Vol. 11 No. 08 (2022) | International Journal of Engineering and Computer Science

Home
Archives
Vol. 11 No. 08 (2022)

Published: 2022-08-18

Articles

Innovative Techniques for Optimizing Supply Chain Operations

Goli Mallesham

Pages No. 25564-25573

Innovative Techniques for Optimizing Supply Chain Operations

Goli Mallesham

Supply chain management is an approach used by firms to ensure that their business can be highly effective, and profitable and that operations run smoothly. This involves managing the movement of raw materials inwards and finished goods outwards. Logistics is a key component of this. It also involves managing the flow of products between companies, which can involve the movement of products between a manufacturer, a wholesaler, and a retailer. Supply chain management is therefore the integration of these flows between companies. Several innovative techniques can be used to optimize supply chain operations, particularly given recent advances in information technology. The function of logistics is known as activities that are related to the flow of products between companies, such as the transportation and warehousing of goods. Activities that take place within companies, such as inventory management and materials handling, are not considered to be logistical activities but part of the supply chain. The level of interest in supply chain management has risen quite dramatically over the last few years. This is partly due to advances in information technology, which have enabled closer integration of the supply chain. As well as increasing competition between companies, on both a national and an international level, has led to an increasing emphasis on the need for companies to concentrate on their core competencies, and to look to outside suppliers to provide other goods and services. This has led to the increased use of external suppliers.

More.. PDF

Abstract PDF Full text

Abstract 87 | PDF DOI https://doi.org/10.18535/ijecs/v11i08.4691

Advancing Digital Payment Systems: Combining AI, Big Data, and Biometric Authentication for Enhanced Security

Gagan Kumar Patra, Shravan Kumar Rajaram, Venkata Nagesh Boddapati, Chandrababu Kuraku, Hemanth Kumar Gollangi

Pages No. 25618-25631

Advancing Digital Payment Systems: Combining AI, Big Data, and Biometric Authentication for Enhanced Security

Gagan Kumar Patra, Shravan Kumar Rajaram, Venkata Nagesh Boddapati, Chandrababu Kuraku, Hemanth...

Digital payment solutions have recently gained extraordinary popularity across the globe. Many individuals in cities or urban areas have begun to use these payment systems for financial transactions. The combined use of different advanced technologies is indispensable for widespread adoption. Various obstacles to performing financial transactions using digital payment systems must be considered. Among these obstacles, security is the most remarkable point. Although this online payment system is fast and convenient, the chances of fraud are very high. A cybercriminal can hack into the system, stealing personal information like phone numbers and passwords. This article will focus on a new digital payment security system that will take advantage of artificial intelligence (AI), big data, and biometric authentication (BA). When using this security system, a transaction request will be sent. First, it will check whether the phone number is a registered user and if it is, further authentication will be carried out. The system will automatically authenticate the user’s identity with the help of facial recognition technology that will analyze the features of the face. Further, the user has to show various gestures that are analyzed by the system. This and other personal information will be converted into a number and then encrypted before being stored in the database. The incoming transaction request will be compared against the personal number. If the user is not unique, the transaction request will be immediately rejected. Secondly, if the number matches but the user is fraudulent, the transaction will also be rejected. Only if both are unique, the system will check for duplicate numbers among the other transactions for the particular period. If other numbers are not found, the transaction is executed; otherwise, it is still rejected. This is done to find out fake and auto-generated numbers. This security system will be helpful to ensure the authenticity of the user in digital payment systems.

More.. PDF

Abstract PDF Full text

Abstract 177 | PDF DOI https://doi.org/10.18535/ijecs/v11i08.4698

A Finger Vein Pattern based Key GenerationExchange and Security framework for IoT using ID based cryptography, ECDH and AES

Dnyaneshwari P.Wagh, Fadewar H.S , Shinde G. N , Santosh P. Shrikhande

Pages No. 25553-25563

A Finger Vein Pattern based Key GenerationExchange and Security framework for IoT using ID based cryptography, ECDH and AES

Dnyaneshwari P.Wagh, Fadewar H.S , Shinde G. N , Santosh P. Shrikhande

Every person has a unique finger vein pattern existing within each finger. Unlike facial features or fingerprints, finger vein authentication systems aren’t vulnerable to forgery. Finger vein authentication systems are more secure and reliable, and less expensive, than biometric security systems using fingerprint. This paper presents a novel security framework based on finger vein pattern. Finger Vein pattern in used in ID based cryptography to generate the keys for data encryption. These keys are combined with generator of Elliptic Curve Cryptography (ECC) to exchange the keys using Diffie Hellman key exchange algorithm. Once the keys are exchanged, the data is encrypted using Advance Encryption Standard (AES). This framework is tested in Internet of Things (IoT) environment for enhancing the security. The IoT based security systems implemented in the banks and other organizations can be enhanced considerably using the proposed security model.

More.. PDF

Abstract PDF Full text

Abstract 577 | PDF DOI https://doi.org/10.18535/ijecs/v11i08.4695

Modernizing Procurement in Supply Chain with AI and Machine Learning Techniques

Goli Mallesham

Pages No. 25574-25584

Modernizing Procurement in Supply Chain with AI and Machine Learning Techniques

Goli Mallesham

Public procurement in Europe represents, on average, 16.9% of the GDP and is the cornerstone of the European Single Market. Simplifying public procurement and reducing procurement administrative costs for the public and private sectors can deliver substantial benefits at the national and European levels. However, the complexity and diversity of public procurement processes, as well as the huge expenditure at hand, implement automatic systems tailored to specific procurement needs necessary. This paper shows how artificial intelligence, and in particular machine learning techniques, can be used to modernize public procurement. It presents implemented systems and showcases pilot projects. The results of an extensive evaluation are also reported.

The paper also argues that public procurement should be used more strategically by public administrations. This means aligning procurement actions with overall business objectives and using procurement to leverage supplier innovation and create a competitive advantage. Such advanced objectives are seldom achieved through the lowest price model. The paper also contains several recommendations for both the supply and demand sides to help realize the full potential of public procurement. On the supply side, recommendations relate to a better understanding of how artificial intelligence can be used in procurement activities, working with AI systems, and creating AI systems. On the demand side, recommendations involve the careful planning of how and when to use AI in procurement activities.

More.. PDF

Abstract PDF Full text

Abstract 94 | PDF DOI https://doi.org/10.18535/ijecs/v11i08.4692

Sustainable Manufacturing Engineering: Enhancing Product Quality through Green Process Innovations

Harshitkumar Ghelani

Pages No. 25632-25649

Sustainable Manufacturing Engineering: Enhancing Product Quality through Green Process Innovations

Harshitkumar Ghelani

The imperative for sustainable practices within manufacturing has become increasingly critical, driven by environmental concerns, regulatory pressures, and the evolving expectations of eco-conscious consumers. This paper investigates the role of sustainable manufacturing engineering in enhancing product quality through the implementation of green process innovations. Specifically, the study explores how sustainable manufacturing methods, including energy-efficient systems, additive manufacturing, zero-waste production, and renewable energy integration, can drive quality improvements while reducing the environmental impact of production processes.

The research is grounded in a comprehensive analysis of current green innovations in the manufacturing sector, drawing on case studies of companies that have successfully balanced quality and sustainability. The findings indicate that companies adopting sustainable practices not only reduce waste and lower emissions but also experience notable gains in product durability, customer satisfaction, and brand reputation. For instance, additive manufacturing is shown to improve precision and material efficiency, while zero-waste initiatives lead to a more efficient resource cycle, which in turn supports consistent quality standards.

This paper further delves into the challenges of implementing green manufacturing processes without compromising product quality, examining factors such as initial costs, technological readiness, and market adaptation. Visual data in the form of tables and graphs are presented to compare different green process innovations and their respective impacts on quality and sustainability metrics. In addition, case study summaries provide real-world examples of how leading companies have enhanced product quality through sustainable engineering approaches.

This study underscores the value of sustainable manufacturing engineering as a dual-benefit strategy that can drive environmental stewardship alongside quality enhancement. The paper recommends that future research focus on the development of new, cost-effective green technologies and the application of advanced data analytics for improved process control in sustainable manufacturing. Through these efforts, the manufacturing sector can continue advancing toward a more sustainable, high-quality production paradigm.

More.. PDF

Abstract PDF Full text

Abstract 59 | PDF DOI https://doi.org/10.18535/ijecs/v11i08.4700

AI-Enhanced Information Security: Safeguarding Government and Healthcare PHI

Venkata Bhardwaj Komaragiri, Andrew Edward

Pages No. 25601-25617

AI-Enhanced Information Security: Safeguarding Government and Healthcare PHI

Venkata Bhardwaj Komaragiri, Andrew Edward

Ransomware attacks on government agencies and the tendency of gangs are contributing to an unprecedented crisis in the U.S. These strikes not only lock organizations, including large healthcare providers and government networks, data for large amounts of ransom but also, in some cases, suspend patient care and critical social services provided by vital government employees. In addition to these recent destructive attacks, the annual Verizon Data Breach Investigation Report (DBIR) always shows that government and healthcare organizations have numerous security incidents and data crimes. These incidents cost millions of dollars in response tendencies and remediation measures, damage or extinction of data, and harm the reputation of affected organizations. Furthermore, the potential for causing physical harm to a patient has raised concerns about the current summary of the implications of cybercrime.This demonstrates that healthcare data collection and protection are of increasing importance to the public and the government. However, protecting healthcare-protected health information (PHI) is very difficult. Hospital information systems are complex, fragmented, and heterogeneous, involving a large number of medical devices, sensors, and software applications from multiple manufacturers. Protecting sensitive data in this environment is not easy, especially when the purpose of providing medical care is to improve the patient's condition through treatment by collecting, accessing, and sharing data. Most security measures are not compatible with this mission, so cybersecurity is still important. A recent report by the International Health Research Institute found that 82 American hospitals have been targets of last year's ransomware attacks, and it is very likely that these attacks and their negative impact will continue to grow. A recent report from the Obama Administration's President's Information Technology Advisory Committee (PITAC) described this particular situation in detail. The report further states that healthcare organizations must be prepared for future large-scale cyber attacks and that it is likely that highly motivated attackers have the potential to shut down large parts of critical network infrastructure.

More.. PDF

Abstract PDF Full text

Abstract 141 | PDF DOI https://doi.org/10.18535/ijecs/v11i08.4697

Transforming Cyber Defense: Anomaly Detection and Predictive Analytics for Automated Threat Response

Phani Durga Nanda Kishore Kommisetty, Bala Maruthi Subba Rao Kuppala, Hussain Vali Buvvaji

Pages No. 25585-25600

Transforming Cyber Defense: Anomaly Detection and Predictive Analytics for Automated Threat Response

Phani Durga Nanda Kishore Kommisetty, Bala Maruthi Subba Rao Kuppala, Hussain Vali Buvvaji

Currently, cyber defense remains a pre-eminently human-driven endeavor, lacking fundamental capabilities for comprehensive and timely detection, response, and prediction. Here, we present transformative concepts to mature cyber defense toward automated anomaly detection, prediction, and response. Our concepts treat the underlying problem at its most basic and essential level: violation of the predictability of correct actions and correct system and service performance, representing unintended relationships and change. We mathematically generalize prediction to explore relationships between dependencies, predict correct action sets, discern and anticipate both intended and unintended change, and mitigate the effects of correlated nested risk to enhance defense capabilities within and across organizations.

These general attributes can also provide the principal knowledge and mechanisms essential for new generations of cyber defense and information assurance. Our concepts directly address immediate and long-term, broad and fundamental needs in defense and, we believe, will be studied indefinitely. The fundamental nature of these concepts leads to their broad applicability across scientific, engineering, and human endeavors, including social, economic, and political systems, where incomplete knowledge-supported decisions steadily increase untenable manipulation and control. These general attributes can also provide the principal knowledge and mechanisms essential for new generations of cyber defense and information assurance.

More.. PDF

Abstract PDF Full text

Abstract 141 | PDF DOI https://doi.org/10.18535/ijecs/v11i08.4694

Implementing Efficient Data Operations: An Innovative Approach (Part -1)

Vinayak Pillai

Pages No. 25650-25670

Implementing Efficient Data Operations: An Innovative Approach (Part -1)

Vinayak Pillai

In today’s data-driven environment, efficient data operations are essential for organizations to optimize performance, enhance data accuracy, and enable rapid decision-making. This paper presents an innovative approach to implementing an automated data ingestion and processing framework designed to streamline repetitive tasks, ensure data quality, and support scalability within complex data ecosystems. The approach centers on a multi-step process that integrates robotic process automation (RPA), serverless computing, and advanced data transformation algorithms, thereby reducing manual interventions and accelerating data integration from multiple sources.

The data ingestion process initiates with the identification and automation of repetitive data collection tasks through RPA, effectively reducing the time and potential human error associated with manual operations. Subsequently, serverless computing and platforms such as Alteryx are utilized to integrate data from diverse sources into a unified true-source repository, following either ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) workflows. This integration facilitates seamless data transformation and mapping, applying business logic and best practices to ensure alignment with organizational data standards. Automated quality monitoring is established post-ingestion to maintain high data quality, deploying event-driven triggers to detect anomalies, validate data integrity, and promptly notify relevant stakeholders of any irregularities.

The technology stack supporting this framework includes Snowflake, AWS Redshift, and Azure Data Storage, along with relational databases like SQL Server and MySQL. These tools are selected for their robust processing capabilities and scalability, addressing challenges such as real-time data processing and storage requirements. Additionally, thorough documentation and version control are maintained to capture process updates and ensure a reliable knowledge base for future iterations.

Implementing this approach led to an 88% improvement in data accuracy and reliability for service and manufacturing operations, underscoring the importance of proactive decision-making, end-to-end validation checks, and cross-departmental collaboration on a unified data platform. This paper discusses the methodologies, technologies, and best practices applied in each stage of the data engineering process, as well as strategies to overcome common challenges in data quality, scalability, and pipeline integration. The findings and insights presented here offer a comprehensive framework for organizations seeking to enhance their data operations through automation, efficient resource utilization, and continuous monitoring.

More.. PDF

Abstract PDF Full text

Abstract 28 | PDF DOI https://doi.org/10.18535/ijecs/v11i08.4704