International Journal of Innovations in Science & Technology
Not a member yet
813 research outputs found
Sort by
Energy-Based Cluster Head Selection in WSN
Get PDFWireless Sensor Networks (WSN) are the collection of sensor nodes, deployed in an ad hoc fashion and mostly powered by batteries. Therefore, efficient energy utilization has remained a vital parameter in designing and developing of WSNs to extend the network lifetime. In any network, routing protocols operate for selecting routes for the transfer of data packets from source to destination. Ad Hoc On-Demand Distance Vector (AODV) is a routing protocol used in various wireless ad hoc networks for transmitting data from source node to destination through intermediate motes. Hence, the efficient path selection mechanism can significantly improve energy utilization and elongate the lifetime of the network. This paper provides an investigation using the AODV routing protocol, based on the Cluster Head (CH) selection mechanism and shortest path selection between a source node, CH, and sink using multi-hop communication. The proposed scenarios significantly reduce energy consumption by selecting the shortest path between the source, cluster head, and sink. The Matlab simulation results show the comparison between AODV and Cluster head-based AODV (CH-AODV), indicating the CH-AODV consumes much less energy compared to normal AODV protocol
Deep Learning Based Multi Crop Disease Detection System
Get PDFThis research explores the integration of deep learning, computer vision, and edge computing to revolutionize crop disease detection. In response to the pressing need for prompt and accurate disease identification, this work leverages the capabilities of edge computing devices deployed within agricultural fields. Real-time data processing at the edge facilitates quick disease classification across various crops, enabling timely interventions. At the heart of the methodology lies a fine-tuned ResNet50 deep learning model, specifically chosen for its proficiency in handling complex visual data. Trained on a specialized dataset derived from the ImageNet database, the model exhibits promising accuracy rates in preliminary testing. Integrating edge computing into precision agriculture, this research presents a significant advancement toward sustainable agricultural practices. By empowering farmers with early detection and timely interventions, this endeavor equips agricultural communities with the knowledge and tools necessary to safeguard their crops, ensuring both food security and economic stability
A Computational Study of Ichthyofaunal Diversity of River Kabul
No full textMcClelland initiated the scientific study of the fish species of the River Kabul in 1842, and many researchers have continued this work since then. The primary goal of these studies has been to review the fully characterized fish fauna of the River Kabul and its major tributaries. The fish in the river are all members of the superclass Gnathostomata, including the Actinopterygii, subclass Neopterygii, division Teleostei, and superorder Ostaryophysi. Seventy-five fish species have been described from Pakistan and Afghanistan, belonging to four orders, ten families, and thirty-nine genera. Research indicates that Cypriniformes is the largest order and Cyprinidae is the largest family of fish in the River Kabul. Of the thirty-nine genera, twenty-seven are monospecific, and twelve are polyspecific. Notably, 27% of these fish are large and edible, highlighting the river\u27s significant economic potential for the region. It is concluded that the ichthyofauna of this river is diverse and holds great economic value for the local population. However, pollution from industrial zones and anthropogenic settlements poses a significant threat to the aquatic fauna. To preserve the fish and aquatic resources in this river, proper management, law enforcement, and public education are highly recommended
Classifying Text in Citation Context as Relevant or Irrelevant to the Cited Paper
Get PDFCitation contexts, whether in the form of full citing sentences or text within a fixed window around the citation, have been widely used in various citation analysis applications. However, the absence of precise techniques to identify the exact span of text describing citations forces these applications to rely on extended texts as citation contexts. In this paper, we introduced new features combined with baseline features to accurately identify text that characterizes citations. Specifically, we utilized a Conditional Random Field (CRF) sequence classifier to categorize the surrounding text of citations as relevant or irrelevant. The integration of these features enhances the precision, recall, and F-measure scores for the Relevant (R) class. Although the average values of all measures are similar to those obtained with baseline features alone. Our approach significantly improves the extraction of relevant text
Predictive Maintenance in Industrial Internet of Things: Current Status
Get PDFIntroduction/Importance of Study: Predictive Maintenance (PdM) is a key challenge within the Industrial Internet of Things (IIoT). It aims to enhance system operations by minimizing equipment failures, leading to smoother operations and increased productivity. By anticipating maintenance needs before failures occur, PdM ensures more reliable and efficient industrial processes. Novelty Statement: This study examines maintenance techniques and datasets that leverage AI and ML for predictive maintenance in the context of industrial IoT. The primary goal is to enhance productivity, identify faults before failures occur, and minimize downtime. By utilizing advanced algorithms, the study aims to improve the efficiency and reliability of industrial systems. Material and Method: A systematic literature review of state-of-the-art predictive maintenance in the context of industrial IoT, incorporating machine learning (ML) and artificial intelligence (AI) methods, is conducted. This review is based on research articles retrieved from the Dimensions.ai database, covering publications from 2018 to 2024.Result and Discussion: This comprehensive analysis offers valuable insights for advancing Predictive Maintenance (PdM) strategies in the Industrial Internet of Things (IIoT), ultimately contributing to more efficient manufacturing processes. The study highlights leading publication venues and top keywords in this research area, providing a clear picture of emerging trends. It also explores the prognosis of PdM within the manufacturing industry. Additionally, the review discusses relevant models, methods, input variables, and datasets in the PdM and IIoT domain, with a particular focus on machine learning (ML) and artificial intelligence (AI) techniques. Among the most widely used techniques for PdM in IIoT are deep learning, artificial neural networks, and random forest.Concluding Remarks: Subsequently, the study highlights various challenges, offering future research directions aimed at refining predictive maintenance techniques
Volatility Prediction in Cryptocurrency Using NFTs
Get PDFThe cryptocurrency market has evolved in unprecedented ways over the past decade. However, due to the high price volatility associated with cryptocurrencies, predicting their prices remains an attractive research topic. While many researchers have focused on predicting cryptocurrency prices, there has been relatively little attention given to the latest trend in blockchain applications, specifically non-fungible tokens (NFTs). In this study, we have prepared a dataset comprising NFT sales and transaction data, along with information from other cryptocurrencies. This dataset is utilized to forecast the future price of Bitcoin using several machines learning models, including Linear Regression, Random Forest, and XG Boost. The results highlight the prediction accuracy of these models. Among the three, the Random Forest regressor demonstrates the highest accuracy, followed closely by the XG Boost regressor and Linear Regression. These findings may assist investors in making informed decisions when investing in cryptocurrencies
Adapting Transfer Learning for Accurate ECG Based Heart Disease Classification
Get PDFECG signals are widely used for analyzing heart rhythms and detecting abnormalities. This study presents an experimental evaluation of a Deep CNN model for classifying ECG scalograms. Using publicly available datasets containing records from 242 patients, the study aims to classify three different cardiovascular diseases: Congestive Heart Failure (CHF), Myocardial Infarction (MI), and Coronary Artery Disease (CAD). The raw ECG signals undergo several preprocessing steps, including up-sampling, removal of noise and artifacts, and conversion into 2D images. Continuous Wavelet Transform (CWT) is applied to represent the ECG signals as 2D scalograms. The experiments in this work are conducted using a Deep CNN model and the pre-trained Inception V3 model, which achieved accuracies of 96.87% and 90.11%, respectively, on the CWT scalograms of the ECG datasets. The results were thoroughly analyzed, and the model’s performance was compared with other existing studies in the field
Human Factors and Risk Analysis in Conventional System of Marble Mining: Using HFACS Framework and Structural Equation Modeling Technique
Get PDFAccidents in mines can occur due to sources of hazards that lead to the loss of hundreds of precious lives every year. Among these sources, human error is considered one of the significant sources that contribute to human errors causing accidents. In this study, different risk factors were analyzed that contribute to human errors and subsequent accidents in the conventional marble mining system. Data was collected from marble mines workers through a questionnaire based on the Human Factors and Classification System framework. Structural equation modeling was applied to examine the interaction between contributory factors that trace back to mine accidents. Two structural models were developed, showing good fit for indices with chi-square to the degree of freedom values of 2.967 and 2.095, respectively, and root mean square error of approximation value below 0.08. The results indicate that the risks caused by individuals or systems have considerable effects on human performance and safety. The findings further explore that safety management at the managerial and supervisory levels is mostly associated with systematic risks, influencing safety policies, procedures, and oversight mechanisms. However, risk caused by lack of PPE, improper machinery, and lack of training has a direct effect on workers, leading to unsafe activities. These risk factors significantly contribute to the development of unsafe conditions that increase the probability and potential severity of accidents. For improving unsafe conditions, the implementation of mechanization can effectively decrease reliance on workers, thereby minimizing human errors and ultimately enhancing safety. The findings of this study will be helpful for the assessment the surface mines safety in a better way
A Investigation of Feminism Trends Through Sentiment Analysis Using Machine Learning and Natural Language Processing
No full textIntroduction/Importance of Study:
One of the recent changes seen in Pakistan is the growing awareness among people, to end gender discrimination and bring equality, across various spheres. “Aurat March”, is a series of rallies that began in 2018 to mark International Women’s Day. People across the nation, comment on these rallies through social media.
Novelty Statement:
The response to the “Aurat March”, held annually since 2018, was mixed and no analysis of Twitter data had been previously done to investigate the polarity of comments, through Machine learning and Natural Language Large Language Models.
Material and Method:
For this, Sentiment analysis was performed, using Machine learning and NLP techniques, on the pre-processed data. Lexical rule-based VADER and transformer-based pre-trained Large Language Models were used to check the polarity of Twitter comments.
Results and Discussion:
The best results were achieved through RoBERTa-LARGE, which were closest to the Human Labelled Data, hence validating the accuracy of the LLM. On the other hand, VADER results were clearly far from the manually labeled results. The sentiment analysis that was applied the first time on “Aurat March” tweets, gave us satisfactory results, and we were further able to validate our research, by comparing the models’ accuracy with human-labeled results. Consequently, by analyzing the sentiments expressed on Twitter, we were able to discern the general mindset of users and gain insights into prevailing trends.
Conclusing Remarks:
This analysis provided us with a reasonably accurate gauge to assess the perception of feminism over the past few years, allowing us to evaluate whether it has garnered fame or faced defamation in the public discourse.
Digital Twins and Engineering Education: Current Status
Get PDFThis paper presents a comprehensive review of the use of Digital twins in engineering education among various disciplines. A total of 83 research papers were analyzed, spanning the last decade from 2012 to 2022. Almost all publications were reported after the year 2018, indicating a recent surge in interest and development in this area. The review reveals that digital twin technology offers students an interactive experience with virtual models of real-world products and systems, significantly enhancing the effectiveness of engineering education. It also improves industrial competitiveness through predictive maintenance and fault diagnosis. Digital twins can be used in various engineering disciplines and for personalized learning. However, challenges such as model accuracy and data transfer must be considered when implementing them. Overall, this technology can improve student learning outcomes, increase education accessibility and cost-effectiveness, and improve production systems\u27 safety, visibility, and accessibility. Future requirements of the field are also discussed in this paper