bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and Implementation of Auto Switch Lighting Monitoring and Control System
Authors: Maitanmi Stephen O. , Sunday Idowu A. , Yaw Mensah A. , Samuel Okafor , Victor Illuyomade , Simon Sharon 
Published: September 2023

The havoc that the electricity can cause may be unimaginable compared with the benefits if not proper controlled, maintained, managed and used. Developed countries has setup good system of management and maintenance of electricity supply. Studies have shown that electricity supply should be properly distributed and managed with respect to the use, however, only a few of these studies were implemented in developing countries including Nigeria. Therefore, this research was aimed at the design and development of auto switch lighting monitoring and control system. The research adopted multiple concepts by making use of the development of software, and hardware configurations. The configurations combined a micro-controller (Arduino), sensor GSM module for wireless communication, battery, Real Time Clock (RTC) and LEDs (Light Emitting Diodes) for display purpose. By considering the time set by Real Time Clock (RTC) and from the status of (Light Dependent Resistor) LDR sensor, the controller turns the light ON/OFF. The results showed that the photoelectric (presence) sensor turns on the light only when the presence is detected during night time and Current Sensor will sense the current value from which power consumed is calculated. It also provides the information about the faulty lamps which ease the fault detection. The research concluded by ensuring that the objectives were met. The research employed the synchronization of database created and linked with a mobile software that was further linked with a digital board. The designed software was implementation and it was able to control the auto switching, monitoring and control of the lighting system in Babcock University. It was recommended that this software should be replicated for the country.
Exploring the Business-Culture Relationship with Box-Jenkins ARIMA Analysis for Forecasting the Path and Future Prospects of the Popular Music Industry
Authors: Jin Xi , Kim Hyuntai 
Published: September 2023

Time series analysis plays a crucial role in understanding and predicting the path and future prospects of industries, including the popular music industry. This paper constructed an Box-Jenkins ARIMA (BJ-ARIMA) methodology to analyze the time series data in the popular music industry, with a focus on the relationship between business and culture. By employing the Box-Jenkins approach, BJ-ARIMA forecast future trends and make informed predictions about the development of the industry. Identification, estimation, and diagnostic testing using the BJ-ARIMA framework are the three main components of the Box-Jenkins approach. Autocorrelation and partial autocorrelation plots are analyzed in the identification phase to help choose the best BJ-ARIMA model. The estimation phase involves fitting the selected BJ-ARIMA model to the historical data, using techniques such as maximum likelihood estimation. Finally, BJ-ARIMA diagnostic checking is performed to ensure the model's adequacy and reliability. The findings of BJ-ARIMA analysis will provide a solid foundation for forecasting trends and making informed decisions in the dynamic and evolving world of the popular music industry.
Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)
Authors: Srinivas C. , Venkatramulu S. , Chandra Shekar Rao V. , Raghuram B. , Vinay Kumar K . , Pratapagiri Sreenivas 
Published: September 2023

Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity. Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput.
Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review
Authors: Dinesh P , Lakshmanan Ramanathan 
Published: September 2023

In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology. By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic.
Exploiting Emotions via Composite Pretrained Embedding and Ensemble Language Model
Authors: Jawale Shila S. , Sawarkar S. D. 
Published: September 2023

Decisions in the modern era are based on more than just the available data; they also incorporate feedback from online sources. Processing reviews known as Sentiment analysis (SA) or Emotion analysis. Understanding the user's perspective and routines is crucial now-a-days for multiple reasons. It is used by both businesses and governments to make strategic decisions. Various architectural and vector embedding strategies have been developed for SA processing. Accurate representation of text is crucial for automatic SA. Due to the large number of languages spoken and written, polysemy and syntactic or semantic issues were common. To get around these problems, we developed effective composite embedding (ECE), a method that combines the advantages of vector embedding techniques that are either context-independent (like glove & fasttext) or context-aware (like XLNet) to effectively represent the features needed for processing. To improve the performace towards emotion or sentiment we proposed stacked ensemble model of deep lanugae models.ECE with Ensembled model is evaluated on balanced dataset to prove that it is a reliable embedding technique and a generalised model for SA.In order to evaluate ECE, cutting-edge ML and Deep net language models are deployed and comapared. The model is evaluated using benchmark datset such as MR, Kindle along with realtime tweet dataset of user complaints . LIME is used to verify the model's predictions and to provide statistical results for sentence.The model with ECE embedding provides state-of-art results with real time dataset as well.
Secure Face and Liveness Detection with Criminal Identification for Security Systems
Authors: Shinde Pratibha , Raundale Ajay 
Published: September 2023

The advancement of computer vision, machine learning, and image processing techniques has opened new avenues for enhancing security systems. In this research work focuses on developing a robust and secure framework for face and liveness detection with criminal identification, specifically designed for security systems. Machine learning algorithms and image processing techniques are employed for accurate face detection and liveness verification. Advanced facial recognition methods are utilized for criminal identification. The framework incorporates ML technology to ensure data integrity and identification techniques for security system. Experimental evaluations demonstrate the system's effectiveness in detecting faces, verifying liveness, and identifying potential criminals. The proposed framework has the potential to enhance security systems, providing reliable and secure face and liveness detection for improved safety and security.
The accuracy of the algorithm is 94.30 percent. The accuracy of the model is satisfactory even after the results are acquired by combining our rules inwritten by humans with conventional machine learning classification algorithms. Still, there is scope for improving and accurately classifying the attack precisely.
Attribute Selection Algorithm with Clustering based Optimization Approach based on Mean and Similarity Distance
Authors: Kaseebhotla Rajasekhar , Rao K. Raghava , Rao Mallikarjuna 
Published: September 2023

With hundreds or thousands of attributes in high-dimensional data, the computational workload is challenging. Attributes that have no meaningful influence on class predictions throughout the classification process increase the computing load. This article's goal is to use attribute selection to reduce the size of high-dimensional data, which will lessen the computational load. Considering selected attribute subsets that cover all attributes. As a result, there are two stages to the process: filtering out superfluous information and settling on a single attribute to stand in for a group of similar but otherwise meaningless characteristics. Numerous studies on attribute selection, including backward and forward selection, have been undertaken. This experiment and the accuracy of the categorization result recommend a k-means based PSO clustering-based attribute selection. It is likely that related attributes are present in the same cluster while irrelevant attributes are not identified in any clusters. Datasets for Credit Approval, Ionosphere, Annealing, Madelon, Isolet, and Multiple Attributes are employed alongside two other high-dimensional datasets. Both databases include the class label for each data point. Our test demonstrates that attribute selection using k-means clustering may be done to offer a subset of characteristics and that doing so produces classification outcomes that are more accurate than 80%.
A Novel Densenet-324 Densely Connected Convolution Neural Network for Medical Crop Classification using Remote Sensing Hyperspectral Satellite Images
Authors: Thamaraikannan N. , Manju S. 
Published: September 2023

In the past few decades, importance of the medicinal Crops is extending to a large extent due to its benefits in treating life-threatening diseases. Medicinal Crop has excellent medicinal properties on its roots, stem, and leaves to prevent human and animal health. Particularly detection and identification of the Crop classes are effectively carried out using hyperspectral images as discrimination of the target feature or objects is simple and it contains rich information containing the spatial and temporal details of underlying the land cover. However, Crop classification using machine learning architectures concerning spectral characteristics obtained on the anatomical features and morphological features. Extracted features towards classification lead to several challenges such as large spatial and temporal variability and spectral signatures similarity between different objects. A further hyperspectral image poses several difficulties with changes in illumination, environment, and atmospheric aspects. To tackle those non-trivial challenges, DenseNet-324 Densely Connected convolution neural network architecture has been designed in this work to discriminate the crop and medical Crop effectively in the interested areas. Initially, the Hyperspectral image is pre-processed against a large number of noises through the employment of the noise removal technique and bad line replacement techniques. Pre-processed image is explored to image segmentation using the global thresholding method to segment it into various regions based on spatial pieces of information on grouping the neighboring similar pixels intensity or textures. Further regions of the image are processed using principle component analysis to extract spectral features of the image. That extracted feature is employed to ant colony optimization technique to obtain the optimal features. Computed optimal features are classified using Convolution Neural Network with a hyper parameter setup. The convolution Layer of the CNN architecture process spatial, temporal, and spectral feature and generates the feature map in various context, generated feature map is max pooled in the pooling layer and classified into crops and medicinal Crop in the SoftMax layer. Experimental analysis of the proposed architecture is carried out on the Indiana Pines dataset using cross-fold validation to analyze the representation ability to discriminate the features with large variance between the different classes. From the results, it is confirmed that the proposed architecture exhibits higher performance in classification accuracy of 98.43% in classifying the Crop species compared with conventional approaches.
Enhancement of Performance Metrics of Heterogeneous Wireless Camera Sensor Network with Functionalization of Extensive Zonal Stable Election Protocol using Threshold Amplification and Residual Energy
Authors: Tripathi Nishant , Singh Charanjeet , Sharma Kamal Kumar 
Published: September 2023

We report a new improved energy competent and optimized data packet flow protocol with Hierarchical Clustering utilized in Wireless camera Sensor Network.The existing Extensive Zonal Stable Election Protocol has been modified along with the threshold parameters amplification and residual energy. It incorporates dynamic hybrid method with finite number of Member Sensor nodes (MSN) in proximity with the base station share their data directly , while the rest of the farther nodes form a cluster for data transference using Cluster Head. The performance metrics accompanied by heterogeneity, longer network survival and better throughput have been improved. The network field was divided into 4 zones with a gateway for defined region 2, 3, and 4. The criterion for zone division remained on the energy status (residual) of the MSNs and distance from the BS and the formulated field characteristics in the simulation were kept unknown. The obtained results demonstrate that our proposed modified version of EZSE protocol considerably performs better than existing EZ-SEP, Z-SEP, SEP, LEACH, Mod-Leach protocols during entire stability timeframe. The notable achievement is also reported in throughput as the same is enhanced more than by ~ 39%, 43%, 49% ,56%, 53% while total packets communicated with base station has been increased more than by ~ 127%, 131%, 147%, 151%, 148% stability of the network is also improved more than by ~ 37%, 42%, 45%, 49%, 51% with the corresponding increase in the heterogeneity of networks.
Uncovering Semantic Inconsistencies and Deceptive Language in False News Using Deep Learning and NLP Techniques for Effective Management
Authors: Chopra Yash , Kaushik Priyanka , Rathore Saurabh Pratap Singh , Kaur Pramneet 
Published: September 2023

In today's information age, false news and deceptive language have become pervasive, leading to significant challenges for individuals, organizations, and society as a whole. This study focuses on the application of deep learning and natural language processing (NLP) techniques to uncover semantic inconsistencies and deceptive language in false news, with the aim of facilitating effective management strategies.
The research employs advanced deep learning models and NLP algorithms to analyze large volumes of textual data and identify patterns indicative of deceptive language and semantic inconsistencies. By leveraging the power of machine learning, the study aims to enhance the detection and classification of false news articles, enabling proactive management measures. The proposed approach not only examines the superficial aspects of false news but also delves deeper into the linguistic nuances and contextual inconsistencies that are characteristic of deceptive language. By employing advanced NLP techniques, such as sentiment analysis, topic modeling, and named entity recognition, the study strives to identify the underlying manipulative strategies employed by false news purveyors.
The findings from this research have far-reaching implications for effective management. By accurately detecting semantic inconsistencies and deceptive language in false news, organizations can develop targeted strategies to mitigate the spread and impact of misinformation. Additionally, individuals can make informed decisions, enhancing their ability to critically evaluate news sources and protect themselves from falling victim to deceptive practices.
In this research study, we suggest a hybrid system for detecting fake news that incorporates source analysis and machine learning techniques. Our system analyzes the language used in news articles to identify indicators of fake news and evaluates the credibility of the sources cited in the articles. We trained our system using a large dataset of news articles manually annotated as real or fake and evaluated its performance measured by common metrics like F1-score, recall, and precision. In comparison to other advanced fake news detection systems, our results show that our hybrid method has a high level of accuracy in detecting false news.
Design of Multiplexers using Reversible Logic Technique
Authors: Sooriamala A. P. , Solomi V. Sherlin , Korah Reeba , Thomas Aby K. 
Published: September 2023

Reversible logic is promising as it can be applied to different applications in low power like nanocomputing especially in quantum computing. Reversible logic is a technioue for power reduction. Reversible circuits are similar to digital circuits but they work using reversible logic gates. This study focuses on reducing the garbage output and ancilla inputs in reversible multiplexers, thereby reducing the power consumption. In this study two designs of Multiplexers are given. Design1 is using TwinSJ gate and AJ gate. 2:1, 4:1 and 8:1 multiplexers are built. In Design 2, a new gate (SJ gate) is built which functions as 2:1 multiplexer. It has 4*4 configuration. The inputs are suitably configured so that it performs various logic functions. Using this SJ gate and other basic reversible logic gates, 2:1, 4:1 and 8:1 multiplexers are built. In 2:1 multiplexer, Ancilla inputs are improved to '0' from 5 and garbage output has been reduced to 2 against 7 in existing design. 4:1 multiplexers are built with ‘0’ ancilla inputs against 2 and 11 in existing designs. Garbage output of the proposed 4:1 multiplexer is 5 against 6 and 16 in existing designs. 8:1 multiplexer is built with 1 ancilla input and 11 garbage output against 2 and 12 respectively in existing design. This is designed using VHDL code - xilinx 14.7 for verification purpose and simulated on ISIM.
Exploratory Data Analysis for Textile Defect Detection
Authors: Thivakaran T. K. , Priyanka N. , Antony J. Cruz , Surendran S. , Mohan E. , Innisai Rani P. Jona 
Published: September 2023

The capacity to recognize anomalies in real-world visual data is essential for many computer vision uses. New approaches and ideas in unsupervised defective garments identification require data for training and evaluation. Understanding the constraints of the currently employed approach of human inspection is crucial for improving clothing quality. Uses for digital image processing in the textile sector are suggested. This method proposes a novel quantitative measuring strategy by fusing digital image processing with the Lab view platform. As this study progresses, it becomes clear that the FLDA yields the best results, with 95% accuracy, while the Hoeffiding Tree yields the lowest results, with 60% accuracy. When compared to other models, the FLDA's precision of 0.96 is the best you'll find, while the Hoeffiding Tree's is the lowest at 0.62. The FLDA provides the best result, with a recall value of 0.95, while the Hoeffiding Tree shows the lowest result, with a recall value of 0.60. The FLDA yields the best results (0.90 kappa value), whereas the Hoeffiding Tree yields the worst (0.20 kappa value).The FLDA exhibits the best results, with an F-Measure value of 0.95, while the Hoeffiding Tree displays the lowest results, with an F-Measure value of 0.58. The FLDA provides the best results, with an MCC value of 0.91, while the Hoeffiding Tree displays the worst results, with an MCC value of 0.22. The FLDA yields the best results (0.98 ROC value), whereas the Decision Table produces the worst results (0.69 ROC value). The best prediction accuracy is shown by the FLDA, at 0.98 of the PRC value, while the worst is shown by the Decision Table, at 0.67. The MAE is lowest (0.07) for the FLDA and highest (0.39) for the Hoeffiding Tree. The MAE deviation of the Bayes Net is 0.19. The best result is shown by the FLDA, with an RMSE of 0.22, while the largest RMSE deviation is found in the Hoeffiding Tree, at 0.62. The RMSEdeviation for Bayes Net is 0.41. The finest RAE is shown by the FLDA, at 13.39%, while the largest RAE deviation is 78.28% for the Hoeffiding Tree. The Bayes Net explains 38.74% of the variation in RAE. The best result is shown by the FLDA, with an RRSE of 44.36%; the largest RRSE variation is shown by the Hoeffiding Tree, with 123.99%. When compared to other models, the IBK's preparation time of 0 seconds is by far the shortest. While the Bayes Net completes its task in 0.03 seconds, FLDA can take up to 0.17 seconds. The FLDA model is found to have superior performance in this study.