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Smart Product Packing and IoT Marketing: Enhancing Customer Interaction
The convergence of smart product packaging and IoT marketing has transformed commerce. This study examines the fundamental ramifications of convergence and its potential to improve customer engagement. Our research shows the transformational potential of these technologies via quantitative and qualitative analyses.Smart packaging outperforms non-smart items, giving firms an advantage, according to quantitative data. Regression and correlation analysis confirm IoT data-customer interaction. Our study also emphasizes ethical data acquisition, which supports data privacy and consumer protection.Consumers may expect personalized experiences, transparency, and real-time feedback from this technology transformation. Smart product packaging and IoT marketing enable readers to make educated decisions and influence product development to meet changing consumer expectations.This research allows academics to study the ideas and models that affect consumer engagement. Data privacy and consumer protection may inform IoT marketing and smart device packaging policy.Our research guides organizations and customers towards better customer interactions, data-driven decision-making, and ethical data practices in this changing age. The future promises revolutionary customer contact. 2023 IEEE. -
A Machine Learning Model for Augmenting the Media Accessibility for the Disabled People
In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called 'Fast Captioning (FC)' to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern Machine Learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. Machine Learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate. 2023 IEEE. -
Hyperledger Fabric as a Secure Blockchain Solution for Healthcare 4.0 Framework
The healthcare sector deals with extremely sensitive information that must be administered in a safe and confidential way. The objective of the proposed framework is to utilize Blockchain Technology (BT) for tracking medical prescriptions and the implementation is carried out using the Hyperledger Fabric platform, an enterprise-grade open-source distributed ledger technology platform designed for Bigdata applications. Multiple entities, including patients, e-pharmacies, pharmacies, doctors and hospitals can establish connections by introducing several nodes in the Fabric chain. A web-centered application is provided for doctors, connecting them with participating pharmacies, hospitals and e-pharmacies through which, they can share patient prescription. Pharmacies and e-pharmacies have access to this data and can notify patients about the availability of prescribed medicines. Additionally, reminders for refills, such as heart medication, can be sent for patients requiring long-term medication. Patients can also try with nearby pharmacies and the availability of their prescribed medicines. The inclusion of a wallet feature in the application enables patients to use mobile tokens for making purchases. Patient data is treated with the utmost confidentiality, kept private, and accessed only upon request and with the consent of the concerned parties. This privacy is ensured through the use of zero-knowledge proof. Patients retain access to their complete medical history, facilitating interactions with doctors without the need for repetitive information sharing. 2023 IEEE. -
Remote Diabetic Retinopathy Screening with IoT and Machine Learning on Edge Devices
This study presents a novel method of screening for diabetic retinopathy using edge devices the Internet of Things and machine learning. The developed remote screening system ensures broad accessibility as well as affordability by overcoming geographical barriers. While edge computing maximizes real-time analysis, the integration of sophisticated machine learning algorithms improves diagnostic accuracy. The investigation of socio-technical subtleties is guided by the interpretivist philosophy. The outcomes show a strong architecture, effective models, as well as revolutionary effects on accessibility. A critical assessment finds the good points and continuous improvements. Suggestions place a strong emphasis on scaling issues and the ongoing improvement of machine learning models. In order to secure data management and keep up with changing healthcare needs, future research suggests combining blockchain technology with sophisticated imaging modalities. This study advances early detection, enhances accessibility to healthcare, and advances remote screening technologies. 2023 IEEE. -
Artificial Intelligence, Smart Contracts, and the Groundbreaking Potential of Blockchain technology: Unlock the Next Generation of Innovation
The blockchain technology consists of blocks and is a decentralized network of nodes (miners). Each block is made up of three parts: the data, the hash, and the hash from the previous block. After data has been stored, it is extremely difficult to temper the data. Transactions are verified by miners, who are compensated with a commission for their labor. Readers will gain a comprehensive understanding of blockchain technology from this review article, including how it may be used in a variety of industries including supply chains, healthcare, and banking. Most individuals were already familiar with Bitcoin as one of the well-known blockchain applications. In this section, we'll discuss a few of the countless research publications on the cutting-edge applications of this technology. We'll talk about the challenges that come with actually using these applications as well. Blockchain is an industry that is growing thanks to its more recent applications in a number of fields, such as hospital administration, cryptocurrency use, and other places. Only the manner that blockchain works and runs makes it possible for these applications. 2023 IEEE. -
SARIMA Techniques for Predictive Resource Provisioning in Cloud Environments
Seasonal Autoregressive Integrated Moving Average (SARIMA) models for dynamic cloud resource provisioning are introduced and evaluated in this work. Various cloud-based apps provided historical data to train and evaluate SARIMA models. The SARIMA(1,1,1)(0,1,1)12 model has an MAE of 0.056 and an RMSE of 0.082, indicating excellent prediction ability. This model projected resource needs better than other SARIMA settings. Sample prediction vs. real study showed close congruence between projected and observed resource consumption. MAE improved with hyperparameter adjustment, according to sensitivity analysis. Moreover, SARIMA-based resource allocation improved CPU usage by 12.5%, RAM utilization by 20%, and storage utilization by 21.4%. These data demonstrate SARIMA's ability to forecast cloud resource needs. SARIMA-based resource management might change dynamic cloud resource management systems due to cost reductions and resource usage efficiency. This research helps industry practitioners improve cloud-based service performance and cost. 2023 IEEE. -
Leveraging Model Distillation as a Defense Against Adversarial Attacks Based on Deep Learning
Adversarial attacks on deep learning models threaten machine learning system security and reliability. The above attacks use modest data alterations to produce erroneous model results while being undetected by humans. This work suggests model distillation to prevent adversarial perturbations. The student model is taught to emulate the teacher model in model distillation. This is done using teacher model soft outputs. Our idea is that this strategy organically strengthens the student model against adversarial assaults by keeping the teacher model's essential knowledge and generalization capabilities while reducing weaknesses. Distilled models are more resilient to adversarial assaults than non-distilled models, according to experiments. These models also perform similarly on undamaged, uncorrupted data. The results show that model distillation may be a powerful defense against machine learning adversaries. This method protects model resilience and performance. 2023 IEEE. -
Detection and Robust Classification of Lung Cancer Disease Using Hybrid Deep Learning Approach
Effective lung cancer diagnosis and treatment hinge on the early detection of lung nodules. Various techniques, such as thresholding, pattern recognition, computer-aided diagnostics, and backpropagation calculations, have been explored by scientists. Convolutional neural networks (CNNs) have emerged as powerful tools in recent times, revolutionizing many aspects of this field. However, traditional computer-aided detection systems face challenges when categorizing lung nodule detection. Excessive reliance on classifiers at every stage of the process results in diminished recognition rates and an increased occurrence of false positives. To address these issues, we present a novel approach based on deep hybrid learning for classifying lung lesions. In this study, we explore multiple memory-efficient and hybrid deep neural network (DNN) architectures for image processing. Our proposed hybrid DNN significantly outperforms the current state-of-the-art, achieving an impressive accuracy of 95.21%, all while maintaining a balanced trade-off between specificity and sensitivity. The primary focus of this research is to differentiate between CT scans of patients who have early-stage lung cancer and those who do not. This is achieved by utilizing binary classification networks, including standard CNN, SqueezeNet, and MobileNet. 2023 IEEE. -
Comparative Analysis of Maize Leaf Disease Detection using Convolutional Neural Networks
Worldwide, maize is a significant cereal crop for crop productivity, identifying diseases in the plant's leaves is essential to raise a good crop. Deep learning methods that have been used in recent years to precisely identify and categorize these serious diseases, offering a non-destructive and effective way to find maize leaf ailments. In order to detect maize leaf disease, this paper suggests using three well-liked deep learning models: VGG16, Inception V3, and EfficientNet. The models were trained and assessed using a datasets of 4000 images of three distinct maize leaf diseases and a healthy class. All three models had high accuracy rates, according to the results, though EfficientNet outperformed the other two models. The suggested method can detect and track diseases in maize crops with high accuracy and can be applied practically. It can accurately classify various diseases. The study also demonstrates that deep learning models can offer a trustworthy and effective solution for detecting crop diseases, which can aid in lowering crop losses, raising crop yields, and enhancing food security. 2023 IEEE. -
Twitter Sentiment Analysis and Emotion Detection Using NLTK and TextBlob
On an average, approximately 7000 tweets are communicated each second and in total it piles up to around 300 billion tweets every year. Society are free to contribute their opinions on public platform and hence it acts as a reliable interface to assess society ongoing viewpoint and attitude over any matter or event. Consumers very often make use of social media to exchange their views about anything. Business may get domain for enhancement and smooth interpretation of the behavior of people regarding various facts through opinion mining. Thus to carry out this mining of opinions on social media interface, textual categorization with language analysis is of great help. With the help of NLP token tool, phrases can be divided into various word series after dropping stop phrases. Larger tweets tokenizing and classifying into distinct labels is a concern. Thus, the main objective of this framework is to process the tweets based on specific keywords given by user, categorize these phrases into negative, positive and neutral ones. TextBlob module assists users and developers to interpret user sentiments about a news. This research tries to give suggestion a textual opinion assessment on social media samples utilizing the NLTK and TextBlob modules. 2023 IEEE. -
KESMR: A Knowledge Enrichment Semantic Model For Recommending Microblogs
In today's world, there's an enormous amount of information available on the Internet. Because of this, it's become really important to come up with better and smarter ways to search for things online. The old-fashioned methods, like just matching certain words or using statistics, don't work so well anymore. They often suggest web pages that are irrelevant. As the Semantic Web keeps getting bigger, it needs algorithms for the best fit. In this paper, a way to measure how different the words used for web search. This helps in suggesting the most relevant web pages. A special algorithm that can change its settings. Our proposed method demonstrates 94% accuracy. 2023 IEEE. -
Time Series Forecasting of Stock Market Volatility Using LSTM Networks
Forecasting stock market volatility is a pivotal concern for investors and financial institutions alike. This research paper employs Long Short-Term Memory (LSTM) networks, a potent class of recurrent neural networks, to predict stock market volatility. LSTM networks have proven adept at capturing intricate temporal dependencies, rendering them a fitting choice for time series data analysis. We commence by elucidating the notion of stock market volatility and its profound significance in financial decision-making. Traditional methodologies, such as GARCH models, exhibit shortcomings in deciphering the convoluted dynamics inherent in financial time series data. LSTM networks, with their capacity to model extended temporal relationships, present an encouraging alternative. In this study, we assemble historical stock price and trading volume data for a diverse array of assets, diligently preprocessing it to ensure its aptness for LSTM modeling. We systematically explore various network architectures, hyperparameter configurations, and input features to optimize the efficacy of our models. Our empirical investigations decisively underscore the supremacy of LSTM networks in capturing the subtleties of stock market volatility compared to conventional techniques. As the study progresses, we delve deeper into the complexities of LSTM network training, leveraging advanced techniques such as batch normalization and dropout to fortify model resilience. Moreover, we delve into the interpretability of LSTM models within the context of stock market forecasting. 2023 IEEE. -
Explaining Autism Diagnosis Model Through Local Interpretability Techniques - A Post-hoc Approach
In this era of machine learning and deep learning algorithms dominating the Artificial Intelligence (AI) world, the trustworthiness of these black box models is still questionable. Life-caring sectors like healthcare and banking make use of these black box models as assistance in critical decision-making processes, but the degree of reliability of these decisions is still uncertain. This is because these black box models will not reveal the causation of the predicted outcome. However, creating an interpretable model that can explain the internal workings of these black box models can provide some reliable insights and trustable justifications for the predicted outcome. This study aimed to create an interpretable model for autism diagnosis which can give some trustable explanations for its predicted outcome. Using local interpretability methods such as LIME, SHAP, and Anchors the predicted outcome for each instance is explained well with some standard visual representations. As a result, this study developed an interpretable autism diagnosis model with an accuracy rate of 91.37% and with good local model explanations. 2023 IEEE. -
Automation using Artificial Intelligence in Business Landscape
The integration of Artificial Intelligence (AI) with automation has sparked a remarkable transformation in the contemporary business landscape, promising elevated efficiency and quality. However, this convergence encounters multifaceted challenges, notably in the adoption of recent AI techniques such as deep learning, reinforcement learning, and natural language processing. These techniques, while potent, grapple with challenges in data quality, interpretability, and ethical considerations. In this study, we aim to delineate the intricate interplay between AI and automation, illuminating their collective potential to augment operational efficiency and confer a competitive advantage. Through a comprehensive review, we will explore the effective integration of these technologies, navigating hurdles such as data bias, system compatibility, and human-machine collaboration. Here, the primary research objective is to provide insights on optimizing the outcomes by synergizing AI and automation while addressing the inherent challenges, ultimately fostering sustainable and impactful implementations in organizational frameworks. 2023 IEEE. -
A Comprehensive Study of Blockchain Technology Based Decentralised Ledger Implementations
Information management and decentralized, secure transactions are made possible by the groundbreaking idea of blockchain technology. Blockchain has attracted considerable attention from a wide range of businesses as a result of the rising popularity of cryptocurrencies like Bitcoin and Ethereum. The primary goal of this paper is to present a thorough examination of decentralized ledger systems based on blockchain technology. It examines at the basic ideas, underpinnings, and real-world uses of blockchain in many industries. The ability to scale, effectiveness, privacy, security, seamless integration, governing scenarios, and consumption of energy are just a few of the technical factors that are looked at in relation to the deployment of blockchain technology. In this research paper we also pinpoint forthcoming developments and future prospects for the blockchain industry like Solutions for scalability at the subsequent layer, protocols for seamless integration, integration with the Internet of Things (IoT), apps for decentralized finance (DeFi), and digital currencies that are issued by central banks (CBDCs) are a few examples. 2023 IEEE. -
EEG Signals Acquisition and Processing of Mental Tasks for Controlling Smart Systems
With the help of this biosignal, we can create various kinds of interfaces. These interfaces were especially used for paralyzed individuals who have problems in the normal bio-sensor channels. Electroencephalogram (EEG) signals play a vital role in semi-paralyzed and fully paralyzed individuals to make communication with their caretakers. EEG signals are used not only for paralyzed individuals, nowadays most of home appliances are designed due to their reliability and accuracy. EEG based Smart devices are very easy to operate because signals are generated by the human brain automatically whenever the task is performed by humans. Through this research, we discussed the basic methodology needed for interfacing the EEG signals with smart devices. From this paper, most of the researchers will be going to know how to connect the EEG signals with the smart systems to control the external devices and induce the researchers to apply the EEG signals in real time. 2023 IEEE. -
Early Detection of Cervical Cancer using Machine Learning Classifiers for Improved Diagnosis in Underserved Regions
One of the incurable diseases that affect women is cervical cancer. It is brought on by a protracted infection of the skin and the vaginal mucous membrane cells. The Human Papilloma Virus (HPV), is the main factor causing aberrant cell proliferation in the area around the cervix. There are no symptoms present when the illness first appears. Early detection of this malignancy may be used to prevent death. People in less developed countries cannot afford to periodically examine themselves due to a lack of awareness, poor medical infrastructure, and expensive medication. The EDA technique is applied to examine the data and understand its characteristics. Machine Learning algorithm has been used to diagnose cervical cancer. In order to spot the existence of cervical cancer, five machine learning classifiers are utilized, the algorithms to begin earlier. The Logistic Regression classifier's results validate the correct stage prediction. 2023 IEEE. -
Enhanced Social Media Profile Authenticity Detection Using Machine Learning Models and Artificial Neural Networks
Fake engagement is one of the main issues with online networks or ONSs, which are used to artificially boost an account's popularity, this study examines the effectiveness of seven sophisticated Machine Learning Algorithms, Random Forest Classifier, Decision Tree Classifier, XGBoost, LightGBM, Extra Trees Classifier, and SVM, and got 93% accuracy in Decision Tree Classifier. In order to solve overfitting issues and improve model resilience, the paper proposes Generative Adversarial Networks (GANs) and uses K-Fold Cross-Validation. Furthermore, design a Gan-ANN model that combines Batch Normalization and Artificial Neural Networks (ANN) with GAN-generated synthetic data is investigated. The enhanced dataset seeks to strengthen model performance and generalization when combined with cutting-edge modeling methods. This study aims to improve model scalability, predictive accuracy, and dependability across different machine learning paradigms. 2023 IEEE. -
Dynamic Behaviour Analysis of Multi-Cell Battery Packs: A Simulation Study
In the era of IoT understanding the dynamic behavior of a Lithium-ion Battery Management System (BMS) has become gradually more important. This research investigates the dynamic behaviour of a six-cell Lithium-ion Battery Management System (BMS) through simulation. The study employs a comprehensive model encompassing key battery parameters, including cell capacity, voltage limits, temperature thresholds, and charge/discharge characteristics. Additionally, state variables such as State of Charge (SOC), State of Health, and State of Function are integrated to capture the battery's internal dynamics. The simulation incorporates a sinusoidal current profile to emulate realistic operating conditions. Notably, Coulomb counting is employed for SOC estimation, and protective measures against overvoltage, undervoltage, and overcurrent are implemented. The study also addresses balancing strategies and communication interfaces within the BMS. The results reveal nuanced interactions between voltage, temperature, SOC, and current, offering insights into the intricate behaviour of the battery system under dynamic conditions. This research not only advances our understanding of BMS functionality but also lays a crucial foundation for the evolution of battery technology and energy management systems in the IoT landscape. The Institution of Engineering & Technology 2023. -
A Self-Attention Bidirectional Long Short-Term Memory for Cold Start Movie Recommendation Models
Movie recommendation systems are useful tools that help users find relevant results and prevent information overload. On the other hand, the user cold-start issue has arisen because the system lacks sufficient user data. Furthermore, they are not very scalable for use in extensive real-world applications. One of the key strategies to address the sparsity and cold-start problems is to leverage other sources of information, including item or user profiles or user reviews. Processing client feedback is typically a challenging process that involves challenging the interpretation and analysis of the textual data. Thus, this research implements an efficient deep learning-based recommendation architecture. Following the acquisition of textual data from the Amazon product reviews database, stop word removal, lemmatization, and stemming techniques are applied to the data pre-processing which eliminate inconsistent and redundant data, facilitating the process of interpreting and utilising data. Then, the Term Frequency-Inverse Document Frequency (TF-IDF) method is applied to extract the feature values from the pre-processed text data. The extracted feature values are fed to the Self-Attention Bidirectional Long Short-Term Memory (SA-BiLSTM) that utilises the matrix factorization method framework's information sources. The SA-BiLSTM model obtained 95.93% of recall, 94.76% of precision, and 97.84% of accuracy on the amazon product reviews database. 2023 IEEE.