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134 results on '"Mekala, M. S."'

2. 3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X Orchestration

Author

Zhang, Haolin, Mekala, M S, Nain, Zulkar, Yang, Dongfang, Park, Ju H., and Jung, Ho-Youl

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Edge computing-based 3D perception has received attention in intelligent transportation systems (ITS) because real-time monitoring of traffic candidates potentially strengthens Vehicle-to-Everything (V2X) orchestration. Thanks to the capability of precisely measuring the depth information on surroundings from LiDAR, the increasing studies focus on lidar-based 3D detection, which significantly promotes the development of 3D perception. Few methods met the real-time requirement of edge deployment because of high computation-intensive operations. Moreover, an inconsistency problem of object detection remains uncovered in the pointcloud domain due to large sparsity. This paper thoroughly analyses this problem, comprehensively roused by recent works on determining inconsistency problems in the image specialisation. Therefore, we proposed a 3D harmonic loss function to relieve the pointcloud based inconsistent predictions. Moreover, the feasibility of 3D harmonic loss is demonstrated from a mathematical optimization perspective. The KITTI dataset and DAIR-V2X-I dataset are used for simulations, and our proposed method considerably improves the performance than benchmark models. Further, the simulative deployment on an edge device (Jetson Xavier TX) validates our proposed model's efficiency., Comment: Submitted to IEEE Transactions on Vehicular Technology

Published
2022

12. Efficient LiDAR-Trajectory Affinity Model for Autonomous Vehicle Orchestration

Author

Mekala, M. S., Dhiman, Gaurav, Viriyasitavat, Wattana, Park, Ju H., and Jung, Ho-Youl

Abstract

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target’s object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

Published
2024
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13. ASXC<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> Approach: A Service-X Cost Optimization Strategy Based on Edge Orchestration for IIoT

Author

Mekala, M. S., Dhiman, Gaurav, Park, Ju H., Jung, Ho-Youl, and Viriyasitavat, Wattana

Abstract

Most computation-intensive industry applications and servers encounter service-reliability challenges due to the limited resource capability of the edge. Achieving quality data fusion and accurate service reliability with optimized service-x execution cost is challenging. While existing systems have taken into account factors such as device service execution, residual resource ratio, and channel condition; the service execution time, cost, and utility ratios of requested services from devices and servers also have a significant impact on service execution cost. To enhance service quality and reliability, we design a 2-step adaptive service-X cost consolidation (ASXC2) approach. This approach is based on the node-centric Lyapunov method and distributed Markov mechanism, aiming to optimize the service execution error rate during offloading. The node-centric Lyapunov method incorporates cost and utility functions and node-centric features to estimate the service cost before offloading. Additionally, the Markov mechanism-inspired service latency prediction model design assists in mitigating the ratio of offload-service execution errors by establishing a mobility-correlation matrix between devices and servers. In addition, the non-linear programming multi-tenancy heuristic method design help to predict the service preferences for improving the resource utilisation ratio. The simulations show the effectiveness of our approach. The model performance is enhanced with 0.13% service offloading efficiency, 0.82% rate of service completion when transmitting data size is 400 kb, and 0.058% average service offloading efficiency with 40 CPU Megacycles when the vehicle moves 60 Km/h speed around the server communication range. Our model simulations indicate that our approach is highly effective and suitable for lightweight, complex environments.

Published
2024
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27. Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems.

Author

Nain, Zulqar, Shahana, B., Chaudhry, Shehzad Ashraf, Viswanathan, P., Mekala, M. S., and Sung Won Kim

Subjects
REINFORCEMENT learning, COMPUTATIONAL complexity, ASSET requirements, CYBER physical systems, INTERNET, NETWORK performance
Abstract

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and computation). The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services. In this regard, an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions. Consequently, the development of a reinforcement learning-based flexible x-scheduling (RFXS) approach resolves the service offloading issues, where x = service/resource for producing the low latency and high performance of the network. Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency. A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set of measurements, as well as the behavioural association rate and adulation factor. Our system managed an average 0.89% of the service offloading rate, with 39 ms of delay over complex scenarios (using three servers with a 50% service arrival rate). The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty, and is suitable for simulating heterogeneous CPS frameworks. [ABSTRACT FROM AUTHOR]

Published
2023
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28. Normalized Attention Neural Network with Adaptive Feature Recalibration for Detecting the Unusual Activities Using Video Surveillance Camera.

Author

Damera, Vijay Kumar, Vatambeti, Ramesh, Mekala, M. S., Pani, Alok Kumar, and Manjunath, Chinthakunta

Subjects
NEURAL circuitry, VIDEO surveillance, ECONOMIC security, FEATURE extraction, REGRESSION analysis
Abstract

Over the past few years, surveillance cameras have become common in many homes and businesses. Many businesses still employ a human monitor of their cameras, despite the fact that this individual is more probable to miss some anomalous occurrences in the video feeds owing to the inherent limitations of human perception. Numerous scholars have investigated surveillance data and offered several strategies for automatically identifying anomalous occurrences. Therefore, it is important to build a model for identifying unusual occurrences in the live stream from the security cameras. Recognizing potentially dangerous situations automatically so that appropriate action may be taken is crucial and can be of great assistance to law enforcement. In this research work, starting with an MRCNN for feature extraction and AFR for fine-tuning, this architecture has a number of key components (AFR). To increase the quality of the features extracted by the MRCNN, the AFR replicas the inter-dependencies among the features to enhance the quality of the low- and high-frequency features extracted. Then, a normalized attention network (NAN) is used to learn the relationships between channels, which used to identify the violence and speeds up the convergence process for training a perfect. Furthermore, the dataset took real-time security camera feeds from a variety of subjects and situations, as opposed to the hand-crafted datasets utilized in prior efforts. We also demonstrate the method's capability of assigning the correct category to each anomaly by classifying normal and abnormal occurrences. The method divided the information gathered into three primary groups: those in need of fire protection, those experiencing theft or violence, and everyone else. The study applied the proposed approach to the UCF-Crime dataset, where it outperformed other models on the same dataset. [ABSTRACT FROM AUTHOR]

Published
2023
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29. Efficient task optimization algorithm for green computing in cloud.

Author

G, Thanmayatejaswi, Ch, Dileep Chakravarthy, Varma, G. P. S., and Mekala, M. S.

Abstract

Cloud infrastructure assets are accessed by all hooked heterogeneous network servers and applications to maintain entail reliability towards global subscribers with high performance and low cost is a tedious challenging task. Most of the extant techniques are considered limited constraints like task deadline, which leads Service Level Agreement (SLA) violation. In this manuscript, we develop Hadoop based Task Scheduling (HTS) algorithm which considers a task deadline time, completion time, migration time and future resource availability of each virtual machine. The Intelligent System (IS) enabled with adaptive neural computation method to assess all above attributes. Specifically, the result of Prophecy Resource Availability (PRA) method has been used to assess the status of each Virtual Machine (VM), which helps to streamline the resource wastage and increases the response time with low SLA violation rate. [ABSTRACT FROM AUTHOR]

Published
2023
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36. Deep learning‐influenced joint vehicle‐to‐infrastructure and vehicle‐to‐vehicle communication approach for internet of vehicles.

Author

Mekala, M. S., Dhiman, Gaurav, Patan, Rizwan, Kallam, Suresh, Ramana, Kadiyala, Yadav, Kusum, and Alharbi, Ali O.

Subjects
*DEEP learning, *MULTICASTING (Computer networks), *INTERNET, *ARCHITECTURAL design, *VEHICLES
Abstract

The internet of vehicle (IoV) orchestration is an emerging technology in heterogeneous vehicles to contrivance diverse intelligent transportation applications. The roadside unit (RSU) plays a vital role during service provisioning. Vehicle‐to‐vehicle and vehicle‐to‐infrastructure communications have consistently accomplished the services in a vehicular network. However, persisting the increased vehicles' quality of experience and network vendors' utilities and which RSUs have to select for effective, reliable service are critical open research challenges to consolidate RSU services to enhance network service utility rate. In this article, we design a deep learning‐inspired RSU Service Consolidation Approach based on two‐models to enhance the service reliability by formulating the RSU coverage issue with the RSU Migration model and content delivery issue with Linear Programming‐based Multicast model. Adaptive Packet‐Error measurement system to optimize service reliability rate at the edge of cooperative vehicular network based on content correlation. The performance and efficiency are examined based on MATLAB. The simulation outcome shows RSC approach has low execution cost by 39%, service reliability rate by 71% than the state‐of‐art approaches. [ABSTRACT FROM AUTHOR]

Published
2022
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39. An Advanced System for Student ID Card Authentication with Facemapping

Author

Gatla, Shakti, B, Santosh Krishna, M, Anitha, Kothamasu, Lakshmi Anusha, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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40. Detection of Gastric Cancer Using Big Data Analytics

Author

Emani, Naga Bhavigna, Seemala, Kavya, Narlapati, Durga Naga Venkata Sai Madhuri, Sanikommu, Venkatasai Ravindra Reddy, Gera, Pradeepini, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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41. Novel Intrusion Detection Approach in Unbalanced Network Traffic Using Modified Random Forest Algorithm

Author

Ravi, P., Saravanan, N., Sriramu, D., Dhanusiya, E., Vinothkumar, M., Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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42. Explainable Computer Vision for Scene Analysis to Detect Crime

Author

Ankunda, Cynthia, Kyagaba, Jonah Mubuuke, Hellen, Nakayiza, Marvin, Ggaliwango, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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43. PRS-UBR: Product Recommender System Using Utility-Based Recommendation

Author

Cruz Antony, J., Thanzia Raksheen, I., Raj, Padma Sri, Deepa, D., Vignesh, R., Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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44. Multifunctional Language Processing Software Framework

Author

Poojary, Amruta, Naskar, Pooja, Deshmukh, Vaishnavi, Mhatre, Sneha, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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45. MediScan Handwritten Prescription Translator Using Deep Learning

Author

Vidhya, S., Yukendhira, M., Sidharth, K., Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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46. Presentation Attack Detection for Multispectral Face Biometric System Using Federated Learning

Author

Malayinmel Purushothaman, Manulal, Adapa, Srinivasa Rao, Bellamkonda, Sivaiah, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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47. An Algorithm for the Computer-Aided Method of Empathy Training by Modulating Shared Intentionality

Author

Val Danilov, Igor, Mihailova, Sandra, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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48. Pest Detection and Eco-Friendly Pesticide Sorting Using Deep Learning

Author

Tapasvi, B., Shireen, Mohammed, Kumar, Penta Nithin, Kalyan, Nuli Shanmukha Manikanta, Murari, Puppala Mohan, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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49. Multi-algorithmic Genetic Disease Detection Using Pupillometry

Author

Lakshmi, A. Jaya, Gajre, Shruti, Burra, Akhil, Reddy, Navar Koushik, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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50. Machine Learning-Based and Deep Learning-Based Intrusion Detection System: A Systematic Review

Author

Upadhyay, Deepak, Patel, Pranav, Rocha, Álvaro, Series Editor, Hameurlain, Abdelkader, Editorial Board Member, Idri, Ali, Editorial Board Member, Vaseashta, Ashok, Editorial Board Member, Dubey, Ashwani Kumar, Editorial Board Member, Montenegro, Carlos, Editorial Board Member, Laporte, Claude, Editorial Board Member, Moreira, Fernando, Editorial Board Member, Peñalvo, Francisco, Editorial Board Member, Dzemyda, Gintautas, Editorial Board Member, Mejia-Miranda, Jezreel, Editorial Board Member, Hall, Jon, Editorial Board Member, Piattini, Mário, Editorial Board Member, Holanda, Maristela, Editorial Board Member, Tang, Mincong, Editorial Board Member, Ivanovíc, Mirjana, Editorial Board Member, Muñoz, Mirna, Editorial Board Member, Kanth, Rajeev, Editorial Board Member, Anwar, Sajid, Editorial Board Member, Herawan, Tutut, Editorial Board Member, Colla, Valentina, Editorial Board Member, Devedzic, Vladan, Editorial Board Member, Ragavendiran, S. D. Prabu, editor, Pavaloaia, Vasile Daniel, editor, Mekala, M. S., editor, and Cabezuelo, Antonio Sarasa, editor

Published
2024
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