bluuebunny/crossref_metadata_2025_split huggingface dataset:
Análisis de la gestión de los residuos sólidos de tipo orgánico en las empresas de Colombia basado en la información de la Encuesta Ambiental Industrial EAI -2019
Authors: Torres Velásquez July Adriana , Pérez Pulido Miguel Oswaldo 
Published: December 2023

Introducción: El presente estudio da a conocer la problemática de la gestión de residuos por actividades de las empresas colombianas y la disposición dada empleando la información obtenida de la encuesta ambiental industrial EAI -2019 disponible por medio del Departamento Administrativo Nacional de Estadística (DANE). Materiales y Métodos: Se enfoca en la cantidad de residuos de tipo orgánico y el destino dado. Para ello se correlacionan las variables: cantidad de residuos orgánicos y cantidad de empresas que los aprovechan para su reutilización según el tipo de actividad principal. Resultados y Discusión: Se evidenció que sólo el 65% de las empresas miden los residuos generados. Cuanto mayor sea la cantidad de residuos orgánicos generados mayor es la cantidad de empresas que los aprovechan para reutilización, reciclaje u otra modalidad. La acción que predomina es entregar los residuos a un tercero que se encargue de la destinación final con 56,9% de las empresas, cifra que supera el 40,3% obtenido de la suma de vender, donar o aprovechar los residuos orgánicos. Conclusiones: Se evidencia un riesgo para la medición del cumplimiento de las metas de economía circular y para los planes de desarrollo municipales teniendo en cuenta que alrededor del 35% de las empresas no realizan la medición de los residuos. Se requiere de compromiso de las empresas para mejorar el volumen de aprovechamiento de los residuos sólidos, incluso estos pueden generar recursos adicionales de tipo económico o energético de acuerdo con el destino dado. Un buen uso de los residuos orgánicos contribuirá en la gestión de la economía circular para dar apoyo al cuidado del medio ambiente.
Contenido de cadmio y caracterización fisicoquímica de almendras y pasta de cacao (Theobroma cacao)
Authors: Revilla Escobar Karol , Intriago Flor Frank , Chávez García Gema , Vásquez Cortez Luis , Alvarado Vásquez Kerly , Vera Chang Jaime , Radice Matteo , Maddela Naga Raju 
Published: December 2023

Introducción: los metales pesados ​​como el cadmio (Cd) son una preocupación mundial; investigaciones recientes han concluido que están relacionados con riesgos para la salud causados ​​por la ingesta de alimentos contaminados. Objetivo: la presente investigación tuvo como objetivo evaluar el contenido de cadmio, características físicoquímicas en almendras y pasta de cacao. Materiales y Métodos: para el cual se utilizó un Diseño Completamente al Azar (DCA) con tres repeticiones, para determinar diferencia significativa entre la media de los tratamientos se empleó la prueba estadística de post hoc Duncan (p<0,05). Resultados: para el contenido de cadmio en almendras de cacao se determinó valores entre 0,80±0,0 y 1,08±0,01 mg kg-1 de Cd+2 (B200088 y B200090 consecutivamente); mientras que, el contenido de Cd+2 en pasta de cacao no cambio significativamente durante su procesamiento, situando valores similares a los obtenidos en la materia prima. Por otro lado, en los parámetros fisicoquímicos de las almendras y pasta de cacao se obtuvo: humedad (7,03 ± 0,02 - 7,45±0,01) y (1,25±0,01 - 1,70±0,04); ceniza (3,67±0,02 - 4,62±0,03) y (3,13±0,02 - 3,68±0,02); acidez titulable (0,97±0,02 - 1,29±0,02) y (0,91±0,01 - 1,23±0,02); contenido de proteína en las almendras (11,21±0,02 - 14,19±0,02) y un porcentaje de grasa en pasta de cacao (46,19±0,02 - 54,16±0,04). Conclusión: las muestras evaluadas cumplen con los requisitos establecidos por las normativas nacionales e internacionales.
A NEEDS ANALYSIS OF TECHNOLOGY-INTEGRATED BASIC ENGLISH GRAMMAR COURSE IN THE INDONESIAN CONTEXT
Authors: Suharti Dwi Sloria , Musthafa Bachrudin , Yusuf Fazri Nur 
Published: December 2023

Needs analysis is essential for developing a curriculum and teaching materials. Integration of technology in courses provides a supportive environment for learners across a wide range of courses. However, the needs analysis on the technology-integrated Basic English Grammar course learning is insufficient. This report aims to determine students' needs for studying Basic English Grammar courses through technology integration. For this purpose, the researchers used a descriptive qualitative design by distributing questionnaires. Twenty-four students completed the questionnaire, and the researchers recruited several students for interviews. The results of this study reveal students' expectations for technology-integrated assignments, such as submitting writing caption chores to Instagram, with a 48 per cent success rate. The technology-integrated Basic English Grammar course learning presences depict the most commonly used technologies such as YouTube, Instagram, the Quiz app, Google Classroom, Webbing, and e-Books. The researchers identified the lack of internet connection and an overabundance of information that perplexes students—this suggests the necessity to provide training to cope with the technological challenges of learning Basic English Grammar. The study proposes a teaching-learning situation in the Basic English Grammar course with technology integration as learning activities materials in the syllabus so that the teachers as the curriculum makers can develop the curriculum according to the needs of students. This report highlights that lecturers and universities perform periodic assessments of their students' needs as a basis for developing a curriculum with these reflective needs. In addition, the pedagogical implications for technology-integrated tools in the Basic English Grammar course are discussed.
Deception/Truthful Prediction Based on Facial Feature and Machine Learning Analysis
Authors: Twisha Patel Et al. 
Published: December 2023

The Automatic Deception detection refers to the investigative practices used to determine whether person is telling you Truth or lie. Automatic deception detection has been studied extensively as it can be useful in many real-life scenarios in health, justice, and security systems. Many psychological studies have been reported for deception detection. Polygraph testing is a current trending technique to detect deception, but it requires human intervention and training. In recent times, many machine learning based approaches have been applied to detect deceptions. Various modalities like Thermal Imaging, Brain Activity Mapping, Acoustic analysis, eye tracking. Facial Micro expression processing and linguistic analyses are used to detect deception. Machine learning techniques based on facial feature analysis look like a promising path for automatic deception detection. It also works without human intervention. So, it may give better results because it does not affect race or ethnicity. Moreover, one can do covert operation to find deceit using facial video recording. Covert Operation may capture the real personality of deceptive persons. By making combination of various facial features like Facial Emotion, Facial Micro Expressions and Eye blink rate, pupil size, Facial Action Units we can get better accuracy in Deception Detection.
Mitigating Hotspot Problem Using Chaotic Salp Swarm Algorithm for Energy Efficient IoT Assisted Wireless Sensor Networks
Authors: A. Gayathri Et al. 
Published: December 2023

Wireless Sensor Networks (WSN) and Internet of Things (IoT) continued to be pro-active study due to their far reaching applications and also a crucial technology for ubiquitous living. In WSN, energy awareness becomes a significant design problem. Clustering can be defined as a renowned energy-efficient method and renders a lot of benefits like energy competence, less delay, scalability, and lifetime; but it resulted in hot spot problems. To sort out this problem a method called unequal clustering is designed. In unequal clustering, the cluster size differs with the Base Station (BS) distance. In this study, a new Chaotic Salp Swarm Algorithm Based Unequal Clustering Approach (CSSA-UCA) methodology to resolve hot spot issues in IoT-assisted WSN is proposed. The major objective of the CSSA-UCA methodology lies in the effectual identification of CHs and unequal cluster sizes. To accomplish this, the CSSA-UCA technique initially derives the CSSA by the incorporation of chaotic notions into the conventional SSA. At the same time, a fitness function incorporating multiple input parameters was considered for unequal cluster construction. A wide range of experimental result analyses is performed to exhibit the supremacy of the CSSA-UCA technique. The experimental results stated that the CSSA-UCA technique proficiently balances energy accretion and improves the network lifetime.
Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework
Authors: Gaurav Goel Et al. 
Published: December 2023

Cloud-Fog environment is useful in offering optimized services to customers in their daily routine tasks. With the exponential usage of IoT devices, a huge scale of data is generated. Different service providers use optimization scheduling approaches to optimally allocate the scarce resources in the Fog computing environment to meet job deadlines. This study introduces the Whale-EarthWorm Optimization method (WEOA), a powerful hybrid optimization method for improving resource management in the Cloud-Fog environment. Striking a balance between exploration and exploitation of these approaches is difficult, if only Earthworm or Whale optimization methods are used. Earthworm technique can result in inefficiency due to its investigations and additional overhead, whereas Whale algorithm, may leave scope for improvement in finding the optimal solutions using its exploitation. This research introduces an efficient task allocation method as a novel load balancer. It leverages an enhanced exploration phase inspired by the Earthworm algorithm and an improved exploitation phase inspired by the Whale algorithm to manage the optimization process. It shows a notable performance enhancement, with a 6% reduction in response time, a 2% decrease in cost, and a 2% improvement in makespan over EEOA. Furthermore, when compared to other approaches like h-DEWOA, CSDEO, CSPSO, and BLEMO, the proposed method achieves remarkable results, with response time reductions of up to 82%, cost reductions of up to 75%, and makespan improvements of up to 80%.
Comparative Analysis Association and Prediction of Various Phenotypic Traits of Oryza Sativa
Authors: B. Kiranmai Et al. 
Published: December 2023

Understanding the genotype-phenotype relationship and accurately predicting breeding values are crucial aspects of crop improvement programs. This paper investigates the genetic basis ,association of phenotypic trait height and yield and predicts the phenotypic traits of Oryza Sativa (rice) through a comprehensive approach encompassing genome-wide association studies (GWAS), phylogenetic analysis, machine learning algorithms, and the development of a graphical user interface (GUI) application. Genotypic and phenotypic data were collected from the RiceVarMap database. The genotypic information consisted of gene variation IDs, while the phenotype data included plant height. Data preprocessing involved the creation of a sequence. fasta file and multiple sequence alignment using the ClustalW tool. A phylogenetic tree was then constructed to analyse the subpopulations of Oryza Sativa. Clustering techniques were applied to further explore the genetic relationships among the samples. A GWAS file was generated to identify associations between genotype and phenotype. Subsequently, machine learning algorithms were employed for the classification and prediction of genomic estimated breeding values (GEBV) for height and yield traits. Random Forest emerged as the most accurate algorithm with 85% accuracy. To facilitate user interaction and data exploration, a GUI application was developed using Flask, allowing users to access the phylogenetic tree, height, and yield information, GWAS results, and make predictions. We explored there is a strong positive association between phenotypic trait height and yield.
Bidirectional Encoder Representations Transformers for Improving CNN-LSTM Covid-19 Disease Detection Classifier
Authors: Jinu Paulson Siluvai Rathinam Et al. 
Published: December 2023

Early identification of COVID-19 diseased persons is crucial to avoid and prevent the transmission of the SARS-CoV-2 virus. To achieve this, lung Computed Tomography (CT) scan segmentation and categorization models have been broadly developed for COVID-19 diagnosis. Amongst, Multi-Scale function learning with an Attention-based UNet and Marginal Space Deep Ambiguity-attentive Transfer Learning (MS-AUNet-MSDATL) framework is developed to concurrently segment the COVID-19 infected regions and classify their risk levels from the CT/Chest X-Ray (CXR) scans. This model utilizes Convolutional Neural Network–Long Short Term Memory (CNN-LSTM) as a classifier for proper recognition. Although CNN-LSTM efficiently learns the spatial and temporal data, but it highly ignores the pixels and their adjacent information which results in lower classification rate. So, in this paper, Bidirectional Encoder Representations from Transformers (BERT) is introduced along with CNN-LSTM in MS-AUNet-MSDATL model to resolve the above mentioned issues for efficient COVID-19 risk level classification. Initially, the segmented CT and CXR images from MS-AUNet is given as input to the BERT model. BERT structure consist of stack of transformer encoder layers which extracts fixed features from a pre-trained models to obtain the numerical representation of the given image. Then, the numerical expressions from the BERT are transformed to pre-learned CNN model for selecting the important features from the given representations. The LSTM model receives the CNN output and generates a new representation based on the data order. In addition, Fully Connected Layer (FCL) maps the CNN-LSTM results into categorization classes to learn pixels and their adjacent information for COVID-19 risk level detection and diagnosis. The complete work is termed as MS-AUNet-EMSDATL. Finally, the test findings shows that the MS-AUNet-MS-B-DATL achieves accuracy of 98.67% and 98.55% on CT and CXR images compared to the other existing frameworks.
Software-Defined Networking-Based Campus Networks Via Deep Reinforcement Learning Algorithms: The Case of University of Technology
Authors: Ali Sadiq Salim Et al. 
Published: December 2023

As a consequence of the COVID-19 pandemic, networks need to be adopted to satisfy the new situation. People have been introduced to new modes of working from home, attending teleconferences, and taking part in e-learning. Other technologies, including smart cities, the Internet of Things, and simulation tools, have also seen a rise in demand. In the new situation, the network most affected is the campus network. Fortunately, a powerful and flexible network model called the software-defined network (SDN) is currently being standardized. SDN can significantly improve the performance of campus networks. Consequently, many scholars and experts have focused on enhancing campus networks via SDN technology.
Integrating deep reinforcement learning (DRL) with SDN is pivotal for advancing the quality of service (QoS) of contemporary networks. Their integration enables real-time collaboration, intelligent decision making, and optimized traffic flow and resource allocation.
The system proposed in this research is a DRL algorithm applied to a campus network—the University of Technology—and investigated as a case study. The proposed system employs a two-method approach for optimizing the QoS of a network. First, the system classifies service types and directs TCP traffic by using a deep Q-network (DQN) for intelligent routing; then, UDP traffic is managed using the Dijkstra algorithm for shortest-path selection. This hybrid model leverages the strengths of machine learning and classical algorithms to ensure efficient resource allocation and high-quality data transmission. The system combines the adaptability of DQN with the proven reliability of the Dijkstra algorithm to enhance dynamically the network performance.
The proposed hybrid system, which used DQN for TCP traffic and the Dijkstra algorithm for UDP traffic, was benchmarked against two other algorithms. The first algorithm was an advanced version of the Dijkstra algorithm that was designed specifically for this study. The second algorithm involved a Q-learning (QL)-based approach. The evaluation metrics included throughput and latency. Tests were conducted under various topologies and load conditions.
The research findings revealed a clear advantage of the hybrid system in complex network topologies under heavy-load conditions. The throughput of the proposed system was 30% higher than the advanced Dijkstra and QL algorithms. The latency benefits were pronounced, with a 50% improvement over the competing algorithms.
Trust Based Mechanism for Isolation of Malicious Nodes in Internet of Things
Authors: Zakiya Manzoor Khan Et al. 
Published: December 2023

The Internet of Things systems are prone to the attacks as they have ad-hoc and limited resource structure. IoT-based systems are utilized for managing a large volume of information and assist in services related to industrial and medical applications. Due to this, the IoT attains becomes a target for a multitude of attackers and adversaries namely occasional hackers, cybercriminals, hacktivists, government, etc. The major goal of potential attackers is to steal the sensitive information such as credit card numbers, location data, credential of financial account and information related to health, by hacking the Internet of Things devices. The version number attack is one of malicious activity of IoT which affect network performance to great extend. The version number attack is triggered by the malicious nodes which can flood unlimited hello packets in the network. The hello flood attack raised situation of denial of service in the network. The trust based mechanism is proposed in this research work in which trust value is assigned to each node based on their activities. The node which is least trusted will be marked as malicious and get isolated from the network. The proposed scheme is implemented in NS2 and results are analyzed in terms of throughput, packetloss, energy consumption and delay.
Design of SRAM Cell using Modified Lector and Dual Threshold Method Based on FINFET
Authors: Ramesh Gullapally Et al. 
Published: December 2023

FinFET (Fin Field Effect Transistor) is a new technology that satisfies the demand for a superior storage system by improving transistor circuit design (SS). CMOS devices experience a wide range of issues due to the gate's diminishing ability to control the channel. Increased total production costs are a few of these disadvantages. But this store needs to dissipate less power, have a quick access time, and a low leakage current. The increased power dissipation and leakage current of traditional CMOS-based SRAM (Static RAM) architectures cause a sharp decline in performance. The nanoscale gadget called FinFET is being introduced for use in SRAM fabrication due to its 3D gate architecture. The adoption of FinFET has helped boost overall performance in terms of efficiency, power, and footprint. And because it is immune to SCEs, FinFET has become the transistor of choice. In this study, we have examined a number of FinFET-based SRAM cells and compared them with CMOS technology. We have also suggested a novel 14T SRAM design that uses the Dual Threshold Method and Modified Lector Approach with FinFET, and it is implemented for the 1bit, 4bit, and 8bit.
Edge AI for Real-Time Video Analytics in Surveillance Systems
Authors: K P N V Satyasree Et al. 
Published: December 2023

More and more surveillance systems are being used to increase security and safety for the general public. However, the conventional method of processing all video data in the cloud can be ineffective and slow response times. By performing video analytics at the network's edge, close to where the data is created, Edge AI is a promising new strategy that can address these issues.
The most recent developments in edge AI for real-time video analytics in security systems are discussed in this paper. We discuss the different techniques that are being used, as well as the applications that are being enabled by edge AI. The paper also discusses the challenges and limitations of edge AI, and the future research directions in this area.
GPU Accelerated Simulation of Scene Generation of 3D Photonic Mixer Device Camera
Authors: Lade Sangita Gautam , Pawale Sanjesh , Patil Aniket 
Published: December 2023

Simulation of Photonic Mixer Device (PMD) sensors have the capability to create virtual environment to test 3D camera design. This simulation comprises of multiple steps like scene generation using ray tracing, power calculation, raw data generation and raw data processing. However, each step-in situation process takes longer time to implement and they are simulation process, simulators need to be faster. In this paper, we propose parallel implementation method for scene generation using GPGPUs. The feasibility of the method is confirmed using Amdahl’s law before implementation. The method is implemented and tested on GeForce 820M, GeForce 750Ti and Volta V100.Tthe highest speed up obtained is 219.913 using Volta (GV100) GPU for block size 1024. Thus, parallel method optimizes the scene generation time as compared to serial processing and the implemented results are better than the state of the art in the literature.
Harnessing Data-Driven Insights: Predictive Modeling for Diamond Price Forecasting using Regression and Classification Techniques
Authors: Basha Md Shaik Amzad , Oveis Peerzadah Mohammad 
Published: December 2023

In the multi-faceted world of gemology, understanding diamond valuations plays a pivotal role for traders, customers, and researchers alike. This study delves deep into predicting diamond prices in terms of exact monetary values and broader price categories. The purpose was to harness advanced machine learning techniques to achieve precise estimations and categorisations, thereby assisting stakeholders in informed decision-making. The research methodology adopted comprised a rigorous data preprocessing phase, ensuring the data's readiness for model training. A range of sophisticated machine learning models were employed, from traditional linear regression to more advanced ensemble methods like Random Forest and Gradient Boosting. The dataset was also transformed to facilitate classification into predefined price tiers, exploring the viability of models like Logistic Regression and Support Vector Machines in this context. The conceptual model encompasses a systematic flow, beginning with data acquisition, transitioning through preprocessing, regression, and classification analyses, and culminating in a comparative study of the performance metrics. This structured approach underscores the originality and value of our research, offering a holistic view of diamond price prediction from both regression and classification lenses. Findings from the analysis highlighted the superior performance of the Random Forest regressor in predicting exact prices with an R2 value of approximately 0.975. In contrast, for classification into price tiers, both Logistic Regression and Support Vector Machines emerged as frontrunners with an accuracy exceeding 95%. These results provide invaluable insights for stakeholders in the diamond industry, emphasising the potential of machine learning in refining valuation processes.