American Digits: Journal of Computing and Digital Technologies
American Digits: Journal of Computing and Digital Technologies is an international peer-reviewed scholarly journal published by American Observer Press. The journal publishes original research, review articles, and scholarly communications covering computing, artificial intelligence, software engineering, data science, cybersecurity, cloud computing, blockchain, digital technologies, and related interdisciplinary fields. It is committed to promoting rigorous, ethical, and impactful research that advances innovation in digital science and technology. Journal Information Publisher: American Observer Press Peer Review: Double-Blind Peer Review Publication Model: Hybrid (Standard Access & Optional Open Access) Article Submission Fee: None Publication Language: English Publication Frequency: Annual Volume with Continuous Publication Website: https://americandigits.com/ Publisher Website: https://www.americanobserver.press/ Call for Papers The journal welcomes high-quality original research articles, review papers, and interdisciplinary studies. Authors are encouraged to review the submission guidelines before submitting manuscripts through the publisher's official submission portal.
By default, articles published in this journal are available under the journal's standard copyright and access policy. Authors who choose the Open Access option by paying the applicable Open Access Charge will have their articles published under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. The applicable license will be clearly indicated on each published article.
Big Data-Driven Customer Analytics for Consumer Behavior Prediction and Sustainable Business Growth
In this research, we examine how big data analytics actually transforms raw consumer information into a roadmap for sustainable business growth. Our primary objective was to move past the usual focus on just selling more and instead look at how predictive models help a company stay resilient over the long haul. We used a systematic review methodology, pulling from a decade of secondary data and peer-reviewed studies to see how tools like machine learning are changing the game. We found that while high-tech algorithms like Random Forests are great at cutting down on wasted resources by sensing demand early, there’s still a huge trust gap because of how invasive some data collection feels. One of our major findings is that the most successful businesses are those that treat data as a way to build a more efficient, human-centric model rather than just a way to spike quarterly profits. Regarding policy, we argue that regulators and managers need to focus more on algorithmic accountability to make sure these black box systems aren't just automating old biases. Ultimately, we conclude that for a business to truly grow in a sustainable way, it has to balance its data hunger with a real commitment to consumer privacy and ethical transparency.
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Unveiling the Layers of Digital Retail: A Multidimensional Framework of the Online Shopping Experience in an Emerging Market
The rapid expansion of digital commerce has transformed consumer interactions with online retail platforms worldwide. Furthermore, comprehensive empirical models are disproportionately concentrated in developed nations, leaving an empirical gap in emerging market environments. To address these deficiencies, this study establishes and validates a comprehensive, multidimensional framework of the online shopping experience. Utilizing a quantitative research design, data were collected from a screened sample of active e-commerce consumers (N = 179). Measurement adequacy and factorability were verified through a high Kaiser-Meyer-Olkin value (KMO = 0.925) and a significant Bartlett’s Test of Sphericity (p < 0.001). Exploratory Factor Analysis (EFA) using Principal Component Analysis with Varimax rotation revealed a stable, five-factor structure exhibiting exceptional internal consistency (Cronbach's alpha = 0.957) and explaining 64.28% of the cumulative variance. The extracted dimensions include: Interactivity and Personalization (44.61%), Website Quality and Visualization (7.11%), Navigation and Information Accessibility (5.24%), Trust and Reliability (3.81%), and Customer Experience and Loyalty (3.50%). Demographic profiling indicates adoption is overwhelmingly led by young, educated, and digitally active cohorts. Theoretically, this research provides a unified matrix demonstrating that consumers evaluate digital retail environments holistically. The findings provide a data-driven framework for optimizing digital retail platforms through enhanced personalization, usability, information accessibility, and transaction security.
Cybersecurity in 5G Networks: Obstacles and Solutions for Safeguarding Next-Generation Wireless Systems
Due to their complexity and large attack surface, 5G networks pose threats and potential for cybersecurity. This paper examines these issues. The study seeks to uncover 5G network design cybersecurity challenges and assess new solutions. The paper uses secondary data from research, industry sources, and expert evaluations to discuss the expanding threat environment, including virtualization, edge computing, and IoT integration risks. Significant findings show that 5G systems need Zero-Trust Architecture, AI-powered threat detection, and Post-Quantum Cryptography to safeguard against sophisticated assaults. The report emphasizes worldwide collaboration in achieving harmonized cybersecurity standards and regulatory frameworks. Governments and companies must prioritize secure IoT devices, engage in quantum-resistant technology research, and expand public-private collaborations for threat intelligence sharing. The report concludes that 5G network security requires a multilayered strategy incorporating innovative technology and coordinated efforts to provide a durable and trustworthy foundation for the connected future.
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AI-Powered Deep Learning Models for Early Detection and Prognosis of Cancer in Bangladeshi Clinical Settings
This study investigates the design, implementation, and clinical impact of AI-powered deep learning models for early cancer detection and prognosis in Bangladeshi healthcare settings. The primary objectives were to enhance diagnostic accuracy, reduce reporting times, and integrate explainable AI into clinical workflows. Using a mixed dataset of histopathology, mammography, CT, and MRI images, models such as DenseNet121 were trained and validated through 5-fold cross-validation and prospective hospital trials. Performance metrics showed accuracies above 94% across cancer types, with Grad-CAM visualizations matching 88% of expert-marked regions. Clinical integration reduced reporting times by up to 50%, improved early detection rates by 12%, and decreased missed diagnoses by 4–6%. These results suggest that localized, explainable AI models can significantly improve diagnostic efficiency and patient outcomes. Policy implications include the need for investment in AI infrastructure, clinician training, and ethical governance to enable sustainable, nationwide adoption.
Enhanced Fixed-Point Iterative Schemes for Efficient Network Bandwidth Allocation: A Multi-Step Optimization Approach
Efficient allocation of network bandwidth is a critical challenge in optimizing network performance. In this study, we compare different fixed-point iterative schemes to determine a most effective approach for solving the bandwidth allocation problem. Our findings indicate that the three-step fixed-point iterative method significantly outperforms the one-step scheme in terms of convergence speed and efficiency. The results suggest that increasing the number of iteration steps beyond four may further enhance the convergence of the allocation point, leading to improved optimization. Building on this insight, we propose future research to develop and analyze a fixed-point iteration method with more than four steps. Such an approach has the potential to offer even greater efficiency in solving network bandwidth allocation problems. The findings of this study provide a foundation for further advancements in iterative optimization techniques for network resource management.
A Data-Driven Neuromarketing Approach to Understanding Consumer Buying Preferences
In today's digital landscape, understanding consumer buying preferences is critical for crafting effective marketing strategies. This study adopts a data-driven neuromarketing approach that integrates neuroscience, cognitive psychology, and data analytics to investigate how consumers respond to marketing stimuli. By analyzing peer-reviewed literature and recent advancements in neuroimaging, biometric technologies, and digital data processing, this research explores the intersection of consumer behavior and neural activity. The article highlights how businesses can leverage data collected from neuromarketing tools—such as EEG, eye-tracking, and facial coding—to optimize decision-making and personalize marketing campaigns. Additionally, it examines the ethical implications and technical challenges in implementing neuromarketing techniques, with an emphasis on transparency, data privacy, and scientific rigor. This approach provides a deeper, quantifiable understanding of the neural and psychological drivers of consumer decision-making.
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DevOps and Software Architecture: Bridging the Gap between Development and Operations
This research integrates DevOps ideas with software design to connect development and operations to improve contemporary software systems' agility, scalability, and sustainability. Its primary goal is to examine how architectural and DevOps approaches improve collaboration, processes, and iterative development while protecting system integrity. Secondary material such as academic literature, industry reports, and case studies is reviewed to synthesize ideas and identify best practices. Cultural shifts shared responsibility between development and operations teams, and modular designs like microservices are key to effective integration. Automation, cloud-native technologies, and observability provide agility and scalability. Maintaining flexible and robust systems requires evolutionary architecture and constant feedback loops. However, legacy system modernization, technical debt, and toolchain fragmentation remain obstacles. The study's policy recommendations are to promote cross-functional cooperation, automation, contemporary tooling, and incremental system design for long-term success. Aligning DevOps with software architecture helps firms produce high-quality software quicker and more reliably, guaranteeing agility and operational stability in a changing software development environment.
The Role of Digital Transformation in Achieving Sustainability Goals: A Preview of SAC 2024 (2.0)
Digital technologies like AI, IoT, big data, and blockchain are leveraged to improve resource efficiency, transparency, and sustainable business practices. The study examines how digital technology might be integrated with sustainability programs, utilizing case studies from Unilever, Siemens, and Walmart. Secondary data was used to evaluate sustainable digital initiative frameworks and tactics. According to research, digital technologies can reduce environmental impact and boost operational efficiency. However, hefty expenses, the digital divide, and data privacy concerns prevent widespread implementation. The report recommends financial incentives for digital innovation, digital literacy programs to close the digital divide, and robust data security standards to address these issues. As the Sustainable Action Conference (SAC) 2024 approaches, these insights can help organizations link digital transformation with sustainability and contribute to global sustainability goals.
Harnessing Kali Linux for Advanced Penetration Testing and Cybersecurity Threat Mitigation
This study aims to improve comprehension and application of solid security measures by investigating using Kali Linux for advanced penetration testing and cybersecurity threat mitigation. The main goals are to evaluate the platform's extensive toolkit, show how well it works for proactive vulnerability management, and emphasize its adaptability to various industry types. To demonstrate the usefulness and practical applicability of the methodology, case studies from the financial, healthcare, and e-commerce industries are analyzed using secondary data-based assessment. Important discoveries show that Kali Linux's wide range of tools makes it possible to do in-depth vulnerability evaluations, sophisticated exploitation, and post-exploitation techniques—all essential for detecting and thwarting cyber threats. The platform's educational value promotes increased cybersecurity knowledge and readiness, while its versatility and adaptability assure its use in various situations. Policy implications indicate that enterprises should incorporate Kali Linux's complete capabilities and constant updates into their cybersecurity policies to take advantage of Kali Linux's full capabilities. This integration will protect sensitive data and information systems by strengthening proactive threat management, improving system security, and offering a solid defense against changing cyber threats.
Edge Computing in Network-based Systems: Enhancing Latency-sensitive Applications
With its vast improvements in latency-sensitive applications like industrial automation, healthcare diagnostics, and autonomous vehicles, edge computing is transforming network-based systems. This study aims to investigate the advantages, difficulties, and prospective applications of edge computing—in particular, its ability to lower latency and boost efficiency. The study thoroughly analyzes secondary data sources to highlight essential conclusions such as significant latency reduction, the need for 5G integration, advances in AI and machine learning, and solid security measures. The challenges mentioned include scalability, interoperability, and the requirement for sustainable practices. The policy implications include the necessity of standardization initiatives, funding for cutting-edge security measures, and incentives for environmentally friendly edge computing. The study concludes that although edge computing has a lot of promise, its widespread adoption depends on how these issues are resolved through creative solutions and supporting regulations. The findings highlight the revolutionary effect of edge computing on contemporary network-based systems and offer insightful information on the state of the industry and emerging trends.
Efficient Neural Network Deployment: A Review of Compression Techniques for Edge Computing
This review talks about the growing difference in intelligence between big deep learning architectures and edge hardware that has strict resource limits, like mobile SoCs and IoT sensors. The main goal is to find out how to use different compression methods, like pruning, quantization, and knowledge distillation, without slowing down real-time performance. Our method is based on a systematic look at secondary data from top-tier peer-reviewed research. We compare theoretical accuracy gains with real hardware benchmarks like latency and energy use. The main findings show that while pruning offers high theoretical compression, quantization (especially INT8) is still the best way to save power right away in the industry. Also, it has been shown that hardware-aware optimization and hybrid techniques work much better than isolated methods. We come to the conclusion that there is an urgent need for standardized reporting metrics and policy frameworks to make sure that compressed models are safe and reliable in autonomous systems. This study gives engineers a useful set of tools to help them move AI from the cloud to the edge.
Copyright (c) 2023 American Observer Press https://americandigits.com/ad/copyright
Integrating Data Remediation Strategies in Robotic Data Processing
This research applies data remediation methodologies to robotic data processing systems to improve data quality and reliability in automated processes. The main goals are to evaluate rule-based and AI-driven error detection systems, identify obstacles in implementing error correction in large robotic architectures, and evaluate data integrity after real-time error correction. The research uses secondary sources to evaluate literature and case studies to identify optimal practices and problems. Significant results show that rule-based systems identify frequent mistakes, while AI-driven techniques adapt better to changing data contexts. Due to system integration complexity and error correction mechanism scalability, innovative algorithmic solutions are needed. Standardized frameworks and procedures for continuous monitoring, real-time correction, and scalability in robotic data processing are stressed in the research. Organizations seeking data integrity in complex and data-intensive situations must consider these policy consequences. The results add to the discussion on strategic data remediation practices to improve automated data processing system efficiency and dependability.
Economic Modeling with Brain-Computer Interface Controlled Data Systems
Economic Modeling using BCI technology improves this study's forecasting accuracy and decision-making speed. The primary objectives are to evaluate how BCI cognitive data (EEG, fNIRS, MEG, fMRI, and ECoG) influences economic forecasts and address ethical and privacy concerns. Secondary data assesses prediction accuracy and decision-making speed before and after BCI data integration. Many BCI technologies improved model performance, including 18.9% prediction accuracy and 31.6% decision-making speed. These discoveries demonstrate how real-time BCI cognitive insights might enhance economic forecasting. However, data security, integration complexity, and high expenses must be addressed. The paper recommends solid legal frameworks, data integration standards, and BCI technological funding and assistance to address these obstacles. Data protection laws and ethical standards are needed to guarantee appropriate BCI usage. BCI technology has transformational potential for economic Modeling, but its limits must be considered, and sound policies must be adopted to maximize its advantages.
The Role of Kiosks in Omni-Channel Retail Strategies: A Market Perspective
This study looks at kiosks' place in omni-channel retail strategies from a market standpoint. It hopes to provide important information about how they are implemented, how they affect the customer experience, what the future holds, and how policies are concerned. Using a combination of literature review, data analysis, and case studies, the study uses a mixed-method approach to examine the various facets of kiosk integration in retail. Important discoveries show that kiosks work as Omni channel integration catalysts, improving consumer interaction, accessibility, and personalized recommendations. Integrating AR/VR, contactless solutions, and sustainability initiatives are among the emerging trends that provide merchants substantial prospects to introduce novel ideas and set themselves apart from competitors. Nevertheless, the report points out privacy concerns and the shortcomings of the digital divide, advocating for regulatory changes to resolve these problems and promote a more consumer-focused and inclusive approach to kiosk integration. In summary, this research offers significant perspectives for merchants, decision-makers, and industry participants to effectively manage the intricacies of the retail domain and promote sustainable expansion in the era of digitalization.
Enhancing PCB Reliability through Cutting-edge Circuit Simulator Applications
The primary goals of this study are to improve thermal management, signal integrity, power integrity, and mechanical reliability. It also investigates the revolutionary effects of state-of-the-art circuit simulator applications on PCB dependability. The paper applies a secondary data review methodology and uses case studies and real-world applications to demonstrate the advantages of advanced simulation techniques. Key conclusions show that power integrity simulations provide steady power delivery, mechanical simulations strengthen PCBs against physical stresses, thermal simulations efficiently lower operating temperatures, and signal integrity studies limit crosstalk and EMI. Furthermore, combining machine learning and artificial intelligence with circuit simulators provides predictive insights and speeds up the design process, resulting in creative and reliable PCB solutions. The policy implications indicate that design efficiency can be significantly increased using these cutting-edge simulation tools, and development time and costs can decrease. High-quality electronic devices that satisfy the demanding requirements of modern technology may be produced.
Revolutionizing PLC Systems with AI: A New Era of Industrial Automation
This study investigates the revolutionary effects of combining PLCs (programmable logic controllers) and artificial intelligence (AI) in industrial automation. The primary goals are to discover how artificial intelligence (AI) improves productivity, sustainability, and quality control in industrial processes and identify problems and potential solutions related to AI-driven PLC systems. Methodologically, the applicability and advantages of AI algorithms, such as machine learning and deep learning, in PLC systems were examined through a thorough analysis of recent literature and case studies. Important conclusions show that integrating AI significantly improved process efficiency, real-time adaptation, and energy optimization. The accuracy of AI-driven solutions in defect identification helps quality control, while AI-driven solutions support cybersecurity measures. The policy implications highlight the necessity for robust regulatory frameworks to direct the moral use of AI in industrial contexts. According to the study's findings, AI-enhanced PLC systems are a significant development in industrial automation that promises increased manufacturing productivity, sustainability, and innovation in the future.
Innovations in Payment Processing: Integrating Accelerated Testing for Enhanced Security
This study uses accelerated testing and modern technology to improve payment processing system security and efficiency. The primary goals are to identify and evaluate blockchain, AI, machine learning, and biometric authentication advances in protection and performance. The study uses secondary data to demonstrate the revolutionary power of these technologies and the importance of automated, continuous, and AI-driven testing. Main findings: blockchain is secure and decentralized, AI and ML improve real-time fraud detection, and biometric authentication lowers unwanted access. Faster testing methods identify and fix vulnerabilities, ensuring system integrity and meet changing regulatory demands. The study emphasizes the need for constant monitoring and investment in advanced testing technologies despite cybersecurity threats, regulatory compliance, interoperability, scalability, and user experience. Policy implications show that integrating these technologies and tackling associated problems can considerably improve payment processing system resilience and reliability, ensuring a secure and seamless user experience in a digital financial ecosystem.
Optimizing Web Performance While Enhancing Front End Security for Delta Airlines
This article investigates the dual objective of optimizing web performance while enhancing front end security for Delta Airlines, a leading player in the aviation industry. The study employs a comprehensive methodology involving the implementation of performance optimization techniques and robust security measures. Key objectives include improving page load times, enhancing user experience, and fortifying front end security through HTTPS implementation, content security policies (CSPs), and regular security audits. Findings reveal significant enhancements in web performance metrics such as reduced page load times, improved time to interactive (TTI), and enhanced security posture with effective mitigation of cyber threats. The study emphasizes the strategic alignment of performance and security objectives, regulatory compliance with data protection standards, and the imperative for continuous monitoring and adaptation in a dynamic digital landscape. These insights underscore Delta Airlines' commitment to delivering secure, efficient, and customer-centric digital services in the competitive aviation sector.