Documentos donde el Autor es "Anand, Divya"

Artículo

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Abierto Inglés This paper presents grid-oriented multiobjective harmony search algorithm (GOMOHSA) to incorporate the multiple grid parameters for minimization of the active power loss, reactive power loss, and total voltage deviations (TVD) in a part of practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in southern parts of Rajasthan state of India. This is achieved by optimal deployment of optimally sized renewable energy (RE) generators using GOMOHSA. Performance indexes such as active power loss minimization index (APMLI), the reactive power loss minimization index (RPMLI), and the total voltage deviation improvement index (TVDII) are introduced to evaluate the health of the test network with different load scenarios. Performance of proposed GOMOHSA has been tested for five different operating scenarios of loads and RE generation. It is established that the proposed GOMOHSA finds the optimal deployment of optimally sized RE generators, and the investment cost of deployment of these RE generators can be recovered within a time period that is less than 5 years. Performance of GOMOHSA is superior compared to a conventional genetic algorithm (GA) in terms of performance indexes, RE generator capacity, payback period, and parameter sensitivity. Study is performed using MATLAB software for loading scenario of base year 2021 and projected year 2031. metadata Kumar, Pramod; Swarnkar, Nagendra Kumar; Mahela, Om Prakash; Khan, Baseem; Anand, Divya; Singh, Aman; Vidal Mazón, Juan Luis; Alharithi, Fahd S. y Saikia, Lalit Chandra mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, juanluis.vidal@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements. International Transactions on Electrical Energy Systems, 2023. pp. 1-19. ISSN 2050-7038

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés This paper introduced a method using hybrid combination of network restructuring and optimal placement of optimally sized distributed generators (DG) to reduce loss and improve voltage profile in a practical transmission network for scenario of high load demand for a period of ten years. A study is performed for four study cases which includes the test transmission network without considering optimal DG placement and network restructuring, considering network restructuring, optimal placement of DG units using proposed grid parameter oriented harmony search algorithm (GPOHSA) and considering hybrid combination of network restructuring and DG placement using GPOHSA. Network restructuring is achieved by addition of a new 400 kV Grid-substation (GSS) and a 220 kV GSS along with associated transmission system. GPOHSA is obtained by a modification in the conventional harmony search algorithm (HSA) where grid coordinates are used for locating the individuals in an objective space. Performance Improvement Indicators such as real power loss reduction indicator (SPLRI), reactive power loss reduction indicator (SQLRI) and summation of node voltage deviation reduction indicator (SNVDRI) are proposed to evaluate performance of each case of study. The period of investment return is assessed to evaluate the pay back period of the investments incurred in network restructuring and DG units. It is established that hybrid combination of network restructuring and DG units placement using GPOHSA is effective to meet the increased load demand for time period of ten years with reduced losses and improved voltage profile. Investment incurred on the network restructuring and DG units placement will be recovered in a time period of 4 years. Effectiveness of the GPOHSA is better relative to the conventional genetic algorithm (GA) for DG unit placement. The study is performed using the MATLAB software on a practical transmission network in India. metadata Kumar, Pramod; Swarnkar, Nagendra Kumar; Ali, Ahmed; Mahela, Om Prakash; Khan, Baseem; Anand, Divya y Brito Ballester, Julién mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, julien.brito@uneatlantico.es (2023) Transmission Network Loss Reduction and Voltage Profile Improvement Using Network Restructuring and Optimal DG Placement. Sustainability, 15 (2). p. 976. ISSN 2071-1050

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world. In general, it is difficult for a person to know if they are under stress. According to previous research, temperature, heart rate variability (HRV), humidity, and blood pressure are used to assess stress levels with the use of instruments. With the development of sensor technology and wireless connectivity, people around the world are adopting and using smart devices. In this study, a bio signal detection device with Internet of Things (IoT) capability with a galvanic skin reaction (GSR) sensor is proposed and built for real-time stress monitoring. The proposed device is based on an Arduino controller and Bluetooth communication. To evaluate the performance of the system, physical stress is created on 10 different participants with three distinct tasks namely reading, visualizing the timer clock, and watching videos. MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e., relaxed for <1.75 volts; Normal: between 1.75 and 1.44 volts and stressed: >1.44 volts. In addition, LabVIEW is used as a data acquisition system, and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication. metadata Singh, Rajesh; Gehlot, Anita; Saxena, Ritika; Alsubhi, Khalid; Anand, Divya; Delgado Noya, Irene; Vaseem Akram, Shaik y Choudhury, Sushabhan mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, irene.delgado@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2023) Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI. Computers, Materials & Continua, 74 (1). pp. 1217-1233. ISSN 1546-2226

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Cerrado Inglés The Internet of Things (IoT) is a network of interconnected devices that includes low-end devices (sensors) and high-end devices (servers). The routing protocol used the Low-Power and Lossy Networks (RPL) protocol, which was designed to collect data in Low-Power and Lossy Networks (LLN) efficiently and reliably. The RPL rank property specifies how sensor nodes are placed in Destination Oriented Directed Acyclic Graphs (DODAG) based on an Objective Function (OF). The OF includes information such as the Expected Transmission Count (ETX) and packet delivery rate. The rank property aids in routing path optimization, reducing control overhead, and maintaining a loop-free topology by using rank-based data path validation. However, it causes many issues, such as optimal parent selection, next-hop node selection, and network instability. We proposed an Enhanced Opportunistic Rank-based Parent Node Selection for Sustainable and Smart IoT Networks to address these issues. The optimal parent node is determined by forecasting the expected energy of each node using Received Signal Strength (RSS) and an enhanced reinforcement learning algorithm. The proposed method addresses the issue of selecting the next-hop neighbor node and improves routing stability. Furthermore, when a large number of new nodes try to join the sustainable IoT-based smart cities, the proposed technique provides optimal load balance metadata Chithaluru, Premkumar; Singh, Aman; Mahmoud, Mahmoud Shuker; Kumar, Sunil; Vidal Mazón, Juan Luis; Alkhayyat, Ahmed y Anand, Divya mail SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, juanluis.vidal@uneatlantico.es, SIN ESPECIFICAR, divya.anand@uneatlantico.es (2023) An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks. Sustainable Energy Technologies and Assessments, 56. p. 103079. ISSN 22131388

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Rivers are dynamic geological agents on the earth which transport the weathered materials of the continent to the sea. Estimation of suspended sediment yield (SSY) is essential for management, planning, and designing in any river basin system. Estimation of SSY is critical due to its complex nonlinear processes, which are not captured by conventional regression methods. Rainfall, temperature, water discharge, SSY, rock type, relief, and catchment area data of 11 gauging stations were utilized to develop robust artificial intelligence (AI), similar to an artificial-neural-network (ANN)-based model for SSY prediction. The developed highly generalized global single ANN model using a large amount of data was applied at individual gauging stations for SSY prediction in the Mahanadi River basin, which is one of India’s largest peninsular rivers. It appeared that the proposed ANN model had the lowest root-mean-squared error (0.0089) and mean absolute error (0.0029) along with the highest coefficient of correlation (0.867) values among all comparative models (sediment rating curve and multiple linear regression). The ANN provided the best accuracy at Tikarapara among all stations. The ANN model was the most suitable substitute over other comparative models for SSY prediction. It was also noticed that the developed ANN model using the combined data of eleven stations performed better at Tikarapara than the other ANN which was developed using data from Tikarapara only. These approaches are suggested for SSY prediction in river basin systems due to their ease of implementation and better performance. metadata Yadav, Arvind; Chithaluru, Premkumar; Singh, Aman; Joshi, Devendra; Elkamchouchi, Dalia H.; Mazas Pérez-Oleaga, Cristina y Anand, Divya mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, cristina.mazas@uneatlantico.es, divya.anand@uneatlantico.es (2022) An Enhanced Feed-Forward Back Propagation Levenberg–Marquardt Algorithm for Suspended Sediment Yield Modeling. Water, 14 (22). p. 3714. ISSN 2073-4441

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The world population is on the rise, which demands higher food production. The reduction in the amount of land under cultivation due to urbanization makes this more challenging. The solution to this problem lies in the artificial cultivation of crops. IoT and sensors play an important role in optimizing the artificial cultivation of crops. The selection of sensors is important in order to ensure a better quality and yield in an automated artificial environment. There are many challenges involved in selecting sensors due to the highly competitive market. This paper provides a novel approach to sensor selection for saffron cultivation in an IoT-based environment. The crop used in this study is saffron due to the reason that much less research has been conducted on its hydroponic cultivation using sensors and its huge economic impact. A detailed hardware-based framework, the growth cycle of the crop, along with all the sensors, and the block layout used for saffron cultivation in a hydroponic medium are provided. The important parameters for a hydroponic medium, such as the concentration of nutrients and flow rate required, are discussed in detail. This paper is the first of its kind to explain the sensor configurations, performance metrics, and sensor-based saffron cultivation model. The paper discusses different metrics related to the selection, use and role of sensors in different IoT-based saffron cultivation practices. A smart hydroponic setup for saffron cultivation is proposed. The results of the model are evaluated using the AquaCrop simulator. The simulator is used to evaluate the value of performance metrics such as the yield, harvest index, water productivity, and biomass. The values obtained provide better results as compared to natural cultivation. metadata Kour, Kanwalpreet; Gupta, Deepali; Gupta, Kamali; Anand, Divya; Elkamchouchi, Dalia H.; Mazas Pérez-Oleaga, Cristina; Ibrahim, Muhammad y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, cristina.mazas@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation. Sensors, 22 (22). p. 8905. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Abierto Inglés This study involves a working limestone mine that supplies limestone to the cement factory. The two main goals of this paper are to (a) determine how long an operating mine can continue to provide the cement plant with the quality and quantity of materials it needs, and (b) explore the viability of combining some limestone from a nearby mine with the study mine limestone to meet the cement plant’s quality and quantity goals. These objectives are accomplished by figuring out the maximum net profit for the ultimate pit limit and production sequencing of the mining blocks. The issues were resolved using a branch-and-cut based sequential integer and mixed integer programming problem. The study mine can exclusively feed the cement plant for up to 15 years, according to the data. However, it was also noted that the addition of the limestone from the neighboring mine substantially increased the mine’s life (85 years). The findings also showed that, when compared with the production planning formulation that the company is now using, the proposed approach creates 10% more profit. The suggested method also aids in determining the desired desirable quality of the limestone that will be transported from the nearby mine throughout each production stage. metadata Joshi, Devendra; Chithaluru, Premkumar; Singh, Aman; Yadav, Arvind; Elkamchouchi, Dalia H.; Breñosa, Jose y Anand, Divya mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, josemanuel.brenosa@uneatlantico.es, divya.anand@uneatlantico.es (2022) An Optimized Open Pit Mine Application for Limestone Quarry Production Scheduling to Maximize Net Present Value. Mathematics, 10 (21). p. 4140. ISSN 2227-7390

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés In today’s technological and stressful world, when everyone is busy in their daily routines and places blind faith in pharmaceutical advancements to protect their health, the sudden, horrifying effects of the COVID-19 pandemic have resulted in serious emotional and psychological impacts in the general population. In spite of advanced vaccination campaigns, fear and hesitation have become a part of human life since there are a number of people who do not want to take these immunity boosting vaccinations. Such people may become carriers of infectious viruses, leading to a more rapid rate of spread; therefore, this class of spreaders needs to be screened at the earliest opportunity. In this context, there is a need for advanced health monitoring systems which can assist the pharmaceutical industry to monitor and record the health status of people. To address this need and reduce the uncertainty of the situation, this study has designed and tested an Internet of Things (IoT) and Fog computing-based multi-node architecture was for real-time initial screening and recording of such subjects. The proposed system was able to record current body temperature and location coordinates along with the facial images. Further, the proposed system was able to transmit data to a cloud database using internet-connected services. An implementation and reviews-based working environment analysis was conducted to determine the efficacy of the proposed system. It was observed from the statistical analysis that the proposed IoT Fog-enabled ecosystem could be utilized efficiently. metadata Khullar, Vikas; Singh, Harjit Pal; Miró Vera, Yini Airet; Anand, Divya; Mohamed, Heba G.; Gupta, Deepali; Kumar, Navdeep y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, yini.miro@uneatlantico.es, divya.anand@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) IoT Fog-Enabled Multi-Node Centralized Ecosystem for Real Time Screening and Monitoring of Health Information. Applied Sciences, 12 (19). p. 9845. ISSN 2076-3417

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Traditional optimization of open pit mine design is a crucial component of mining endeavors and is influenced by many variables. The critical factor in optimization is the geological uncertainty, which relates to the ore grade. To deal with uncertainties related to the block economic values of mining blocks and the general problem of mine design optimization, under unknown conditions, the best ultimate pit limits and pushback designs are produced by a minimum cut algorithm. The push–relabel minimal cut algorithm provides a framework for computationally efficient representation and processing of the economic values of mining blocks under multiple scenarios. A sequential Gaussian simulation-based smoothing spline technique was created. To produce pushbacks, an efficient parameterized minimum cut algorithm is suggested. An analysis of Indian iron ore mining was performed. The developed mine scheduling algorithm was compared with the conventional algorithm, and the results show that when uncertainty is considered, the cumulative metal production is higher and there is an additional increase of about 5% in net present value. The results of this work help the mining industry to plan mines in such a way that can generate maximum profit from the deposits. metadata Joshi, Devendra; Chithaluru, Premkumar; Singh, Aman; Yadav, Arvind; Elkamchouchi, Dalia H.; Mazas Pérez-Oleaga, Cristina y Anand, Divya mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, cristina.mazas@uneatlantico.es, divya.anand@uneatlantico.es (2022) A Novel Large-Scale Stochastic Pushback Design Merged with a Minimum Cut Algorithm for Open Pit Mine Production Scheduling. Systems, 10 (5). p. 159. ISSN 2079-8954

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés One of the toughest biometrics and document forensics problems is confirming a signature’s authenticity and legal identity. A forgery may vary from a genuine signature by specific distortions. Therefore, it is necessary to continuously monitor crucial distinctions between real and forged signatures for secure work and economic growth, but this is particularly difficult in writer-independent tasks. We thus propose an innovative and sustainable writer-independent approach based on a Siamese neural network for offline signature verification. The Siamese network is a twin-like structure with shared weights and parameters. Similar and dissimilar images are exposed to this network, and the Euclidean distances between them are calculated. The distance is reduced for identical signatures, and the distance is increased for different signatures. Three datasets, namely GPDS, BHsig260 Hindi, and BHsig260 Bengali datasets, were tested in this work. The proposed model was analyzed by comparing the results of different parameters such as optimizers, batch size, and the number of epochs on all three datasets. The proposed Siamese neural network outperforms the GPDS synthetic dataset in the English language, with an accuracy of 92%. It also performs well for the Hindi and Bengali datasets while considering skilled forgeries metadata Sharma, Neha; Gupta, Sheifali; Mohamed, Heba G.; Anand, Divya; Vidal Mazón, Juan Luis; Gupta, Deepali y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, juanluis.vidal@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification. Sustainability, 14 (18). p. 11484. ISSN 2071-1050

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Cerrado Inglés Smart vehicle parking is a collaborative effort of technology and human innovation where the efforts are to be minimized to save time and efforts. In smart cities it is one of the common challenges to introduce smart parking to increase parking efficiency and combat numerous issues like identification of free parking slot and real-time dynamic updation on traffic to save fuel and energy. In this work, a new cloud-based smart parking architecture is proposed that can help in predicting the available free parking slots in smart cities. Initially, the methodology collects the car count at any near by parking using Internet of Things (IoT) and Cloud-based approach. Later, the approach uses the Kernel Least Mean Square algorithm to make heuristic predictions about future vacancy using auto-regression. The proposed approach thus utilizes the online learning or model training. To validate the efficacy of the proposed work, the testing is done on the real-time dataset. The extensive numerical investigation is performed on parking lots of four international airports of a smart city in actual deployment scenarios. The experimentation has revealed superior performance of the method in terms of vacancy prediction. metadata Anand, Divya; Singh, Aman; Alsubhi, Khalid; Goyal, Nitin; Abdrabou, Atef; Vidyarthi, Ankit y Rodrigues, Joel J. P. C. mail divya.anand@uneatlantico.es, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) A Smart Cloud and IoVT-Based Kernel Adaptive Filtering Framework for Parking Prediction. IEEE Transactions on Intelligent Transportation Systems. pp. 1-9. ISSN 1524-9050

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés MANET is a mobile ad hoc network with many mobile nodes communicating without a centralized module. Infrastructure-less networks make it desirable for many researchers to publish and bind multimedia services. Each node in this infrastructure-less network acts as self-organizing and re-configurable. It allows services to deploy and attain from another node over the ad hoc network. The service composition aims to provide a user’s requirement by combining different atomic services based on non-functional QoS parameters such as reliability, availability, scalability, etc. To provide service composition in MANET is challenging because of the node mobility, link failure, and topology changes, so a traditional protocol will be sufficient to obtain real-time services from mobile nodes. In this paper, the ad hoc on-demand distance vector protocol (AODV) is used and analyzed based on MANET’s QoS (Quality of Service) metrics. The QoS metrics for MANET depends on delay, bandwidth, memory capacity, network load, and packet drop. The requester node and provider node broker acts as a composer for this MANET network. The authors propose a QoS-based Dynamic Secured Broker Selection architecture (QoSDSBS) for service composition in MANET, which uses a dynamic broker and provides a secure path selection based on QoS metrics. The proposed algorithm is simulated using Network Simulator (NS2) with 53 intermediate nodes and 35 mobile nodes of area 1000 m × 1000 m. The comparative results show that the proposed architecture outperforms, with standards, the AODV protocol and affords higher scalability and a reduced network load metadata Ramalingam, Rajakumar; Muniyan, Rajeswari; Dumka, Ankur; Singh, Devesh Pratap; Mohamed, Heba G.; Singh, Rajesh; Anand, Divya y Delgado Noya, Irene mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, irene.delgado@uneatlantico.es (2022) Routing Protocol for MANET Based on QoS-Aware Service Composition with Dynamic Secured Broker Selection. Electronics, 11 (17). p. 2637. ISSN 2079-9292

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés To provide faster access to the treatment of patients, healthcare system can be integrated with Internet of Things to provide prior and timely health services to the patient. There is a huge limitation in the sensing layer as the IoT devices here have low computational power, limited storage and less battery life. So, this huge amount of data needs to be stored on the cloud. The information and the data sensed by these devices is made accessible on the internet from where medical staff, doctors, relatives and family members can access this information. This helps in improving the treatment as well as getting faster medical assistance, tracking of routine activities and health focus of elderly people on frequent basis. However, the data transmission from IoT devices to the cloud faces many security challenges and is vulnerable to different security and privacy threats during the transmission path. The purpose of this research is to design a Certificateless Secured Signature Scheme that will provide a magnificent amount of security during the transmission of data. Certificateless signature, that removes the intricate certificate management and key escrow problem, is one of the practical methods to provide data integrity and identity authentication for the IoT. Experimental result shows that the proposed scheme performs better than the existing certificateless signature schemes in terms of computational cost, encryption and decryption time. This scheme is the best combination of high security and cost efficiency and is further suitable for the resource constrained IoT environment. metadata Kakkar, Latika; Gupta, Deepali; Tanwar, Sarvesh; Saxena, Sapna; Alsubhi, Khalid; Anand, Divya; Delgado Noya, Irene y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, irene.delgado@uneatlantico.es, SIN ESPECIFICAR (2022) A Secure and Efficient Signature Scheme for IoT in Healthcare. Computers, Materials & Continua, 73 (3). pp. 6151-6168. ISSN 1546-2226

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés SIN ESPECIFICAR metadata Kimothi, Sanjeev; Thapliyal, Asha; Akram, Shaik Vaseem; Singh, Rajesh; Gehlot, Anita; Mohamed, Heba G.; Anand, Divya; Ibrahim, Muhammad y Delgado Noya, Irene mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, irene.delgado@uneatlantico.es (2022) Big Data Analysis Framework for Water Quality Indicators with Assimilation of IoT and ML. Electronics, 11 (13). p. 1927. ISSN 2079-9292

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Large-scale distributed systems have the advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data within a time constraint becomes tricky, due to the complexity of data parallel task scheduling in a time constrained environment. This paper proposes data parallel task scheduling in cloud to address the minimization of cost and time constraints. By running concurrent executions of tasks on multi-core cloud resources, the number of parallel executions could be increased correspondingly, thereby, finishing the task within the deadline is possible. A mathematical model is developed here to minimize the operational cost of data parallel tasks by feasibly assigning a load to each virtual machine in the cloud data center. This work experiments with a machine learning model that is replicated on the multi-core cloud heterogeneous resources to execute different input data concurrently to accomplish distributive learning. The outcome of concurrent execution of data-intensive tasks on different parts of the input dataset gives better solutions in terms of processing the task by the deadline at optimized cost. metadata Rajalakshmi, N. R.; Dumka, Ankur; Kumar, Manoj; Singh, Rajesh; Gehlot, Anita; Akram, Shaik Vaseem; Anand, Divya; Elkamchouchi, Dalia H. y Delgado Noya, Irene mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, irene.delgado@uneatlantico.es (2022) A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud. Electronics, 11 (13). p. 2022. ISSN 2079-9292

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Food and agriculture are significant aspects that can meet the food demand estimated by the Food Agriculture Organization (FAO) by 2050. In addition to this, the United Nations sustainable development goals recommended implementing sustainable practices to meet food demand to achieve sustainability. Currently, aquaponics is one of the sustainable practices that require less land and water and has a low environmental impact. Aquaponics is a closed-loop and soil-less method of farming, where it requires intensive monitoring, control, and management. The advancement of wireless sensors and communication protocols empowered to implementation of an Internet of Things- (IoT-) based system for real-time monitoring, control, and management in aquaponics. This study presents a review of the wireless technology implementation and progress in aquaponics. Based on the review, the study discusses the significant water and environmental parameters of aquaponics. Followed by this, the study presents the implementation of remote, IoT, and ML-based monitoring of aquaponics. Finally, the review presents the recommendations such as edge and fog-based vision nodes, machine learning models for prediction, LoRa-based sensor nodes, and gateway-based architecture that are beneficial for the enhancement of wireless aquaponics and also for real-time prediction in the future. metadata Gayam, Kiran Kumari; Jain, Anuj; Gehlot, Anita; Singh, Rajesh; Akram, Shaik Vaseem; Singh, Aman; Anand, Divya; Delgado Noya, Irene y Ahmad, Shafiq mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@unic.co.ao, divya.anand@uneatlantico.es, irene.delgado@uneatlantico.es, SIN ESPECIFICAR (2022) Imperative Role of Automation and Wireless Technologies in Aquaponics Farming. Wireless Communications and Mobile Computing, 2022. pp. 1-13. ISSN 1530-8669

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés In December 2019, a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia. The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9, 2020, named Novel Coronavirus 2019 (nCoV-2019). This nCoV-2019 is now known as COVID-19. There is a big list of infections of this coronavirus which is present in the form of a big family. This virus can cause several diseases that usually develop with a serious problem. According to the World Health Organization (WHO), 2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) coronaviruses, so COVID-19 can repeatedly change its internal genome structure to extend its existence. Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can form a good leadership role in preventing and fighting against coronavirus. In this research paper, an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses’ complete genome. This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties. This paper identifies five main clusters of mutations with as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses. metadata Dumka, Ankur; Verma, Parag; Singh, Rajesh; Bhardwaj, Anuj; Alsubhi, Khalid; Anand, Divya; Delgado Noya, Irene y Aparicio Obregón, Silvia mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, irene.delgado@uneatlantico.es, silvia.aparicio@uneatlantico.es (2022) Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome. Computers, Materials & Continua, 72 (3). pp. 4453-4466. ISSN 1546-2226

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Education 4.0 imitates Industry 4.0 in many aspects such as technology, customs, challenges, and benefits. The remarkable advancement in embryonic technologies, including IoT (Internet of Things), Fog Computing, Cloud Computing, and Augmented and Virtual Reality (AR/VR), polishes every dimension of Industry 4.0. The constructive impacts of Industry 4.0 are also replicated in Education 4.0. Real-time assessment, irregularity detection, and alert generation are some of the leading necessities of Education 4.0. Conspicuously, this study proposes a reliable assessment, irregularity detection, and alert generation framework for Education 4.0. The proposed framework correspondingly addresses the comparable issues of Industry 4.0. The proposed study (1) recommends the use of IoT, Fog, and Cloud Computing, i.e., IFC technological integration for the implementation of Education 4.0. Subsequently, (2) the Symbolic Aggregation Approximation (SAX), Kalman Filter, and Learning Bayesian Network (LBN) are deployed for data pre-processing and classification. Further, (3) the assessment, irregularity detection, and alert generation are accomplished over SoTL (the set of threshold limits) and the Multi-Layered Bi-Directional Long Short-Term Memory (M-Bi-LSTM)-based predictive model. To substantiate the proposed framework, experimental simulations are implemented. The experimental outcomes substantiate the better performance of the proposed framework, in contrast to the other contemporary technologies deployed for the enactment of Education 4.0 metadata Verma, Anil; Anand, Divya; Singh, Aman; Vij, Rishika; Alharbi, Abdullah; Alshammari, Majid y Ortega-Mansilla, Arturo mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, arturo.ortega@uneatlantico.es (2022) IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0. Electronics, 11 (9). p. 1436. ISSN 2079-9292

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown. In this research, we have used a Long Short-Term Memory (LSTM) network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive, negative, or neutral emotional out bust based on their Twitter posts. The results showed that the model has 88.14% accuracy (representation of the correct prediction over the test dataset) after 10 epochs which most tweets showed had neutral polarity. The evaluation shows interesting results in positive (1), negative (–1), and neutral (0) emotions through different visualization. metadata Dumka, Ankur; Verma, Parag; Singh, Rajesh; Kumar Bisht, Anil; Anand, Divya; Moaiteq Aljahdali, Hani; Delgado Noya, Irene y Aparicio Obregón, Silvia mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, irene.delgado@uneatlantico.es, silvia.aparicio@uneatlantico.es (2022) A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis. Computers, Materials & Continua, 72 (3). pp. 6029-6044. ISSN 1546-2226

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumukhi script is a complex script for which a holistic approach can be proposed for offline handwritten word recognition. In this paper, the authors propose a Convolutional Neural Network-based architecture for recognition of the Gurumukhi month names. The architecture is designed with five convolutional layers and three pooling layers. The authors also prepared a dataset of 24,000 images, each with a size of 50 × 50. The dataset was collected from 500 distinct writers of different age groups and professions. The proposed method achieved training and validation accuracies of about 97.03% and 99.50%, respectively for the proposed dataset. metadata Singh, Tajinder Pal; Gupta, Sheifali; Garg, Meenu; Gupta, Deepali; Alharbi, Abdullah; Alyami, Hashem; Anand, Divya; Ortega-Mansilla, Arturo y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, arturo.ortega@uneatlantico.es, SIN ESPECIFICAR (2022) Visualization of Customized Convolutional Neural Network for Natural Language Recognition. Sensors, 22 (8). p. 2881. ISSN 1424-8220

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés The rapid expansion of Internet of Things (IoT) devices deploys various sensors in different applications like homes, cities and offices. IoT applications depend upon the accuracy of sensor data. So, it is necessary to predict faults in the sensor and isolate their cause. A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults. This technique identifies the faulty sensor and determines the correct working of the sensor. Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form. Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described. There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique. So, some solutions are provided to overcome the limitations of the fall curve technique. In this paper, a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years. Its novelty is to predict a fault before its occurrence by looking at the fall curve. The sensing of current flow in devices is important to prevent a major loss. So, the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices. The analysis result proved that if any of the current sensors gets faulty, then the fall curve will differ and the value will immediately drop to zero. Various evaluation metrics for fault prediction are also described in this paper. At last, this paper also addresses some possible open research issues which are important to deal with false IoT sensor data. metadata Uppal, Mudita; Gupta, Deepali; Anand, Divya; S. Alharithi, Fahd; Almotiri, Jasem; Ortega-Mansilla, Arturo; Singh, Dinesh y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, arturo.ortega@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve. Computers, Materials & Continua, 72 (1). pp. 1799-1814. ISSN 1546-2226

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Using artificial intelligence (AI) based software defect prediction (SDP) techniques in the software development process helps isolate defective software modules, count the number of software defects, and identify risky code changes. However, software development teams are unaware of SDP and do not have easy access to relevant models and techniques. The major reason for this problem seems to be the fragmentation of SDP research and SDP practice. To unify SDP research and practice this article introduces a cloud-based, global, unified AI framework for SDP called DePaaS—Defects Prediction as a Service. The article describes the usage context, use cases and detailed architecture of DePaaS and presents the first response of the industry practitioners to DePaaS. In a first of its kind survey, the article captures practitioner’s belief into SDP and ability of DePaaS to solve some of the known challenges of the field of software defect prediction. This article also provides a novel process for SDP, detailed description of the structure and behaviour of DePaaS architecture components, six best SDP models offered by DePaaS, a description of algorithms that recommend SDP models, feature sets and tunable parameters, and a rich set of challenges to build, use and sustain DePaaS. With the contributions of this article, SDP research and practice could be unified enabling building and using more pragmatic defect prediction models leading to increase in the efficiency of software testing metadata Pandit, Mahesha; Gupta, Deepali; Anand, Divya; Goyal, Nitin; Aljahdali, Hani Moaiteq; Ortega-Mansilla, Arturo; Kadry, Seifedine y Kumar, Arun mail SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, arturo.ortega@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework. Applied Sciences, 12 (1). p. 493. ISSN 2076-3417

Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés A population explosion has resulted in garbage generation on a large scale. The process of proper and automatic garbage collection is a challenging and tedious task for developing countries. This paper proposes a deep learning-based intelligent garbage detection system using an Unmanned Aerial Vehicle (UAV). The main aim of this paper is to provide a low-cost, accurate and easy-to-use solution for handling the garbage effectively. It also helps municipal corporations to detect the garbage areas in remote locations automatically. This automation was derived using two Convolutional Neural Network (CNN) models and images of solid waste were captured by the drone. Both models were trained on the collected image dataset at different learning rates, optimizers and epochs. This research uses symmetry during the sampling of garbage images. Homogeneity regarding resizing of images is generated due to the application of symmetry to extract their characteristics. The performance of two CNN models was evaluated with the state-of-the-art models using different performance evaluation metrics such as precision, recall, F1-score, and accuracy. The CNN1 model achieved better performance for automatic solid waste detection with 94% accuracy metadata Verma, Vishal; Gupta, Deepali; Gupta, Sheifali; Uppal, Mudita; Anand, Divya; Ortega-Mansilla, Arturo; Alharithi, Fahd S.; Almotiri, Jasem y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, arturo.ortega@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle. Symmetry, 14 (5). p. 960. ISSN 2073-8994

Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm metadata Bali, Himani; Gill, Amandeep; Choudhary, Abhilasha; Anand, Divya; Alharithi, Fahd S.; Aldossary, Sultan M. y Vidal Mazón, Juan Luis mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, juanluis.vidal@uneatlantico.es (2022) Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network. Energies, 15 (14). p. 5237. ISSN 1996-1073

Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés The proper verification of users plays a vital role during communication over a social network to protect the personal data of users. Multifarious protocols have been implemented to secure the confidential data of the users, but these protocols have various limitations and are incapable of providing secrecy of data against various attacks, such as replay and cryptanalysis attacks. In this article, the authors proposed a novel method for security verification of the social network model using an improved three-party authenticated key exchange (3PAKE) protocol based on symmetric encryption and (ECC) elliptic curve cryptography. The outcome of the paper demonstrates that our proposed algorithm provides the desired secrecy to the confidential data exchange over social networks in real-time and consumes less time in comparison to existing protocols. Our protocol consumes a search time of 0.09 s, overall communication steps took 2 during the verification, and depth plies was 3 along with 20 visited nodes. The 3PAKE protocol has been considered a suitable approach for social network secrecy during information exchange between user and server, thereby providing greater secrecy to the user in data exchange over social networks and more robustness against multifarious known attacks, such as cryptanalysis and replay attacks in real-time metadata Sinha, Vivek Kumar; Anand, Divya; Kaur, Sandeep; Singh, Pankaj y Delgado Noya, Irene mail SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR, irene.delgado@uneatlantico.es (2022) Security Verification of Social Network Model Using Improved Three-Party Authenticated Key Exchange Protocol. Symmetry, 14 (8). p. 1567. ISSN 2073-8994