Volume-12 , Issue-6 , Dec 2024, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Deep Neural Network and Multiparametric Deep Neural Networks for Harvest Predictions

  S. Ashok Kumar, S. Anusya

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.6 , pp.1-5, Dec-2024

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  Abstract
  Deep Neural Network is a Deep Learning technique which was used for predicting the crop yields. To forecast various crop yields, the Deep Neural Network(DNN) used a series of non-linear layers, which, at each level, abstracted the original meteorological and soil data. The paper proposes use of the Multiparametric Deep Neural Network(MDNN) to simulate various soil properties, climatic variables such as temperature, vapour pressure, cloud coverage, frequency of the damp days and humidity and other climatic variations in order to forecast the agricultural yields outperforming the existing DNN. In order to improve the accuracy the MDNN uses the data from the past. This paper presents an improvement of accuracy of the harvest predictions which will help farmers and officials which aid for better yield. Results from the experiments show that in predicting the agricultural yields of the selected crops the MDNN performs better than the DNN.

  Key-Words / Index Term
  Agriculture, Crop Yield, Deep Neural Network, Harvest, Hyperparameters, Multiparametric Neural Networks

  References
  [1] Chen, Y., Lee, W. S., Gan, H., Peres, N., Fraisse, C., Zhang, Y., & He, Y, “Strawberry Yield Prediction Based on a Deep Neural Network using High-Resolution Aerial Orthoimages”, Remote Sensing, Vol.11, Issue.13, pp.1584, 2019.
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  [6] Hanuman, V., Pinnamaneni, K.V, & Singh, T.,“Best Fit Radial Kernel Support Vector Machine for Intelligent Crop Yield Prediction Method. In Machine Learning and Information Processing”, Proceedings of ICMLIP, pp.457-467, 2020.
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  [18] Mamunur Rashid , Bifta Sama Bari , Yusri Yusup , Mohamad Anuar Kamaruddin , And Nuzhat Khan,“A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction”, IEEE Access date of current version Vol.9, 2021.
  [19] Abid Badshah , Basem Yousef Alkazemi , Fakhrud Din , Kamal Z. Zamli , Muhammad Hari,“Crop Classification and Yield Prediction Using Robust Machine Learning Models for Agricultural Sustainability”, IEEE Access, Vol.12, 2024 .
  [20] Nisha Thakur, Sanjeev Karmakar,“Deep Learning Approach using Long Short Term Memory Technique for Monthly Rainfall Prediction in Chhattisgarh, India", International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.1, pp.8-13, 2021.
  [21] Venkata Rama Rao Kolipaka, Anupama Namburu,“Crop Yield Prediction using Machine Learning and Deep Learning Techniques”, International Journal on Recent and Innovation Trends in Computing and Communication, ISSN:2321-8169 Vol. 11, Issue.10, 2023.
  [22] Leelavathi Kandasamy Subramaniam, Rajasenathipathi Marimuthu,“Crop Yield Prediction using Effective Deep Learning and Dimensionality Reduction Approaches for Indian Regional Crop”,E-Prime Advances in Electrical Engineering ,Electronics and Energy, Vol.8, 100611, 2024.
  [23] Thogaru Mallika, Selvani Deepthi Kavila, Balamurali Pydi, Budumuru Rajesh,“A Novel Method for Prediction of Crop Yield Using Deep Neural Networks”, International Journal of Intelligent Systems And Applications In Engineering, IJISAE, Vol.11, Issue.4, pp.308–315, 2023.

  Citation
  S. Ashok Kumar, S. Anusya, "Deep Neural Network and Multiparametric Deep Neural Networks for Harvest Predictions," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.6, pp.1-5, 2024

• Open Access   Article

  Real-Time Bus Occupancy Tracking With Nearby Stop Details and Schedules Displayed Online

  Vinothini S., Abinaya Shree R., Akshaya Devi S., Sandhiya Devi G.

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.6 , pp.6-13, Dec-2024

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  Abstract
  This FLEETSMART system is an innovative solution designed to accurately monitor and manage passenger flow on buses by counting the number of individuals boarding and exiting. Utilizing advanced sensors to the system provides real-time data on passenger boarding and exiting the bus. The system displays the current count on a LED display to show current passenger counts clearly and effectively. And additionally it features visible (flashing lights) and audible(buzzers) alerts to notify drivers and passengers when the bus reaches its maximum capacity to ensure safety and compliance with capacity regulations. By monitoring passenger numbers in real-time, the system helps to prevent overcrowding, reducing the risk of accidents and ensuring compliance with safety regulations. The system allows bus operators to make informed decisions regarding route management and capacity planning. Passengers benefit from a safer environment, knowing that their comfort and safety are prioritized. This innovative counting system represents a significant advancement in public transportation management. By leveraging cutting-edge technology in sensor integration, real-time data display, and alert mechanisms, it not only enhances safety but also improves operational efficiency. And also it displays the nearby stop and availability of the seats the website named as FLEETSMART. It helps passengers to find out the timing of the bus as well as schedules.

  Key-Words / Index Term
  fleet -vehicles that move together ; compliance - tractability ; leveraging - manipulating ; alert - a warning of possible danger ; prioritized - in order of importance ; buzzers - alarm bell gong signal warning

  References
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  Citation
  Vinothini S., Abinaya Shree R., Akshaya Devi S., Sandhiya Devi G., "Real-Time Bus Occupancy Tracking With Nearby Stop Details and Schedules Displayed Online," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.6, pp.6-13, 2024

• Open Access   Article

  A Reinforcement Learning-Based Multi-Agent System for Advanced Network Attack Prediction

  Stephen Kahara Wanjau, Stephen Njenga Thiiru

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.6 , pp.14-25, Dec-2024

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  This paper addresses the challenges of traditional Network Intrusion Detection Systems (NIDS) in handling the increasing complexity and volume of modern cyberattacks. The authors suggest a novel multi-agent deep reinforcement learning (MADRL) approach, employing a deep Q-network (DQN) architecture with convolutional and fully connected layers. This architecture incorporates Target networks and Experience Replay to enhance learning and adaptation. A hierarchical reinforcement learning strategy decomposes complex intrusion detection tasks into manageable subtasks, enabling efficient exploration of high-dimensional state-action spaces. The proposed model, trained and evaluated on the CICIDS2017 dataset using a 70% training set and 30% test split and 10-fold cross-validation, achieves exceptional performance. It attains 97.71% accuracy, 98.34% recall, 97.29% precision, and 96.76% F1-score after 50 iterations, surpassing existing NIDS solutions in comparative analysis. The model`s strength lies in its ability to effectively mimic environmental characteristics through multi-agent learning, leading to robust detection of intricate attack patterns. Furthermore, our approach demonstrates strong generalization capabilities on unseen data, indicating its potential for real-world deployment. This research contributes significantly to the evolution of intelligent network security systems by introducing an innovative MADRL framework. Future research directions include implementing the solution in real-time network environments, expanding the agent network, and extending the model`s application to outlier detection and software-defined networking. This work lays the foundation for future advancements in cyber threat detection and mitigation, paving the way for more robust and adaptive network security solutions.

  Key-Words / Index Term
  Network Intrusion Detection Systems, Multi-Agent Systems, Deep Reinforcement Learning, Deep Q-Network, Cybersecurity, Machine Learning

  References
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  Citation
  Stephen Kahara Wanjau, Stephen Njenga Thiiru, "A Reinforcement Learning-Based Multi-Agent System for Advanced Network Attack Prediction," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.6, pp.14-25, 2024

• Open Access   Article

  Securing the H.323 Protocol: A Steganographic and Hybrid Encryption Approach

  Most. Mazriha Akter Mohua

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.6 , pp.26-35, Dec-2024

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  Abstract
  An empirical investigation has established a novel approach for secure data transfer via the H.323 protocol by combining hybrid cryptography and real-time steganography. It has been found that the H.323 protocol vulnerabilities, such as vulnerability in NAT implementation in CISCO IOS, NAT slipstreaming, out-of-bounds memory write flaw, etc., allow attackers to send malformed H.323 traffic to execute a Denial of Service (DoS) or buffer overflow attack. This compromises security by causing resource exhaustion, session table consumption, and more. The proposed framework utilizes a hybrid cryptography algorithm that combines multi-layer symmetric encryption with the asymmetric Rivest-Shamir-Adleman (RSA) algorithm, and a steganography technique that embeds the encrypted data in the 3rd LSBs of the pixels of a real-time video stream. This allows for multilevel security for data transmitted via H.323. This study also outlines a mechanism for categorizing transmitted data into Unicode and Non-Unicode using machine learning algorithms to ensure data integrity. However, the study’s scope is limited to excluding alternative data transmission methods or protocols. A more comprehensive examination could provide a better understanding of how the proposed approach adapts to diverse data formats. Comprehensive experimental evaluations demonstrate that the hybrid algorithm optimizes both speed and memory usage compared with previous approaches. The proposed algorithm maintains the quality of the video stream. A low-latency connection and fast extraction with minimal distortion of the signal were feasible with increased Internet speed. By utilizing the machine learning model for error detection and increasing efficiency, the study achieves an accuracy of 99.21%. This research introduces a new framework to enhance privacy, circumvent attackers, increase payload capacity, and improve operating efficiency while reducing computing overhead. This makes it a practical solution for securely transferring data using the H.323 protocol.

  Key-Words / Index Term
  Steganography, H.323 Protocol, Multi-Layer Encryption, Hybrid Cryptography, Real-time Steganography, RSA, LSB, Video Stream

  References
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  Citation
  Most. Mazriha Akter Mohua, "Securing the H.323 Protocol: A Steganographic and Hybrid Encryption Approach," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.6, pp.26-35, 2024

• Open Access   Article

  Real-time Ad Click Prediction: Logistic Regression vs. Decision Tree

  Divyanshu Prajapati, Indranil Samanta, Amit Kumar, Pankaj Dumka

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.6 , pp.36-42, Dec-2024

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  Abstract
  This study studies the usefulness of different algorithms based on machine learning viz. Logistic Regression and Decision Tree, in forecasting the real-time ad clicks. Python has been used to implement and evaluate these models on a static dataset. Compared to Logistic Regression, the Decision Tree model has established better performance i.e. attaining an F1-score of 0.744 versus 0.7154. However, the Decision Tree`s performance might be affected by overfitting and dataset characteristics. Future research should explore joint methods, deep learning, addressing imbalanced data, and privacy-preserving techniques to improve ad click prediction models.

  Key-Words / Index Term
  Ad click prediction; Logistic regression; Linear regression; Decision tree; Python programming

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  Citation
  Divyanshu Prajapati, Indranil Samanta, Amit Kumar, Pankaj Dumka, "Real-time Ad Click Prediction: Logistic Regression vs. Decision Tree," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.6, pp.36-42, 2024

• Open Access   Article

  A CNN-Based Digraph Extraction Model for Enhanced Swahili Natural Language Processing

  Tirus Muya Maina, Aaron Mogeni Oirere, Stephen Kahara

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.6 , pp.43-55, Dec-2024

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  Abstract
  Swahili, a prominent language in East Africa, is integral to the region`s communication, commerce, and cultural exchange. Enhancing the accuracy of Swahili speech recognition systems is critical for improving accessibility, Transcription, linguistic translation, text analysis, speech recognition, sentiment analysis and aiding individuals with disabilities. However, the unique linguistic features of Swahili, particularly its digraphs, pose significant challenges to existing speech recognition technologies. This study addressed these challenges by introducing a novel approach for the extraction of Swahili digraphs from speech data. The research involved the extraction of a specialized Swahili digraph dataset and the design of an advanced Digraph Extraction Model. This model leverages Dense layer and Convolutional Neural Networks architecture to improve the precision and efficiency of Natural Language Processing tasks related to Swahili. Employing Design Science Research Methodology, the study systematically designs, implements, and evaluates the digraph extraction model. Results from the study demonstrate the model`s robust performance across several metrics. The low Mean Absolute Error and Root Mean Squared Error values indicate that the model is highly accurate, with predictions closely aligning with the actual values. Furthermore, the R-squared value of 0.89 demonstrates that the model effectively captures and this accounts for a substantial part of the variation in the dataset. The low-test loss suggests effective generalization to new, unseen data, affirming the model`s reliability for practical applications. This research significantly advances the field of Swahili speech recognition by enhancing accessibility and usability for Swahili speakers while supporting the preservation of the language`s oral traditions. The innovations introduced, including the annotated Swahili digraph corpus and the advanced Swahili Digraph Extraction Model, provide a substantial foundation for future research and development in Swahili digraph recognition technology. The model`s potential for successful deployment in real-world scenarios offers promising implications for improving Swahili language processing across various applications.

  Key-Words / Index Term
  Swahili Digraph Recognition, Digraph Extraction Model, Dense Neural Networks, Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Swahili Corpus

  References
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  Citation
  Tirus Muya Maina, Aaron Mogeni Oirere, Stephen Kahara, "A CNN-Based Digraph Extraction Model for Enhanced Swahili Natural Language Processing," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.6, pp.43-55, 2024

• Open Access   Article

  Optimizing Color Detection in Digital Images: A Machine Learning Approach Using K-Nearest Neighbor

  Most. Mazriha Akter Mohua

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.6 , pp.56-60, Dec-2024

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  Abstract
  Real-world problem-solving using machine learning algorithms, whether they are supervised or unsupervised, has become a significant issue. In this case, the system is trained with provided data to make predictions based on that data or previous experience. To recognize any object, color detection is necessary and machine learning has been proven benificial in this regard. However, color detection means identifying color name. This study utilizes the supervised ML algorithm K-Nearest Neighbor to optimize color detection in digital (RGB) images. This algorithm is used to separate different colors in the RGB images. This algorithm is beneficial for classification and regression (nonlinear) and provides advantages to some extent over other algorithms. A dataset containing 818 color names with their respective r, g, b values along with the intensity of each color has been trained with the KNN algorithm. By using the feature similarity of the nearest data points, it predicts the new data points` values. Initially, it calculates the distance between new and each training point, then selects the nearest points upon the value of K. The experimentation showed that a notable accuracy of 93.089% has been achieved with the optimal number of neighbors set at K=5. As computers can only track three basic colors (RGB), the OpenCV library is used to help detecting colors based on this fundamental color combination at different intensities.

  Key-Words / Index Term
  Color detection, Machine learning, KNN, Digital image, RGB, OpenCV

  References
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  Citation
  Most. Mazriha Akter Mohua, "Optimizing Color Detection in Digital Images: A Machine Learning Approach Using K-Nearest Neighbor," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.6, pp.56-60, 2024

• Open Access   Article

  Development of the Annotated Swahili Digraph Corpus Using a CNN-Based Digraph Extraction Model

  Tirus Muya Maina, Aaron Mogeni Oirere, Stephen Kahara

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.6 , pp.61-68, Dec-2024

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  Citation

  Abstract
  This study undertakes the development of the Annotated Swahili Digraph Corpus, utilizing a convolutional neural network-based model specifically designed for the extraction of digraphs. This initiative addresses a significant gap in the availability of dedicated digraph corpora for the Swahili language, which is increasingly needed for various applications in Natural Language Processing (NLP). The CNN-based model was accurately crafted to optimize the extraction and classification of digraphs, taking full advantage of the annotated features within the corpus. Digraphs are pairs of letters that create distinct sounds in a language, and Swahili`s linguistic structure presents unique challenges and requirements in this regard. Therefore, specialized tools and models are essential for ensuring accurate transcription and efficient speech recognition that cater specifically to the nuances of the Swahili language. The resulting Swahili Digraph Corpus comprises a comprehensive collection of 31,197 words, each systematically annotated to highlight their respective digraphs. Notably, this corpus features the nine key Swahili digraphs: "ch," "dh," "gh," "kh," "ng’," "ny," "sh," "th," and "ng." Furthermore, it includes annotations for vowel distribution, showcasing the core vowels "a," "e," "i," "o," and "u." This detailed annotated corpus supports a wide array of NLP applications, enabling researchers and developers to utilize accurate linguistic data for tasks such as text processing, machine translation, and speech synthesis. Through this dedicated effort, we aim to enhance the resources available for processing the Swahili language, ultimately contributing to its greater accessibility in the digital landscape.

  Key-Words / Index Term
  Annotated, Swahili, Digraph, Corpus, NLP, CNN, Dense layer

  References
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  Citation
  Tirus Muya Maina, Aaron Mogeni Oirere, Stephen Kahara, "Development of the Annotated Swahili Digraph Corpus Using a CNN-Based Digraph Extraction Model," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.6, pp.61-68, 2024

• Open Access   Article

  A Deep Learning-based Hybrid CNN-LSTM Model for Human Activity Recognition

  Damilola Akinola, Adenike Oluyemisi Oyedemi, Micheal Olalekan Ajinaja

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.6 , pp.69-75, Dec-2024

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Human Activity Recognition (HAR) involves identifying and classifying physical activities performed by individuals using data collected from sensors like accelerometers, gyroscopes, or cameras. HAR has broad applications in healthcare, fitness monitoring, and human-computer interaction, where accurate activity recognition can enhance user experiences and provide actionable insights. Despite progress in standalone deep learning models, limitations persist in capturing both spatial and temporal dependencies effectively. To address this, we developed a hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. CNNs were employed to extract spatial features from multivariate time-series sensor data, while LSTMs captured temporal dependencies crucial for accurate classification. The UCI HAR dataset, consisting of six activity labels, was used to benchmark the model. The implementation was carried out in Python, leveraging libraries like TensorFlow and Keras. The proposed hybrid CNN-LSTM model achieved an overall accuracy of 94%, with precision, recall, and F1-scores (macro and weighted averages) also reaching 94%. Individual activity labels recorded F1-scores ranging from 85% to 100%, demonstrating the model`s robustness across diverse activities. These findings validate the effectiveness of the hybrid CNN-LSTM architecture in overcoming the limitations of standalone models. The ability to capture both spatial and temporal patterns in sensor data underscores the model`s potential in advancing HAR applications. This study provides a foundation for future research in refining hybrid approaches, exploring additional datasets, and deploying such models in real-world applications. The results have significant implications for improving healthcare monitoring, fitness tracking, and human-computer interaction systems.

  Key-Words / Index Term
  human activity recognition, CNN-LSTM, hybrid deep learning, multivariate time-series, temporal dependencies, spatial feature extraction

  References
  [1] A. Ignatov. “Real-time human activity recognition from accelerometer data using Convolutional Neural Networks”. Applied Soft Computing, Issue 62, pp. 915-922, 2017. https://doi.org/10.1016/j.asoc.2017.09.027
  [2] A. Sharma, P. Singh, C. Madhu, N. Garg and G. Joshi, "Human Activity Recognition with smartphone sensors data using CNN-LSTM," 2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India, 2022, pp. 1-6, 2022. doi: 10.1109/IATMSI56455.2022.10119372.
  [3] L. Chen, X. Liu, L. Peng, and L. Wu. “Deep learning based multimodal complex human activity recognition using wearable devices.” Applied Intelligence Issue 51, pp. 4029–4042, 2021. https://doi.org/10.1007/s10489-020-02005-7
  [4] F.J. Ordóñez, and D. Roggen. “Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition”. Sensors, Vol. 16, Issue 1 pp. 1–25, 2016. https://doi.org/10.3390/s16010115
  [5] HAR dataset. Retrieved from https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones
  [6] C.A. Ronao, and S. Cho. “Human activity recognition with smartphone sensors using deep learning neural networks”. Expert Systems with Applications, Vol. 59, pp. 235-244, 2016. https://doi.org/10.1016/j.eswa.2016.04.032
  [7] M. Atikuzzaman, T. R. Rahman, E. Wazed, M. P. Hossain and M. Z. Islam, "Human Activity Recognition System from Different Poses with CNN," 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh, pp. 1-5, 2020 doi: 10.1109/STI50764.2020.9350508.
  [8] R. Maurya, T. H. Teo, S. H. Chua, H. -C. Chow and I. -C. Wey, "Complex Human Activities Recognition Based on High Performance 1D CNN Model," 2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), Penang, Malaysia, pp. 330-336, 2022. doi: 10.1109/MCSoC57363.2022.00059.
  [9] Lee, S., Yoon, S.M., & Cho, H. “Human activity recognition from accelerometer data using Convolutional Neural Network”. IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 131-134, 2017.
  [10] A. Gumaei, M. M. Hassan, A. Alelaiwi and H. Alsalman, "A Hybrid Deep Learning Model for Human Activity Recognition Using Multimodal Body Sensing Data," in IEEE Access, vol. 7, pp. 99152-99160, 2019, doi: 10.1109/ACCESS.2019.2927134.
  [11] W. Xu, Y. Pang, Y. Yang and Y. Liu, "Human Activity Recognition Based on Convolutional Neural Network," 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, pp. 165-170, 2018. doi: 10.1109/ICPR.2018.8545435.
  [12] Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. “Deep learning for sensor-based activity recognition: A survey.” Pattern Recognition Letters, Vol. 119, pp. 3-11, 2019. https://doi.org/10.1016/j.patrec.2018.02.010

  Citation
  Damilola Akinola, Adenike Oluyemisi Oyedemi, Micheal Olalekan Ajinaja, "A Deep Learning-based Hybrid CNN-LSTM Model for Human Activity Recognition," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.6, pp.69-75, 2024