International Journal of Innovations in Engineering Research and Technology
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NOVEL SMART CONTACT LENSES WITH EMBEDDED AI FOR PIONEERING CONTINUOUS HEALTH TRACKING AND DISEASE DETECTION
Full text linkSmart contact lenses equipped with embedded artificial intelligence (AI) represent a groundbreaking advancement in continuous health monitoring and disease detection. This paper explores the development and application of smart contact lenses that integrate real-time data collection with sophisticated AI algorithms to monitor various health parameters. By incorporating sensors and AI processing units directly into the lenses, these devices can continuously measure and analyze biometric data such as glucose levels, intraocular pressure, and ocular health indicators. The embedded AI system processes this data to provide real-time feedback, identify potential health issues, and predict disease onset with high accuracy. The study evaluates the efficacy of these smart contact lenses in detecting early signs of chronic conditions like diabetes and glaucoma, and their ability to enhance patient engagement through personalized health insights. Additionally, the paper discusses the technological challenges, including data accuracy, device comfort, and privacy concerns, and proposes solutions to address these issues. The integration of smart contact lenses with AI technology offers a novel approach to proactive health management, paving the way for more accessible and effective continuous monitoring and early disease detection
INVESTIGATION OF THE EFFECTIVENESS OF FIRE RISK EMERGENCY PREPAREDNESS IN HIGH INSTITUTIONS IN THE NIGER DELTA REGION OF NIGERIA
Full text linkThe study was carried out to investigate the effectiveness of fire risk emergency preparedness in high institutions in the Niger delta region of Nigeria. Three objectives and two hypotheses were raised to captivate the aim the of the study. The study adopted survey design, and systematic and purposive sampling technique was used to select eight high institutions from eight states in the Niger-Delta while Krejcie and Morgan, table was used to determine sample size of three hundred and twenty-three (382) student, Structured questionnaire, designed using 5-point Likert scale, was used for data collection. Descriptive statistics (percentage and weighted mean score WMS) and ANOVA were used for data analysis. The results of descriptive statistic showed that; the current and existing fire risk assessment methods in the high institution in the Niger-delta is below average (46.80%), the level of awareness and knowledge of students and other stakeholders on fire risk assessment methods among the high institutions in the Niger delta is substantially good (WMS= 3.79 > 3.00) and the level of effectiveness of existing fire risk assessment methods. in the high institution in the study area is substantially good (WMS= 3.91 > 3.00). The ANOVA results used to test the hypotheses revealed that; there is no significant variation in awareness and knowledge levels regarding fire risk assessment methods across the eight states (p-value 0.883 > 0.05 significance level) and there is no significant variation in level of effectiveness of the fire risk assessment methods across the high institutions in the eight states (p-value 0.238 > 0.05 significance level). It was concluded that the effectiveness of fire risk emergency preparedness in high institutions in the Niger delta region of Nigeria is good but required more improvement. It was recommended that the authorities and fire service department in the high institution should improve coordination in emergency response by addressing identified areas for enhancement, such as equipment readiness and communication system
NOVEL EARLY DIAB EDI DEVICE FOR PREDICTING TYPE 2 DIABETES
Full text linkThe rising prevalence of Type 2 diabetes mellitus (T2DM) poses significant public health challenges globally, necessitating early detection and intervention strategies to mitigate its impact. This study investigates the application of machine learning (ML) algorithms for the prediction of T2DM, utilizing a comprehensive dataset that includes demographic, clinical, and lifestyle factors. Various ML models, including logistic regression, decision trees, random forests, and support vector machines, were employed to identify key predictors and enhance the accuracy of diabetes prediction. The performance of these models was evaluated using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC). Our findings demonstrate that the random forest model outperformed other algorithms, achieving an accuracy of 88.5% and an AUC-ROC score of 0.92, indicating its robustness in predicting T2DM. The logistic regression model followed with an accuracy of 84%, while decision trees and support vector machines achieved accuracies of 81% and 79%, respectively. Additionally, feature importance analysis revealed that factors such as body mass index (BMI), age, and family history significantly influenced the risk of developing diabetes. The results underscore the potential of ML techniques as effective tools for early diabetes prediction, facilitating timely intervention and personalized treatment strategies. This study contributes to the growing body of literature advocating for the integration of machine learning in diabetes management and encourages future research to explore more complex models and larger datasets for improved predictive performance
TRAVEL INFLUENCERS AND INFLUENCER MARKETING IN TOURISM: THE CASE OF LIBYA
Full text linkInfluencer marketing has become an increasingly popular tool for promoting tourism, but the use of this strategy in Libya is not well understood and it is not widely implemented. The current study is meant to investigate the existing situation of influencer marketing in the tourism industry in Libya, a country that is currently facing political and economic challenges after the Arabic spring. A qualitative research design was used, with the utilization of online interviews as the primary source of data collection. The participants in the study were selected through a random sampling technique and included two travel influencers, two tourism professionals, and four travel agency managers. The interviews were conducted in the form of online video meetings which they were held through the google meet application and the data collected was scrutinized and then grouped accordingly using thematic analysis. The findings revealed that the current use of influencer marketing in promoting tourism in Libya is limited. Some participants reported that they have not yet implemented influencer marketing in their tourism promotions, while others have recently begun working with a few travel influencers on Instagram. The study also found that the main challenges faced by participants in implementing influencer marketing in their tourism promotions are identifying the right influencers and measuring the success of influencer marketing campaigns. Participants reported difficulties in finding influencers with a large following who are also knowledgeable about and passionate about Libyan tourism. This aligns with the literature which indicates that recognizing suitable influencers is one of the main challenges of influencer marketing
COAGULATION POTENTIALS OF THE LEAVES OF DESERT DATES, GUIERA, JUJUBE TREE, KAMEL’S FOOT AND MAHOGANY
Full text linkCoagulation activity in water and waste water treatment tends to reduce turbidity by agglomerating the dissolved and suspended particles thereby making them dense enough to settle. Alluminium phosphate is the common solution that is used to achieve coagulation. In case of lacking aluminum phosphate, an alternative has to be considered. In this study, leaves of five trees are considered. The trees include Jujube Tree (Prunus Domestica), Desert dates (Balanites Eagyptiaca), Kamel’s Foot (Piliostigma reticulatum; P. Thonningii), Guiera (Guiera Senegalensis) and Mahogany (Khaya Senegalensis). Leaves of these trees were dried and grinded to powder before they were dissolved in water. Extract of these solution were used for coagulation potentialities with a concentration of 2 ml per liter. Turbidity of the water was tested before coagulation and was found to be 233 NTU. The results of the turbidity after coagulation showed that the water has 53 NTU, 69 NTU, 70 NTU, 74 NTU and 700 NTU for Mahogany, Jujube, Guiera, Desert Dates and Kamel’s Foot respectively. Drinking water limit of 5NTU for turbidity in accordance to W.H.O has not been achieved in all the tests mad
FIRE FIGHTING ROBOTIC VEHICLES
Full text linkThe project is designed to develop a fire fighting robot using Arduino uno . The robotic vehicle is loaded with water pump which is controlled by servos. An ATMega 328 microcontroller is used for the desired operation. At the transmitting end using commands are sent to the receiver to control the movement of the robot either to move forward, and left or right etc. At the receiving end tow motors are interfaced to the microcontroller where two of them are used for the movement of the vehicle and the one to position the robot. The ultrasonic sensor adequate range with obstacle detection, while the receiver driver module used to drive DC motors via motor driver IC for necessary work. A water tank along with water pump is mounted on the robot body and its operation is carried out from the microcontroller output through appropriate command from the transmitting end. The whole operation is controlled by an ATmega 328 microcontroller. A motor driver IC is interfaced to the microcontroller through which the controller drives the motors,three ir flame sensors are fixed on robot chassis to sense the fire and to reach the destination to putoff the fire
RESEARCH ON THE SIGNIFICANCE OF READING COMPREHENSION TASKS IN MASTERING RUSSIAN LANGUAGE LESSONS IN HIGHER EDUCATION
Full text linkReading comprehension in an article is the ability to understand the meaning of the text being read, the ability to interpret it and form an attitude to what has been read. That this is the most important skill that students are taught in elementary school, literal understanding. The first level of comprehension is the repetition or re-creation of the text, highlighting basic information, identifying the main characters, etc. It is said to have the ability to identify the month, place, time, chronology, plot, movement
THE EVOLUTION AND REVOLUTION OF MARKETING MIX FROM 4P TO 4C TO 4E TO E-MARKETING MIX: A LITERATURE OVERVIEW
Full text linkMarketing mix is an important concept in the world of marketing that was first introduced by an academic named Jerome McCarthy in the 1960s. The marketing mix was created in the early days of the marketing concept when physical products, physical distribution and mass communication were dominant. The marketing mix paradigm, in its famous version of the 4 Ps, went all the way through the evolution of marketing theory being object of discussion both in academic literature as well as in managerial practice. In today’s era of revolutionary change, the digital business represents the more recent of the business contexts and the one with the greater need for a differentiation and upgradation of the mix. Throughout this evolutionary process, researchers have given their views who think the 4 Ps paradigms is able to adapt to the environmental changes by including new elements inside each “P”, and some affirmed that the 4 Ps paradigm is obsolete and propose new paradigms. This paper aims to clarify these two different approaches to marketing mix evolution through a review of the main literature regarding e-marketing mix and its constituents which provides an up gradation to the existing elements of marketing mix as well as focusing on the development of marketing mix theory for the digital context. . In the context of the wars between the upstart internet retailers and the existing brick and mortar retailers, this study would pave the way to consider E-marketing as an innovated way to customer delight. The evolution of \u27Product,\u27 \u27Price,\u27 \u27Place,\u27 and \u27Promotion\u27 in the digital era requires a holistic understanding of consumer behavior, technological advancements, and the interconnected nature of the online world
ADVANCES IN NATURAL LANGUAGE PROCESSING: A SURVEY OF TECHNIQUES
Full text linkNatural Language Processing (NLP) has witnessed remarkable advancements over the past few decades, transforming the way machines understand and interact with human language. This survey provides a comprehensive overview of the key techniques and methodologies that have propelled the field forward, highlighting both traditional approaches and contemporary innovations. We begin by discussing foundational NLP techniques such as tokenization, part-of-speech tagging, and syntactic parsing, which laid the groundwork for understanding language structure. The evolution of statistical methods, including Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs), is explored as a significant advancement in the probabilistic modeling of language. The survey then delves into the rise of machine learning approaches, particularly supervised and unsupervised learning, which have revolutionized various NLP tasks such as sentiment analysis, named entity recognition, and machine translation. We examine the impact of deep learning, focusing on architectures like Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Convolutional Neural Networks (CNNs) that have enabled significant improvements in performance across a range of applications. The introduction of transformer models, particularly the attention mechanism and BERT (Bidirectional Encoder Representations from Transformers), marks a paradigm shift in how contextual information is captured, leading to state-of-the-art results in numerous NLP benchmarks. In addition to technical advancements, the survey addresses the challenges that persist in NLP, including issues of bias in language models, the necessity for large annotated datasets, and the importance of explainability in AI systems. We discuss ongoing research efforts aimed at mitigating these challenges, including techniques for domain adaptation, few-shot learning, and unsupervised representation learning. This survey aims to provide researchers and practitioners with a clear understanding of the trajectory of NLP techniques, illustrating how traditional methods have evolved into sophisticated deep learning models. We conclude by highlighting future directions for research in NLP, emphasizing the need for interdisciplinary approaches that integrate linguistics, cognitive science, and ethical considerations to build more robust, fair, and interpretable NLP systems. Through this comprehensive survey, we seek to inspire further exploration and innovation in the field of Natural Language Processing, paving the way for applications that can better understand and generate human language in diverse contexts
A REVIEW PAPER ON AUTOMATED FIRE DETECTION
Full text linkAutomated fire detection and localization systems play a pivotal role in enhancing early response mechanisms and minimizing the devastating impact of fires. This review explores recent advancements in technologies and methodologies related to automated fire detection and localization. The study encompasses a comprehensive analysis of various sensing technologies, including infrared, image processing, and machine learning-based approaches. The effectiveness of these systems in diverse environments, such as industrial facilities, residential spaces, and outdoor areas, is evaluated. The review delves into the challenges faced by existing systems, addressing issues related to false alarms, scalability, and real-time response. Additionally, advancements in the integration of Internet of Things (IoT) and artificial intelligence (AI) techniques for more robust fire detection and localization are discussed. The aim is to provide a thorough understanding of the current state of automated fire detection systems, their limitations, and the potential avenues for future research and development in this critical field