the customers’ needs information and rules aids the retailer in designing his
store layout[9]. An algorithm CSHURI, Customer Segmentation using HURI, is proposed, a modified
version of HURI [6], finds customers who purchase high profitable rare items and accordingly classify the
customers based on some criteria; for example, a retail business may need to identify valuable customers
who are major contributors to a company’s overall profit. For a potential customer arriving in the store,
which customer group one should belong to according to customer needs, what are the preferred functional
features or products that the customer focuses on and what kind of offers will satisfy the customer, etc.,
finds the key in targeting customers to improve sales [9], which forms the base for customer utility mining.
IRJET- Shopping Mall Experience using Beacon TechnologyIRJET Journal
The document proposes a system that uses beacon technology to track customers in retail shops and provide personalized discounts based on their shopping patterns. Beacons set up in shops would detect customers' locations via Bluetooth signals from their smartphones. This information would be sent to a server to analyze customers' purchase histories and send personalized offers. The system aims to improve customers' shopping experiences and increase retailers' sales by offering targeted discounts to customers based on their identified preferences and shopping behaviors.
IRJET- Secure Smart Shopping System using Android ApplicationIRJET Journal
This document describes a proposed secure smart shopping system using an Android application. The system aims to simplify the shopping process for customers. It would allow users to download an app, scan product QR codes to add items to their cart, view product recommendations and navigation to products. It also generates bills that can be paid online through banking, allowing quicker checkout. The system is meant to reduce the time spent shopping and waiting in lines compared to traditional shopping methods.
This document summarizes an article that proposes a new algorithm for efficiently mining both positive and negative association rules from transactional databases. The algorithm first constructs a frequent pattern tree (FP-tree) to store the transaction information. It then uses an FP-growth approach to iteratively find frequent patterns and generate the positive and negative association rules without candidate generation. The algorithm aims to overcome limitations of previous methods and efficiently find all valid comparative association rules.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
"NATO Hackathon Winner: AI-Powered Drug Search", Taras KlobaFwdays
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
GlobalLogic Java Community Webinar #18 “How to Improve Web Application Perfor...GlobalLogic Ukraine
Під час доповіді відповімо на питання, навіщо потрібно підвищувати продуктивність аплікації і які є найефективніші способи для цього. А також поговоримо про те, що таке кеш, які його види бувають та, основне — як знайти performance bottleneck?
Відео та деталі заходу: https://bit.ly/45tILxj
Must Know Postgres Extension for DBA and Developer during MigrationMydbops
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
1. R.Priyadharshini, N.Geethanjali, B. Dhivya / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.238-242
238 | P a g e
Mining and Predicting Users M-Commerce Patterns using
Collaborative Filtering Algorithm
R.Priyadharshini1
, N.Geethanjali2
, B. Dhivya3
PG Scholars
Department of Information Technology SNS College of Technology, Coimbatore, India
Abstract
Mobile Commerce, also known as M-
Commerce or mCommerce, is the ability to
conduct commerce using a mobile device.
Research is done by Mining and Prediction of
Mobile Users’ Commerce Behaviors such as
their movements and purchase transactions. The
problem of PMCP-Mine algorithm has been
overcome by the Collaborative Filtering
Algorithm. The main objective is to analyse the
Mobile users’ movements to the new locations
instead of considering only the frequent moving
locations. In the existing approach, a Mobile
Commerce Explorer Framework has been
implemented to make recommendations for
stores and items by analysing the Mobile users’.
The drawbacks are the recommendations that
made are only for frequently moving locations
and stores. The proposed work is to recommend
stores and items in new locations by considering
the rating of items given by the other users in
new locations.
Keywords– Mining, Prediction, Mobile
Commerce.
I. INTRODUCTION
With the rapid advance of wireless
communication technology and the increasing
popularity of powerful portable devices, mobile
users not only can access worldwide information
from anywhere at any time but also use their
mobile devices to make business transactions
easily, e.g., via digital wallet [1]. Meanwhile, the
availability of location acquisition technology, e.g.,
Global Positioning System (GPS), facilitates easy
acquisition of a moving trajectory, which records a
user movement history. At developing pattern
mining and prediction techniques that explore the
correlation between the moving behaviors and
purchasing transactions of mobile users to explore
potential M-Commerce features. Owing to the
rapid development of the web 2.0 technology,
many stores have made their store information,
e.g., business hours, location, and features available
online.
Collecting and analysing user trajectories
from GPS-enabled devices. When a user enters a
building, the user may lose the satellite signal until
returning outdoors. By matching user trajectories
with store location information, a users’ moving
sequence among stores in some shop areas can be
extracted. The mobile transaction sequence
generated by the user is {(A,{i1}), (B,Ø), (C,{i3}),
(D,{i2}), (E,Ø),(F,{i3,i4}), (I,Ø),(K,{i5})}. There is
an entangling relation between moving patterns and
purchase patterns since mobile users are moving
between stores to shop for desired items. The
moving and purchase patterns of a user can be
captured together as mobile commerce patterns for
mobile users. To provide this mobile ad hoc
advertisement, mining mobile commerce patterns
of users and accurately predicts their potential
mobile commerce behaviors obviously are essential
operations that require more research.
Fig 1 Example of Mobile Transaction Sequence
To capture and obtain a better
understanding of mobile users’ mobile commerce
behaviors, data mining has been widely used for
discovering valuable information from complex
data sets. They do not reflect the personal
behaviors of individual users to support M-
Commerce services at a personalized level. Mobile
Commerce or M-Commerce, is about the explosion
of applications and services that are becoming
accessible from Internet-enabled mobile devices. It
involves new technologies, services and business
models. It is quite different from traditional e-
Commerce. Mobile phones impose very different
constraints than desktop computers.
II. PROBLEM DEFINITION
In the MCE framework, frequently
moving locations and frequently purchased items
are considered for analysing mobile users’
commerce behavior. The Personal Mobile
2. R.Priyadharshini, N.Geethanjali, B. Dhivya / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.238-242
239 | P a g e
Commerce Pattern-Mine (PMCP-Mine) algorithm
was used to find only frequent datasets, by deleting
in-frequent data in the Mobile Commerce Explorer
Database. Also, recommendations were done only
for the frequent datasets. The similarity values that
were found in the Similarity Inference Model
(SIM) were not accurate.
III. LITERATURE SURVEY
Chan Lu, Lee and S. Tseng developed the
Mobile Commerce Explorer Framework for mining
and prediction of mobile users’ movements and
purchases [1]. Agrawal and Swami presented an
efficient algorithm [2] that generates all significant
association rules between items in the database.
Han, Pei and Yin proposed a novel
frequent-pattern tree (FP-tree) structure, which is
an extended prefix-tree [3] structure for storing
compressed, crucial information about frequent
patterns, and develop an efficient FP-tree based
mining method, FP-growth, for mining the
complete set of frequent patterns by pattern
fragment growth.
Herlocker, Konstan, Brochers and Riedl
developed an Automated Collaborative Filtering is
quickly becoming a popular technique for reducing
information overload, often as a technique to
complement content-based information filtering
systems [8]. In this paper, present an algorithmic
framework for performing Collaborative Filtering
and new algorithmic elements that increase the
accuracy of Collaborative Prediction algorithms.
Then present a set of recommendations on selection
of the right Collaborative Filtering algorithmic
components.
IV. EXISTING SYSTEM
A novel framework for the mobile users’
commerce behaviors has been implemented for
mining and prediction of mobile users’. MCE
framework has been implemented with three
components: 1) Similarity Inference Model (SIM)
for measuring the similarities among stores and
items, 2) Personal Mobile Commerce Pattern Mine
(PMCP-Mine) algorithm for efficient discovery of
mobile users’ Personal Mobile Commerce Patterns
(PMCPs), 3) Mobile Commerce Behavior Predictor
(MCBP) for prediction of possible mobile user
behaviors. In the MCE framework, only frequently
moved locations and frequently purchased items
are considered. The modules proposed in
framework are:
A. Mobile Network Database
The mobile network database maintains
detailed store information which includes locations.
B. Mobile User Data Base
The Mobile User database maintains
detailed mobile user information which include
network provider.
C. Applying Data Mining Mechanism
System has an “offline” mechanism for Similarity
inference and PMCPs mining, and an “online”
engine for mobile commerce behavior prediction.
When mobile users move between the stores, the
mobile information which includes user
identification, stores, and item purchased are stored
in the mobile transaction database. In the offline
data mining mechanism, develop the SIM model
and the PMCP Mine algorithm to discover the
store/item similarities and the PMCPs, respectively.
Similarity Inference Model for measuring the
similarities among stores and items. Personal
Mobile Commerce Pattern-Mine (PMCP-Mine)
algorithm is used for efficient discovery of mobile
users’ Personal Mobile Commerce Patterns.
D. Behavior prediction engine
In the online prediction engine,
implemented a MCBP (Mobile Commerce
Behavior Predictor) based on the store and item
similarities as well as the mined PMCPs. When a
mobile user moves and purchases items among the
stores, the next steps will be predicted according to
the mobile user’s identification and recent mobile
transactions. The framework is to support the
prediction of next movement and transaction.
Mobile Commerce Behavior Predictor for
prediction of possible mobile user behaviors.
E. Similarity Inference Model
A parameter-less data mining model,
named Similarity Inference Model, to tackle this
task of computing store and item similarities.
Before computing the SIM, derive two databases,
namely, SID and ISD, from the mobile transaction
database. An entry SIDpq in database SID
represents that a user has purchased item q in store
p, while an entry ISDxy in database ISD represents
that a user has purchased item x in store y.
Deriving the SIM to capture the similarity score
between stores/items. For every pair of stores or
items, SIM assigns them a similarity score. In SIM,
used two different inference heuristics for the
similarity of stores and items because some stores,
such as supermarkets, may provide various types of
items.
By applying the same similarity inference
heuristics to both of stores and items, various types
of items may be seen as similar since different
supermarkets are seen as similar. Based on our
heuristics, if two stores provide many similar items,
the stores are likely to be similar; if two items are
sold by many dissimilar stores, the stores are
unlikely to be similar. Since the store similarity and
item similarity are interdependent, computing those
values iteratively. For the store similarity, consider
that two stores are more similar if their provided
items are more similar. Given two stores sp and sq,
compute their similarity SIM (sp; sq) by calculating
the average similarity of item sets provided by sp
and sq. For every item sold in sp (and, respectively,
sq), first find the most similar item sold in sq (and,
3. R.Priyadharshini, N.Geethanjali, B. Dhivya / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.238-242
240 | P a g e
respectively, sp). Then, the store similarity can be
obtained by averaging all similar item pairs.
Therefore, SIM (sp; sq) is defined as
sim (sp,sq)= ∑φϵГsp MaxSim(φ,Гsq)+
∑ϒϵГsq MaxSim(ϒ, Гsp)
|Гsp|+|Гsq|
Where MaxSim(e,E) = Maxe’ϵE sim(e,e’) represents
the maximal similarity between e and the element
in E. Гsp and Гsq are the sets of items sold in sp and
sq, respectively. On the other hand, for the item
similarity, consider that two items are less similar if
the items are sold by many dissimilar stores. Given
two items ix and iy, compute the similarity sim(ix,iy)
by calculating the average dissimilarity of store sets
that provide ix and iy. For every store providing ix
(and, respectively, iy), first find similarity by
averaging all dissimilar store pairs.
F. Personal Mobile Commerce Pattern-Mine
Algorithm
The PMCP-Mine algorithm is divided into
three main phases: 1) Frequent-Transaction
Mining: A Frequent- Transaction is a pair of store
and items indicating frequently made purchasing
transactions. In this phase, first discover all
Frequent-Transactions for each user. 2) Mobile
Transaction Database Transformation: Based on
the all Frequent-Transactions, the original mobile
transaction database can be reduced by deleting
infrequent items. The main purpose is to increase
the database scan efficiency for pattern support
counting. 3) PMCP Mining: This phase is mining
all patterns of length k from patterns of length k-1
in a bottom-up fashion.
G. Mobile Commerce Behavior Predictor
MCBP measures the similarity score of
every PMCP with a user’s recent mobile commerce
behavior by taking store and item similarities into
account. In MCBP, three ideas are considered: 1)
the premises of PMCPs with high similarity to the
user’s recent mobile commerce behavior are
considered as prediction knowledge; 2) more recent
mobile commerce behaviors potentially have a
greater effect on next mobile commerce behavior
predictions and 3) PMCPs with higher support
provide greater confidence for predicting users’
next mobile commerce behavior. Based on the
above ideas, propose a weighted scoring function to
evaluate the scores of PMCPs. For all PMCPs,
calculate their pattern score by the weighted
scoring function. The consequence of PMCP with
the highest score is used to predict the next mobile
commerce behavior.
H. Performance Comparison
Conduct a series of experiments to
evaluate the performance of the proposed
framework MCE and its three components, i.e.,
SIM, PMCP-Mine, and MCBP under various
system conditions. The experimental results show
that the framework MCE achieves a very high
precision in mobile commerce behavior
predictions. Besides, the prediction technique
MCBP in our MCE framework integrates the
mined PMCPs and the similarity information from
SIM to achieve superior performs in terms of
precision, recall, and F-measure. The experimental
results show that the proposed framework and three
components are highly accurate under various
conditions.
Fig 2 Performance Comparison
V. PROPOSED SYSTEM
A. Similarity Inference Model
Propose a parameter-less data mining
model, named Similarity Inference Model, to tackle
this task of computing store and item similarities.
Before computing the SIM, derive two databases,
namely, SID and ISD, from the mobile transaction
database. An entry SIDpq in database SID
represents that a user has purchased item q in store
p, while an entry ISDxy in database ISD represents
that a user has purchased item x in store y.
Deriving the SIM to capture the similarity score
between stores/items. For every pair of stores or
items, SIM assigns them a similarity score. In SIM,
used two different inference heuristics for the
similarity of stores and items because some stores,
such as supermarkets, may provide various types of
items.
By applying the same similarity inference
heuristics to both of stores and items, various types
of items may be seen as similar since different
supermarkets are seen as similar. Based on our
heuristics, if two stores provide many similar items,
the stores are likely to be similar; if two items are
sold by many dissimilar stores, the stores are
unlikely to be similar. Since the store similarity and
item similarity are interdependent, computing those
values iteratively. For the store similarity, consider
4. R.Priyadharshini, N.Geethanjali, B. Dhivya / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.238-242
241 | P a g e
that two stores are more similar if their provided
items are more similar. Given two stores sp and sq,
compute their similarity SIM (sp; sq) by calculating
the average similarity of item sets provided by sp
and sq. For every item sold in sp (and, respectively,
sq), first find the most similar item sold in sq (and,
respectively, sp). Then, the store similarity can be
obtained by averaging all similar item pairs.
Fig 3 Block Diagram of Mobile Commerce
Explorer Framework
B. Collaborative Personal Mobile Commerce
Pattern Algorithm
The proposed system is developed by
implementing CPCMP - Collaborative PCMP
Algorithm which takes into account the newly
updated locations and predicts behavior of the user
based on the Collaborative Filtering. Although
collaborative filtering methods have been
extensively studied recently, most of these methods
require the user-item rating matrix. However, on
MCE Database, in most of the cases, other user
preferences and transactions are not always
available. Hence, collaborative filtering algorithms
cannot be directly applied to most of the
recommendation tasks on the database, like query
suggestion etc.
Combine the Collaborative filtering
aspects of predicting the unknown entities along
with the proposed PCMP which mining the patterns
of the user transactions behavior. This hybrid
algorithm facilitates dynamic predictions and hence
recommendation to the users for better customer
service and experience. For better similarity
inference modelling in cases of users visiting
unknown locations, a new hybrid similarity
inference model is proposed to take into account
the items transacted and stores visited by a similar
user in the same location who have similar Personal
mobile commerce pattern – i.e the frequent mining
patterns of the unknown user matches with respect
to the user under consideration. So, the proposed
work improves the quality of predictions of the
preferred items and stores of the customer or user
thereby bring about better sales and customer
support experience and feedback.
C. Mobile Commerce Behavior Predictor
Propose MCBP, which measures the
similarity score of every PMCP with a user’s recent
mobile commerce behavior by taking store and
item similarities into account. In MCBP, three
ideas are considered: 1) the premises of PMCPs
with high similarity to the user’s recent mobile
commerce behavior are considered as prediction
knowledge; 2) more recent mobile commerce
behaviors potentially have a greater effect on next
mobile commerce behavior predictions and 3)
PMCPs with higher support provide greater
confidence for predicting users’ next mobile
commerce behavior. Based on the above ideas,
propose a weighted scoring function to evaluate the
scores of PMCPs.
VI. CONCLUSION
A novel framework namely MCE was
proposed for mining and prediction of mobile
users’ movements and transactions in mobile
commerce environments. In the MCE framework
were designed with three major techniques: 1) SIM
for measuring the similarities among stores and
items; 2) PMCP-Mine algorithm for efficiently
discovering mobile users’ PMCPs; and 3) MCBP
for predicting possible mobile user behaviors. To
best knowledge, it is the first work that facilitates
mining and prediction of personal mobile
commerce behaviors that may recommend stores
and items previously unknown to a user. To
evaluate the performance of the proposed
framework and three proposed techniques,
conducted a series of experiments.
The experimental results show that the
framework MCE achieves a very high precision in
mobile commerce behavior predictions. Besides,
the prediction technique MCBP in MCE framework
integrates the mined PMCPs and the similarity
information from SIM to achieve superior performs
in terms of precision, recall, and F-measure. The
experimental results show that the proposed
framework and three components are highly
accurate under various conditions.
To overcome the problems of user moving
to new locality, Collaborative Filtering algorithm
was implemented to recommend the users about the
stores and items instead of considering only
frequent data.
VII.FUTURE ENHANCEMENT
For the future work, we plan to explore
more efficient mobile commerce pattern mining
5. R.Priyadharshini, N.Geethanjali, B. Dhivya / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.238-242
242 | P a g e
algorithm, design more efficient similarity
inference models, and develop profound prediction
strategies to further enhance the MCE framework.
In addition, we plan to apply the MCE framework
to other applications, such as object tracking sensor
networks and location based services, aiming to
achieve high precision in predicting object
behaviors.
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