Vol 1, No 1 (2022) |
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1. Human Touch in Retail: Analyzing Customer Loyalty in the Era of Self-Checkout Technology
Pages 1-13 || Authors: Dipakkumar Kanubhai Sachani, Sunil Kumar Reddy Anumandla, Sai Sirisha Maddula
ABSTRACT
The paper explores the complex interplay among customer loyalty, technology advancement, and human interaction in the retail industry. The study intends to clarify the effect of self-checkout technology on customer loyalty and propose solutions for businesses to navigate this changing landscape through a thorough investigation of theoretical frameworks and empirical research. The study's methodology synthesizes significant thoughts and conclusions through data analysis and reviewing previous research. Key findings highlight the significance of balancing human interaction and technical efficiency, prioritizing emotional engagement to encourage loyalty, and modifying retail methods to accommodate changing customer preferences. The policy implications highlight the necessity of legal frameworks that support the ethical integration of self-checkout technology while preserving the integrity of retail experiences that prioritize the needs of people. Ultimately, the study offers insightful advice and strategic imperatives to help merchants negotiate the future of retail with resilience and confidence, allowing them to maximize customer loyalty in the age of self-checkout technology.
Key words:
Human Touch, Retail Experience, Customer Loyalty, Self-Checkout Technology, Consumer Behavior, Personal Interaction, Emotional Engagement, Technological Disruption
The paper explores the complex interplay among customer loyalty, technology advancement, and human interaction in the retail industry. The study intends to clarify the effect of self-checkout technology on customer loyalty and propose solutions for businesses to navigate this changing landscape through a thorough investigation of theoretical frameworks and empirical research. The study's methodology synthesizes significant thoughts and conclusions through data analysis and reviewing previous research. Key findings highlight the significance of balancing human interaction and technical efficiency, prioritizing emotional engagement to encourage loyalty, and modifying retail methods to accommodate changing customer preferences. The policy implications highlight the necessity of legal frameworks that support the ethical integration of self-checkout technology while preserving the integrity of retail experiences that prioritize the needs of people. Ultimately, the study offers insightful advice and strategic imperatives to help merchants negotiate the future of retail with resilience and confidence, allowing them to maximize customer loyalty in the age of self-checkout technology.
Key words:
Human Touch, Retail Experience, Customer Loyalty, Self-Checkout Technology, Consumer Behavior, Personal Interaction, Emotional Engagement, Technological Disruption
Cite as:
Sachani, D. K., Anumandla, S. K. R., Maddula, S. S. (2022). Human Touch in Retail: Analyzing Customer Loyalty in the Era of Self-Checkout Technology. Silicon Valley Tech Review, 1(1), 1-13.
Sachani, D. K., Anumandla, S. K. R., Maddula, S. S. (2022). Human Touch in Retail: Analyzing Customer Loyalty in the Era of Self-Checkout Technology. Silicon Valley Tech Review, 1(1), 1-13.
2. Deep Learning Approaches for Signal and Image Processing: State-of-the-Art and Future Directions
Pages 14-34 || Authors: Takudzwa Fadziso, Rahimoddin Mohammed, Kanaka Rakesh Varma Kothapalli, Manzoor Anwar Mohammed, Raghunath Kashyap Karanam
ABSTRACT
This abstract offers a comprehensive summary of a study that explores deep-learning techniques for Signal and image processing. It covers the main goals, methodology, fundamental discoveries, and potential policy implications. The study examines the latest advancements and future directions in deep learning techniques for signal and image processing tasks. By analyzing various literature sources extensively, we delve into the latest advancements in model architectures, training techniques, and application domains. Notable discoveries highlight impressive progress in artificial intelligence, particularly deep neural network architectures, attention mechanisms, and generative adversarial networks. However, there are still obstacles to overcome, including scalability, efficiency, and the ability to interpret models. It is crucial to address data bias, privacy, resource inequality, and ethical guidelines to develop and deploy deep learning technologies responsibly. The policy implications highlight the significance of these issues. The study provides insights into the ever-changing field of deep learning for Signal and image processing, showcasing possibilities for creativity and positive effects on society.
Key words:
Deep Learning, Signal Processing, Image Processing, State-of-the-Art, Neural Networks, AI Techniques, Machine Learning
This abstract offers a comprehensive summary of a study that explores deep-learning techniques for Signal and image processing. It covers the main goals, methodology, fundamental discoveries, and potential policy implications. The study examines the latest advancements and future directions in deep learning techniques for signal and image processing tasks. By analyzing various literature sources extensively, we delve into the latest advancements in model architectures, training techniques, and application domains. Notable discoveries highlight impressive progress in artificial intelligence, particularly deep neural network architectures, attention mechanisms, and generative adversarial networks. However, there are still obstacles to overcome, including scalability, efficiency, and the ability to interpret models. It is crucial to address data bias, privacy, resource inequality, and ethical guidelines to develop and deploy deep learning technologies responsibly. The policy implications highlight the significance of these issues. The study provides insights into the ever-changing field of deep learning for Signal and image processing, showcasing possibilities for creativity and positive effects on society.
Key words:
Deep Learning, Signal Processing, Image Processing, State-of-the-Art, Neural Networks, AI Techniques, Machine Learning
Cite as: Fadziso, T., Mohammed, R., Kothapalli, K. R. V., Mohammed, M. A., Karanam, R. K. (2022). Deep Learning Approaches for Signal and Image Processing: State-of-the-Art and Future Directions. Silicon Valley Tech Review, 1(1), 14-34.
3. Sentiment Analysis in IoT Data Streams: An NLP-Based Strategy for Understanding Customer Responses
Pages 35-47 || Authors: Aditya Manikyala
ABSTRACT
This research uses NLP to analyze IoT data streams for sentiment analysis to understand and react to consumer emotions and actions in real-time. This study investigates how NLP can handle multi-modal IoT data, including text, speech, and sensor measurements, to discover sentiment indicators and deliver customer satisfaction insights. The research addresses the problems of incorporating real-time sentiment analysis into IoT contexts via a secondary data assessment, including data volume, velocity, and multi-modal model computational complexity. The key results include multi-modal data integration, real-time processing frameworks, and edge computing for sentiment analysis. Contextual sensitivity and model improvement methods like distillation also improve sentiment accuracy. The paper also emphasizes explainability in AI models, particularly in sensitive applications, and recommends clear, ethical frameworks to protect data privacy and user trust. Policy implications show that IoT settings require strong data privacy and AI transparency policies to protect consumer data and promote ethical usage of AI-driven sentiment analysis technology. The study indicates that NLP-based sentiment analysis may improve IoT customer experience by providing real-time, data-driven insights into user preferences and behaviors.
Key words:
Sentiment Analysis, IoT Data Streams, Natural Language Processing (NLP), Customer Response, Real-Time Sentiment Detection, Multi-Modal Data, Edge Computing
This research uses NLP to analyze IoT data streams for sentiment analysis to understand and react to consumer emotions and actions in real-time. This study investigates how NLP can handle multi-modal IoT data, including text, speech, and sensor measurements, to discover sentiment indicators and deliver customer satisfaction insights. The research addresses the problems of incorporating real-time sentiment analysis into IoT contexts via a secondary data assessment, including data volume, velocity, and multi-modal model computational complexity. The key results include multi-modal data integration, real-time processing frameworks, and edge computing for sentiment analysis. Contextual sensitivity and model improvement methods like distillation also improve sentiment accuracy. The paper also emphasizes explainability in AI models, particularly in sensitive applications, and recommends clear, ethical frameworks to protect data privacy and user trust. Policy implications show that IoT settings require strong data privacy and AI transparency policies to protect consumer data and promote ethical usage of AI-driven sentiment analysis technology. The study indicates that NLP-based sentiment analysis may improve IoT customer experience by providing real-time, data-driven insights into user preferences and behaviors.
Key words:
Sentiment Analysis, IoT Data Streams, Natural Language Processing (NLP), Customer Response, Real-Time Sentiment Detection, Multi-Modal Data, Edge Computing
4. Blockchain-Enhanced Machine Learning for Predictive Analytics in Precision Medicine
Pages 48-60 || Authors: Abhishake Reddy Onteddu; Kawsher Rahman; Charlotte Roberts; RamMohan Reddy Kundavaram; Srinikhita Kothapalli
ABSTRACT
This research integrates Blockchain and machine learning (ML) for precision medicine predictive analytics to solve data privacy, security, interoperability, and trust issues. Secondary data from peer-reviewed publications, case studies, and technical reports are reviewed to examine blockchain-enhanced ML's potential and limits in healthcare. Researchers found that Blockchain increases data integrity, secure data sharing, and ML model transparency, boosting healthcare stakeholder trust and cooperation. Privacy rules like GDPR and HIPAA are met while the connection allows individualized treatment recommendations, early illness identification, and enhanced clinical trials. According to the report, scalability, legacy system integration, and regulatory difficulties hinder adoption. Policy implications emphasize the need for clear legislative frameworks that balance innovation and privacy and promote stakeholder engagement to address these challenges. This research sheds light on how Blockchain and ML may be used synergistically to enhance precision medicine and provide more secure, transparent, and effective healthcare solutions.
Key words:
Blockchain, Machine Learning, Predictive Analytics, Precision Medicine, Data Security, Healthcare Interoperability, Smart Contracts
This research integrates Blockchain and machine learning (ML) for precision medicine predictive analytics to solve data privacy, security, interoperability, and trust issues. Secondary data from peer-reviewed publications, case studies, and technical reports are reviewed to examine blockchain-enhanced ML's potential and limits in healthcare. Researchers found that Blockchain increases data integrity, secure data sharing, and ML model transparency, boosting healthcare stakeholder trust and cooperation. Privacy rules like GDPR and HIPAA are met while the connection allows individualized treatment recommendations, early illness identification, and enhanced clinical trials. According to the report, scalability, legacy system integration, and regulatory difficulties hinder adoption. Policy implications emphasize the need for clear legislative frameworks that balance innovation and privacy and promote stakeholder engagement to address these challenges. This research sheds light on how Blockchain and ML may be used synergistically to enhance precision medicine and provide more secure, transparent, and effective healthcare solutions.
Key words:
Blockchain, Machine Learning, Predictive Analytics, Precision Medicine, Data Security, Healthcare Interoperability, Smart Contracts