Store Transaction Research Articles

NOVEL APPROACH FOR HYPERLEDGER FABRIC USING IOT FOR BANK TRANSACTIONS

The increase of attacks on bank accounts and credit cards through various types of attacks. Also, the rapid growth of online bank transfers and rapid transactions in stores and marketing worldwide make the urgent to use ciphering for securing the transactions process. In this paper, ways are proposed to enhance the algorithm that used ciphering and authentications of the user to ensure that the same user made the transactions. This is done through using the blockchain such as the Hyperledger fabric with the using the Internet of things for the authentications. The proposed algorithm helps in improving the safety of online transactions and helps protect the information of the user through the several nodes that use IOT for verifications and authentications. The results enhanced the blockchain of the Hyperledger fabric by enhancing the ways of the transactions and the process through the power of IoT.

Computer Allied Intelligence in the Education Resource-Sharing Based inContract Deep Learning

A resource-sharing platform is an online space where individuals or organizations can exchange or access various resources, such as knowledge, skills, tools, or assets. These platforms facilitate collaboration and cooperation among users by providing a centralized hub for sharing resources efficiently. In university higher education, a resource-sharing platform serves as a vital tool for students, faculty, and staff to optimize access to a wide array of educational resources. These platforms enable the sharing of academic materials, such as textbooks, lecture notes, and research papers, fostering collaborative learning environments. This paper proposed the proposed method of Distributed Consensus Blockchain Deep Learning (dCBDL) for a resource-sharing deep learning platform in higher education aims to revolutionize collaborative learning and resource utilization. Initially, the dCBDL model uses a blockchain network that serves as the underlying infrastructure for the platform. This blockchain will store transaction records, verify the authenticity of shared resources, and ensure data integrity and immutability. The proposed model uses smart contracts and decentralized storage with a consensus mechanism for data processing in higher education. Finally, the proposed dCBDL model uses the deep learning model for the classification and assessment of student performance in higher education. The proposed model achieves a classification accuracy of 98% which is significantly higher than the conventional techniques such as blockchain architecture and SVM and LSTM classifiers.

Enhancing Trust in Transactive Energy with Individually Linkable Pseudonymous Trading Using Smart Contracts

The transactive energy market (TEM) is a recent development in energy management that enables prosumers to trade directly, promising many environmental and economic benefits. Prosumer trading necessitates sharing information to facilitate transactions. Additionally, many TEMs propose using blockchains to manage auctions and store transactions. These facts introduce privacy concerns: consumption data, trading history, and other identifying information pose risks to users if leaked. Anonymity by trading under a pseudonym is commonly presented as a solution; however, this creates risks for market participants: scammed users will not have recourse, and users with innocent malfunctions may be banned from trading. We propose the Individually Linkable Pseudonymous Trading Scheme (ILPTS), which enables users to trade under a pseudonym, protecting their identity, while a smart contract monitors reputations and can temporarily deanonymize a user, ensuring market integrity. ILPTS was developed in stages. Examination of existing TEM literature was performed to identify desirable features. Analysis of cryptography literature was performed to identify techniques that may confer certain features. It was found through formal analysis that ILPTS adheres to identified design goals, improves upon existing solutions, and resists common attacks against TEMs. Future work includes software simulation and on-device implementation to further verify security and feasibility.

THE FUTURE STUDY OF UNMANNED CONVENIENCE STORE IN MALAYSIA

Most conventional convenience stores in Malaysia face operation time and manpower problems, indicating the need for solutions such as unmanned convenience store. The unmanned convenience store is a retail outlet without service personnel or cashier. The technology of artificial intelligence and the Internet of Things are typically used to facilitate transactions in unmanned stores. However, whether unmanned convenience stores might replace conventional stores in the future has not been investigated. Therefore, this study aimed to identify the key drivers of the unmanned convenience store from merged issues, trends, and challenges and its future scenarios of the unmanned convenience store in Malaysia. This study used exploratory research design by using descriptive survey developed from the STEEPV analysis. Eight merged key drivers had been identified after the merging of all the drivers from STEEPV analysis. Descriptive questionnaires were distributed to the consumers who went to unmanned convenience stores. The technology readiness of unmanned convenience stores was also evaluated using Technology Readiness Index (TRI). The top two drivers that were identified which are “innovation of technology adoption” and “consumer preference” in impact-uncertainty analysis and were used for developing future scenarios of unmanned convenience store in the next 5 to 10 years. Four future scenarios discussed were “technology is at par with customer demand”, “improvement is observed”, “uses of technology are not welcome by customers”, and “not fulfilling customer’s demand”. Consequently, this study may assist future researcher and developers to enhance their understanding of the unmanned convenience store in Malaysia.

Decentralize transaction records of digital payment gateway using Ethereum Blockchain and Interplanetary File System

This research proposes a system to store transaction records from a payment gateway based on digital payments by utilizing blockchain technology and Interplanetary File System (IPFS) as distributed storage. In digital payments, the possibility of online fraud or theft of customer data is a problem that needs to be solved. Although the solution is to use a Payment Gateway with security standards to prevent these problems, the system is still centralized and vulnerable to the possibility of system failure or data changes by irresponsible people. Blockchain furnishes a secure and unalterable record for documenting payment transactions. Conversely, IPFS presents a decentralized and immune-to-censorship approach for storing and accessing data. The usage of these technologies guarantees the secure storage of payment transaction data and related documents (such as invoices and receipts) while effectively thwarting data manipulation or censorship attempts. Therefore, this research aims to build a blockchain technology system to increase security and maintain the integrity of the data generated from the Payment Gateway. This research using distributed data storage using IPFS to save transaction from the payment gateway data. Based on the evaluation and security analysis results, implementing a blockchain technology system to store transaction records from digital-based payment gateways can improve data security and integrity. In the gas fee increase analysis, the gas fee will increase according to the total number of bytes generated when the transaction is made, where each byte will cost 12 gwei to pay the gas fee.

Web Development for Bank Statement Analysis: Technologies, Challenges, and Innovations

The purpose of this project is to fulfill the requirements of a bachelor’s degree in Cloud Technology and Information Security. The project aims to design and develop a Bank Statement Analysis system that provides a more secure approach to managing bank customers' statements, strengthening the relationship between banks and their customers by offering solutions with multi-level security to improve customer satisfaction. Methodology The project utilizes a range of technologies, including: · Python Flask Framework · Computer Vision (Google's Tesseract OCR) · PDF to Image Python package (pdf2image) · Machine Learning (Scikit-Learn) to predict transaction categories · Azure SQL Database to store transaction data and user login details · Power-BI for data visualization · Docker and AWS EC2 for service hosting. Findings The Bank Statement Analysis system developed in this project offers a secure and efficient solution for managing bank customers' statements. The use of multi-level security measures, such as Azure SQL Database and Power-BI visualizations, enhances the overall security and user experience, strengthening the relationship between banks and their customers. Conclusion This project demonstrates the successful design and development of a Bank Statement Analysis system that leverages various technologies, including cloud computing, computer vision, and machine learning, to provide a more secure and user-friendly solution for managing bank customers' statements. Keywords Azure SQL Database, Power-BI Tool, AWS EC2 Service

Poverty Stigma: Assessing the Surveillance of and Psychological Reframing by WIC Recipients

ABSTRACT The current study examines the poverty stigma experienced by Women, Infants, and Children program recipients during grocery store transactions. Through in-person interviews with 32 WIC recipients in metro Detroit, the study examines perceptions of the social context, strategies to manage stigma, and the social psychological consequences. The findings highlight the resilience of interviewees and the need for public assistance programs to address not just practical needs but also the social psychological well-being of recipients. This research contributes significantly to understanding poverty stigma, especially within the context of WIC food assistance. Policy implications are discussed.

Privacy-protecting predictive cache method based on blockchain and machine learning in Internet of vehicles

To solve the privacy leakage problem faced by Internet of Vehicles (IoV) users when enjoying location-based services (LBS), a privacy-protecting predictive cache method based on blockchain and machine learning (BML-PPPCM) is proposed. First, a Bi-directional Long-Short Term Memory (Bi-LSTM) model is used to predict query requests over a future period based on historical request information. The predicted results are recommended to neighbors and broadcast to requestors. Then, deep Q-learning (DQN) is utilized to determine the optimal cache decision. Finally, a trust mechanism is introduced to calculate trust values, and blockchain is used to store transaction data and trust data, preventing malicious tampering by attackers. The simulation results show that BML-PPPCM has a higher cache hit ratio than other similar schemes and performs well in privacy protection and suppression of malicious and incentive denial of service providers.

Promoting healthful food purchases through in-store interventions: Empirical evidence from rural food deserts

Effective ways to promote healthful food intake in rural areas are understudied. The paper evaluated whether a two-component, in-store intervention designed to encourage healthy food purchases was associated with improved healthfulness scores of food items purchased by shoppers in rural food deserts. One component introduced a point-of-sales label that assigned a single numerical score to each food item facilitating direct comparisons of the product's nutrition with those of other products shelved around it. The other component was a one-day nutrition education workshop promoted within the store. Interventions took place in 2015 at two stores in rural counties in the U.S. Midwest. Four stores in similar communities were selected as the control group. We applied a difference-in-difference model to estimate changes in the healthfulness of food items purchased attributable to the intervention among shoppers at the treatment stores (n = 486) and control stores (n = 10,759) using store transaction data. Healthfulness of food items was measured in terms of food scores published by the Environmental Working Group on a 1–10 scale. Both components had minimal impacts on the scores, although 0.2 and 0.1 points increases in the score per item and score per dollar were statistically significant at the 1% level respectively. A year after the intervention, these small effects of the intervention further diminished compared to the immediately after implementation. Results suggest the average effects of intervention across the study communities had limited practical significance but benefited some rural residents who were exposed to the intervention.

SHARD-FEMF: Adaptive Forensic Evidence Management Framework using Blockchain Sharding and IPFS

Blockchain technology is a groundbreaking and highly secure decentralized digital ledger used to record and store transactions across a network of computers. Its primary purpose is to safeguard, monitor, and oversee digital assets, providing robust protection against unauthorized tampering, revisions, or deletions. It serves as an immutable and tamper-resistant ledger ideal for storing digital evidence, enabling the tracking of evidence’s origins while strictly controlling access to authorized individuals. Current evidence management systems lack essential functionalities, such as authenticating intermediate user access and efficiently transferring evidence access between users. These systems also rely on the Base64 algorithm, which presents challenges related to storage capacity, time delays, scalability, and transaction throughput. To address these limitations, this research introduces an innovative solution: The integration of the Base64 scheme with sharding and the Interplanetary File System (IPFS). This integration is designed to bolster transaction performance, scalability, and throughput. The Base64 scheme plays a pivotal role by encrypting image evidence, securely housing it within the blockchain network. Concurrently, IPFS decentralizes the storage of these images, thereby optimizing memory usage and enhancing transaction throughput within the blockchain environment. Experimental results showcase the efficacy of the proposed SHARD-FEMF, demonstrating a 25% improvement in memory utilization, a 21.5% reduction in gas utilization, and a 23% enhancement in transaction scalability compared to the existing Base64 scheme. Through the combined utilization of sharding and IPFS, the SHARD-FEMF framework represents a significant advancement in efficient forensic evidence management leveraging blockchain technology

基于ARIMA和GM模型的店铺交易额预测

基于ARIMA和GM模型的店铺交易额预测

APPLICATION OF THE RAPID APPLICATION DEVELOPMENT METHOD FOR SALES INFORMATION SYSTEM FOR TRADITIONAL MINANG KABAU WEDDING DRESSES

The Minang Kabau shop system is running inefficiently because the data processing structure is still manual, causing a number of problems. Processing customer data, processing transactions and generating reports are the first steps that cause problems. To avoid data redundancy which costs the shop money, a Minang Kabau shop sales application was developed. Its purpose is to handle and process data stored in a database system. Rapid Application Development, often known as the RAD System Development Method, is used to develop this information system. To speed up the process, this development strategy involves reducing the development stages. Customer and transaction data processing capabilities are included in this Minang Kabau retail sales application. Thus, this application can facilitate data processing, report creation and processing of Minang Kabau store transactions.

FLUID: Towards Efficient Continuous Transaction Processing in DAG-Based Blockchains

In most blockchain-based application scenarios, a complete application logic consists of multiple continuous transactions, in which the initiation of one transaction depends on the confirmation result of the previous one. This mandates that continuous transactions must be processed in the correct order. Unfortunately, existing chain-based blockchains fail to effectively support continuous transaction processing due to considerable latency in confirming continuous transactions. Recent studies shifted from chain-based blockchains to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Directed Acyclic Graph</i> (DAG) based blockchains, which reduced transaction confirmation latencies. However, DAG-based blockchains store transactions in an out-of-order manner that leads to unordered transaction processing. To address this challenge, we propose FLUID, a new DAG-based blockchain that supports continuous transaction processing while delivering high performance. The fundamental idea of FLUID is to design a transaction dependency tracking structure to ensure that continuous transactions can be processed in the correct order. FLUID utilizes a conflict resolution mechanism to provide instant confirmation and to support concurrent transaction processing with lower latencies. In addition, FLUID builds a checkpoint-based verification mechanism to achieve deterministic consensus on transaction processing results in the DAG. Extensive experiments demonstrate that our proposed FLUID can improve the throughput over state-of-the-art OHIE by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$66\%$</tex-math></inline-formula> with two orders of magnitude lower latencies.

DeScan: Censorship-resistant indexing and search for Web3

The popularity of blockchain technology has bootstrapped many “Web3” applications, e.g., Ethereum and IPFS, that apply distributed ledger technology to store transactions. The amount of transactions generated and stored in such Web3 applications is significant and, in its raw form, usually not searchable by users. Existing Web3 transaction indexing and search engines are predominantly centralized and, therefore, can manipulate search results or censor particular queries. With the proliferation of Web3 transactions and applications, a decentralized and censorship-resistant search primitive is becoming essential.We present DeScan, a decentralized and censorship-resistant indexing and search engine for Web3. Users index their local Web3 transactions using custom rules that output triplets. Generated triplets are bundled in a distributed transaction graph that is searchable by other users. To coordinate search and distribute the storage of the transaction graph over peers in the network, we build upon a Skip Graph (SG) data structure. Since the Skip Graph does not provide any resilience against adversarial peers that censor searches, we propose four modifications to improve its robustness. We implement DeScan and conduct experiments with up to 12 800 peers and 10 million Ethereum transactions. Our experiments show that DeScan with our modifications enabled can tolerate 20% adversarial peers and 35% unresponsive peers without disruption. Moreover, we find that searches in DeScan are usually completed well within a second, even when the network grows. Finally, we show that storage and network costs are evenly distributed amongst peers as the network grows.

Web-Based Altamart Store Sales System Design

Altamart is a grocery store that serves buying and selling transactions for goods for daily needs. Altamart also provides various types of food and drinks such as snacks, bread, instant noodles, syrup, mineral water, and others. Altamart still uses manual tools for transactions in stores. Therefore, to develop technology, a Web-Based Application was designed to improve performance at the Altamart store. Simply using this application consumers can transact remotely, consumers can also find out the total price of goods to be purchased, consumers know the details of goods easily and billing records can be accessed via the internet and also consumers can choose various payment systems offered. In addition, consumers can also choose a home delivery service. So consumers can wait for the goods to be delivered to their destination without having to come to the store. However, it can only deliver within the surrounding area. From this research a WEB-Based Application was created for buying and selling transactions that can make it easier for buyers, also makes it easier for users (Grocery Stores), so that it can help users record incoming and outgoing goods or if you make points, the results obtained are based on trial results are as follows: (1) WEB-Based Application for ordering goods has been successfully created; (2) With the Goods Ordering Application, consumers can find out information on the stock of goods and prices of goods, for users it can make it easier to manage stock of goods; (3) Providing accurate and up-to-date stock information; (4) This application is for ordering goods in the store

The Interferences of Acehnese to Indonesian Used by Shopkeepers in Medan Johor District

This is a research which discuses interference. Language interference is the language phenomenon common in bilingual speakers. It is one of bilingualism aspects that occurs on the level of structure and cultural sense whether oral or written, especially when somebody is learning his or her second language. The main problem in this research is what kind of interference is found in shopkeepers and how is the form of interference from Acehnese language to Indonesian in several shops in Medan Johor district. Interference refers to a deviation in using a language by including another language system. The method used is descriptive qualitative which describes a situation in the form of words that are analyzed orally and writing and consider the opinions of other people called sources. As the result of the research, it can be concluded that the interference of the Acehnese language to Indonesian speech that occurs in the conversations of buying and selling transactions in several stores in the Medan Johor district includes morphological interference, and syntactic interference. Factors that cause Aceh language interference to Indonesian speech in Medan Johor District, especially in some traders include intralinguistic is related to the language itself while extralinguistics includes individual factors and socio-cultural factors.

Sistem Informasi Akuntansi Pengelolaan Kas Berbasis Dekstop di Masjid Pusdai Jawa Barat

In recording cash, mosques are still based on semi-computers so it can result in inaccurate financial statements due to errors in data input, besides that storing archives or documents manually has many risks such as lost or damaged documents. This study aims to design a computerized cash management system so that all inputted data is stored in a database making data storage and management more efficient, and reporting can be done automatically and accurately. In designing a cash management accounting information system the author uses the waterfall development method starting with system analysis, system design, implementation, testing, and maintenance. The tools used in this design are Microsoft Visual Studio 2010 and Microsoft Access as databases. This research produces a system that can manage cash receipts and cash disbursements accurately and store transaction evidence into a database and make it easier to make reports.

Comparison of Apriori Algorithm and FP-Growth in Managing Store Transaction Data

The role and position of data in today's digital era are very important, data can be likened to a resource that can be explored further to produce new information or knowledge. Seeing the importance of data position, several solutions can be offered in getting more value from data, one of which is the use of Data Mining techniques with association techniques, several types of association techniques are a priori algorithms and FP-Growth algorithms. Based on the research results, the a priori algorithm produces a combination of goods with a confidence value of 98.4 and a support value of 98.4, and the algorithm produces a combination of goods with a support value of 95.2 and a confidence value of 95.2. The comparison of these two algorithms in making associations results in a faster execution time of the FP-Growth algorithm than Apriori, and the Apriori algorithm produces more varied itemset combinations.

NFT based model to manage educational assets in Metaverse

Blockchain has become a trend in the last few years due to its financial impact correlated to Cryptocurrency. Blockchain is a distributed, immutable, and decentralized database to store transactions which are done by users without the need for a trusted Third-party. Similar to cryptocurrency, tokens are objects that Represent digital or physical assets over a Blockchain network. Token market value has been dramatically increasing in the last few years due to The breakthrough that was made in preserving intellectual property And the usage in many other fields like tourism, education, and Art. Non- Fungible tokens (NFT) Are unique and exchangeable tokens because they are different in type and value due to their scarcity. Using NFT to manage the educational assets will be an effective way to protect the intellectual property and the educational assets from fraud. In this paper, we propose a NFT-based framework to manage the educational assets on the Metaverse that authenticate the ownership of the assets using Blockchain technology. We also explain the minting process we used to convert the assets into NFT in the framework.

LCTree-Based Approach for Mining Frequent Items in Real-Time.

With the increase of real-time stream data, knowledge discovery from stream data becomes more and more important, which requires an efficient data structure to store transactions and scan sliding windows once to discover frequent itemsets. We present a new method named Linking Compact Tree (LCTree). We designed an algorithm by using an improved data structure to create objective tree, which can find frequent itemsets with linear complexity. Secondly, we can merge items in sliding windows by one scan with Head Linking List data structure. Third, by implementing data structure of Tail Linking List, we can locate the obsolete nodes and remove them easily. Finally, LCTree is able to find all exact frequent items in data stream with reduced time and space complexity by using such a linear data structure. Experiments on datasets with different sizes and types were conducted to compare the proposed LCTree technique with well-known frequent item mining methods including Cantree, FP-tree, DSTree, CPSTree, and Gtree. The results of experiments show presented algorithm has better performance than other methods, and also confirm that it is a promising solution for detecting frequent item sets in real time applications.