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• Open Access   Article

  Design Dreamer: ControlNet Based Generative AI Application for Interior Designing

  Param Dhingana, Vinamra Khandelwal, Sudeep Ganguly, Dipti Chauhan

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.3 , pp.1-7, Jun-2024

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  The emergence of state-of-the-art technologies, which powers the process of Generative AI, is now altering the interior design landscape. This study starts with Design Dreamer, the latest application among all, which gives users an opportunity to dream about and customize their living space in a remarkable way. Design Dreamer makes it possible to implement the most advanced systems in the ControlNet family that provide best-in-class immersive and interactive experience for the end users. Users share their room photos, give a purpose of their preferred design styles and room type then they get digital replays, which are AI-generated, and they are capable of visually reflecting what a person wants to see in their room. Our approach involves a multi-step procedure, uploading images, choosing style preferences, using the ControlNet Hough Model by calling through Replicate API, and efficiently retrieving the generated image when users pose a design. Key findings of our research demonstrate that Design Dreamer significantly enhances image quality and provides a unique output each time. The significance of this research lies in its potential to leverage Diffusion and ControlNet based models for their applications in Interior Design industry.

  Key-Words / Index Term
  Generative AI, Interior Design, State-of-The-Art Technology, Image-Processing, Text-to-Image Generation, Image-to-Image Generation, Image-to-Image Transformation, Stable-Diffusion, ControlNet, AI-Powered Design Tools, Diffusion Models

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  Citation
  Param Dhingana, Vinamra Khandelwal, Sudeep Ganguly, Dipti Chauhan, "Design Dreamer: ControlNet Based Generative AI Application for Interior Designing," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.3, pp.1-7, 2024

• Open Access   Article

  An Ensemble Learning Based Approach for Real-Time Face Mask Detection

  Manjunatha Guru V.G., Kamalesh V.N., Apoorva K.B.

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.3 , pp.8-13, Jun-2024

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  Face mask detection system is recently gained a wider interest from the computer vision research community after the COVID-19 outbreak. As highlighted in the literature, so far a considerably small amount of research is conducted to detect mask over face. Hence, our research contribution aims to a build a technique that can accurately detect mask over the face in public areas to restrict the spread of pandemic diseases. In this paper, the ensemble learning based technique is incorporated in order to effectively address the face mask detection problem. The combination of HOG (i.e. Histogram of Oriented Gradients) and Random Forest is newly explored in the face mask detection literature. Further, the experiments are conducted on our own dataset which is created by using freely available Google images. Also, the proposed approach is tested on RMFD dataset (i.e. Real-World Masked Face Dataset) for the effective comparative analysis. The experimental results have shown that our proposed approach is promising and effective in the face mask detection literature.

  Key-Words / Index Term
  Bagging, COVID-19, Ensemble, Face, HOG, Mask detection, Random Forest

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  Citation
  Manjunatha Guru V.G., Kamalesh V.N., Apoorva K.B., "An Ensemble Learning Based Approach for Real-Time Face Mask Detection," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.3, pp.8-13, 2024

• Open Access   Article

  Digital Agriculture, Agricultural Mechanization and Food Security in Nigeria

  Ale M.O., Oluwalami A.S.

  Review Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.3 , pp.14-20, Jun-2024

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  This paper highlights the crucial role of digital agriculture in agricultural mechanization for achieving food security in Nigeria amidst the challenges posed by a growing population, climate change, and declining agricultural productivity. The study explores the transformative potential of digitalization like precision agriculture, remote sensing, and data analytics in complimenting traditional agricultural practices. It also provides an overview of digital agriculture and agricultural mechanization as essential factors required for optimum food production to fight hunger and poverty in Nigeria. It explores the potential benefits and limitations of the use of digital technologies in precision farming. It also discusses the challenges facing the adoption of digital agriculture for the mechanization of agriculture in Nigeria. Among them are inadequate digital infrastructures, limited technical capacity and insufficient investment. Fostering a supportive policy environment and public private partnerships are recommended to promote the adoption of digital agriculture for sustainable agricultural mechanization to achieve food security and poverty alleviation in Nigeria.

  Key-Words / Index Term
  Agriculture, computer, electronics, Food, hunger, mechanization, poverty alleviation.

  References
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  Citation
  Ale M.O., Oluwalami A.S., "Digital Agriculture, Agricultural Mechanization and Food Security in Nigeria," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.3, pp.14-20, 2024

• Open Access   Article

  Convolutional Neural Network-Based Automated Acute Lymphoblastic Leukaemia Detection and Stage Classification from Peripheral Blood Smear Images

  Deepanshu, K. Srinivas, A. Charan Kumari

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.3 , pp.21-28, Jun-2024

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  Acute Lymphoblastic Leukaemia (ALL) poses significant challenges in diagnosis and treatment due to its high mortality rates and complex subtyping. In this study, ALLNet, a Convolutional Neural Networks (CNN) is employed to automate ALL detection using a publicly available dataset of microscopic peripheral blood smear (PBS) images. The ALLNet model demonstrates good performance, achieving an accuracy of 92% after 70 epochs of training. Through extensive evaluation using classification reports and confusion matrices, the model`s ability to differentiate between four classes of Benign, Early, Pre, and Pro have been analysed. The accuracy, recall, precision, and F1-score metrics for each class indicate robust performance, with particularly high values for the `Pro` class, suggesting the model`s efficacy in capturing nuanced patterns indicative of different leukaemia subtypes. Furthermore, the investigation highlights ALLNet‘s consistency across the dataset, effectively minimising both false positives and false negatives. These findings underscore the potential of CNNs in medical image analysis, particularly in the domain of leukaemia classification and detection.

  Key-Words / Index Term
  Medical Image Analysis, Acute Lymphoblastic Leukaemia, Convolutional Neural Network, Deep Learning, Blood Smear Images

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  [13] M. Shaheen, R. Khan, R. Biswal, “Acute myeloid leukaemia (AML) detection using AlexNet model,” Complexity, Vol.2021, 2021.
  [14] A. Rehman, N. Abbas, T. Saba, S. I. u. Rahman, Z. Mehmood, H. Kolivand, “Classification of acute lymphoblastic leukaemia using deep learning,” Microscopy Research and Technique, Vol.81, Issue. 11, pp.1310–1317, 2018.
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  Citation
  Deepanshu, K. Srinivas, A. Charan Kumari, "Convolutional Neural Network-Based Automated Acute Lymphoblastic Leukaemia Detection and Stage Classification from Peripheral Blood Smear Images," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.3, pp.21-28, 2024

• Open Access   Article

  Performance Evaluation of Proactive, Reactive, and Hybrid Routing Protocols for Low- and High-Density MANETs

  Gatete Marcel, Harubwira Flaubert

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.3 , pp.29-35, Jun-2024

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  Abstract
  Wireless ad hoc networks are becoming more and more popular every day. SENSOR, VANET, and MANET are a few examples of this kind of network. Various wireless network types are dynamic in nature and share distinct functionalities; however, determining which type of network to use based on user requirements is not always clear. It is also usually not clear which protocols are optimal in solving the routing issues that frequently arise in each kind of wireless network. To fill this gap, in this research work, we evaluate the aforementioned networks and prove that, in the end, MANET is the best choice because it can be readily deployed anywhere, at any time, to satisfy the needs of most users. We then conduct a performance evaluation of three popular routing protocol types in MANET namely AODV [Reactive], TORA [Hybrid], and DSDV [Proactive]. We use the well-known network evaluation parameter metrics i.e. throughput, average end-to-end delay, and packet delivery ratio for the above-mentioned comparative evaluation. The OPNET simulator was used to run the simulations. In terms of packet delivery ratio, end-to-end delay, and throughput, the findings obtained for both low- and high-density networks revealed that TORA performs better in networks with low node density while DSDV outperforms other protocols in networks with high node density.

  Key-Words / Index Term
  MANETs, Performance Analysis, Power-aware routing Protocols, Routing Metrics, and Simulation

  References
  [1] R.K. Jha, S. Limkar, U. Dalal, “A Performance Comparison of Routing Protocols (DSR and TORA) for Security Issue in MANET (Mobile Ad-Hoc Networks)”, JCA Special Issue on Mobile Ad -hoc Networks MANETs”, 2010.
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  [8] S. Giannoulis, C. Antonopoulos, E. Topalis, S. Koubias, “ZRP versus DSR and TORA: A comprehensive survey on ZRP performance”, Dept of Electrical and Computer Engineering, University of Patras Rion Campus, Patras, Greece, 2005.
  [9] M. Bouhorma, Bentaouit and Boudhir, “Performance Comparision of ad-hoc routing protocols AODV and DSR", Multimedia Computing and Systems, 2009. ICMCS`09 International Conference, pp.511-514, 2009.
  [10] Lordanakis et al., “Ad-hoc Routing Protocol for Aeronautical Mobile Ad-Hoc Networks”, In Proc. of CSNDSP, pp.543–547, 2006.
  [11] R. Sai and Deepa, "An Intelligent Traffic Management System in Vehicle-to-NH Road (V2N) using Dynamic Optimal Random Access (DORA) Protocol in comparison with Zone Routing Protocol (ZRP) to Improve Packet Delivery Ratio." 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). IEEE, 2023.
  [12] F. Rostand et al. "An In-Depth measurement analysis of 5G mmwave PHY latency and its impact on End-to-End delay." International Conference on Passive and Active Network Measurement. Cham: Springer Nature Switzerland, 2023.
  [13] MOHAN and D. Vimala, "Enhancing congestion control and QoS scheduling using novel rate aware-neuro-fuzzy algorithm in manet.", Journal of Theoretical and Applied Information Technology, 2023.

  Citation
  Gatete Marcel, Harubwira Flaubert, "Performance Evaluation of Proactive, Reactive, and Hybrid Routing Protocols for Low- and High-Density MANETs," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.3, pp.29-35, 2024

• Open Access   Article

  Effectiveness of Code Smells on Energy Profiling of Android Applications

  Kritika

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.3 , pp.36-46, Jun-2024

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  Abstract
  Bad coding techniques that go against basic design principles known as code smells, have a detrimental effect on the maintainability, performance, and resource utilization of software. The usefulness of code smells for Android application energy profiling is examined in this study. Five distinct code smells are examined, namely, the use of hash maps, internal getter/setter, slow loop, member ignoring method, and god class. Choosing an existing Android application corpus from GitHub, detecting code smells with tools like aDoctor and PMD, manually modifying the smells, and assessing the effect on energy usage with the Android Profiler are the steps in the research approach. As compared to the pre-refactored code, the results show that restructuring code smells improves energy efficiency by lowering memory utilisation and energy consumption levels. The research emphasises on of addressing code smells to optimize resource usage and enhance the overall performance of mobile applications.

  Key-Words / Index Term
  Code Smells, Android, Energy Consumption, Energy profiling, CPU profiling, Refactoring

  References
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  [13] J. Reimann, M. Brylski, and U. Aßmann, "A tool-supported quality smell catalogue for android developers," in Proc. Conf. Modellierung 2014 in the Workshop Modellbasierte und modellgetriebene Softwaremodernisierung–MMSM, vol. 2014.
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  [16] A. C. Bibiano et al., "A quantitative study on characteristics and effect of batch refactoring on code smells," in Proc. ACM/IEEE Int. Symp. Empirical Softw. Eng. Measurement (ESEM), pp. 1–11, 2019.
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  [27] M. Ghafari, O. Nierstrasz, and S. Demeyer, "Security smells in mobile apps: An empirical study," Inf. Softw. Technol., vol. 146, p. 107014, 2023.
  [28] H. Liu, J. Jin, Z. Xu, Y. Bu, Y. Zou, and L. Zhang, "Deep learning based code smell detection," IEEE Trans. Softw. Eng., vol. 45, no. 12, pp. 1191-1209, 2019.
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  Citation
  Kritika, "Effectiveness of Code Smells on Energy Profiling of Android Applications," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.3, pp.36-46, 2024

• Open Access   Article

  Enhancing Health Care Systems with Deep Learning-Based Hand Gesture Recognition

  Karthikraja D., Purushothaman N.

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.3 , pp.47-55, Jun-2024

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  Abstract
  Recent years have seen the integration of advanced technologies in healthcare, leading to innovative solutions for improving patient care and streamlining medical processes. One such innovation is the implementation of hand gesture recognition systems using deep learning. This paper explores the development and application of a hand gesture recognition system specifically designed for healthcare environments. Leveraging deep learning algorithms, the system accurately interprets hand gestures, enabling touchless interaction with medical devices and enhancing healthcare efficiency. Convolutional neural networks (CNNs) process and classify hand gestures captured through real-time video feeds. A comprehensive dataset of medical gestures was used to train the neural network, ensuring high precision and reliability. The system includes a robust preprocessing pipeline to handle variations in lighting, background, and hand orientation, improving performance in diverse clinical settings. Evaluation demonstrated significant accuracy in recognizing a wide range of gestures, outperforming traditional machine learning approaches. This touchless method reduces contamination risk in sterile environments and enhances accessibility for healthcare professionals, especially in critical situations. The successful integration of deep learning-based hand gesture recognition into healthcare systems represents a significant advancement in health technology, promising improved patient outcomes and streamlined processes.

  Key-Words / Index Term
  Deep Learning, Hand Gesture Recognition, Healthcare Systems, Convolutional Neural Networks (CNNs), Human-Computer Interaction, Clinical Environment Robustness

  References
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  [5] Z. Ren, J. Yuan, J. Meng, and Z. Zhang, "Robust Part-Based Hand Gesture Recognition Using Kinect Sensor," IEEE Transactions on Multimedia, Vol.15, Issue.5, pp.1110-1120, 2013.
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  [12] J. Han, G. Awad, and A. Sutherland, "Adaptive Background Modeling for Real-Time Hand Gesture Recognition," IEEE Transactions on Consumer Electronics, Vol.56, Issue.3, pp.963-970, 2010.
  [13] Z. Ren, J. Yuan, J. Meng, and Z. Zhang, "Robust Part-Based Hand Gesture Recognition Using Kinect Sensor," IEEE Transactions on Multimedia, Vol.15, Issue.5, pp.1110-1120, 2013.
  [14] K. Wachs, H. Kölsch, M. Stern, and R. Edlinger, "Touchless Interaction with Medical Devices Using Hand Gesture Recognition," IEEE Transactions on Biomedical Engineering, Vol.61, Issue.5, pp.1544-1555, 2014.
  [15] J. Han, G. Awad, and A. Sutherland, "Adaptive Background Modeling for Real-Time Hand Gesture Recognition," IEEE Transactions on Consumer Electronics, Vol.56, Issue.3, pp.963-970, 2010.
  [16] Ahmed, M. R. Amer, and M. A. Ghonemy, "Deep Convolutional Neural Networks for Gesture Recognition Using Kinect," IEEE Transactions on Computational Imaging, Vol.3, Issue.3, pp.345-358, 2017.
  [17] Y. Chen, Z. Yang, and G. Cheng, "Hand Gesture Recognition Using a Convolutional Neural Network Ensemble," IEEE Access, Vol.7, Issue.5, pp.28714-28723, 2019.
  [18] S.P. Akinrinwa, O. Olabode, O.C. Agbonifo, K.G. Akintola, "Breast Histopathology Images Multi-Classification using Ensemble of Deep Convolutional Neural Networks", International Journal of Scientific Research in Computer Science and Engineering, Vol.10, Issue.6, pp.9-21, 2022.
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  Citation
  Karthikraja D., Purushothaman N., "Enhancing Health Care Systems with Deep Learning-Based Hand Gesture Recognition," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.3, pp.47-55, 2024

• Open Access   Article

  Can We Trust Them? A Critical Evaluation of AI-Generated Content Detection Tools

  Sarthak Gupta

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.3 , pp.56-65, Jun-2024

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  Abstract
  This explosion of AI-written text raises the issues of credibility and academic integrity. AI content detectors can be seen as possible solutions, but the question of their effectiveness is still open. This research performs an in-depth analysis of AI content detector accuracy and flaws. We test an array of detectors against a variety of human-written and AI-generated samples that have been modified using various obfuscation methods and are of different text genres. Findings showed notable issues with the detectors concerning specific obfuscation techniques and diversity of styles. We talk about the ethical aspects of the detector, such as possible biases and the risk of incorrect allegations. Finally, we want to point out the necessity of continuous critical evaluation together with the development of other tools like promoting critical thinking and process-level assessment.

  Key-Words / Index Term
  AI-generated text, Content detection, Accuracy, Limitations, Academic integrity, Bias.

  References
  [1] S. Debora Weber-Wulff, A. Anohina-Naumeca, S. Bjelobaba, T. Foltýnek, J. Guerrero-Dib, O. Popoola, P. Šigut, and L. Waddington, “Testing of detection tools for AI-generated text,” International Journal for Educational Integrity, Vol.19, Issue.26, pp.1-39, 2023. https://doi.org/10.1007/s40979-023-00146-z
  [2] A. M. Elkhatat, K. Elsaid, and S. Almeer, “Testing of detection tools for AI-generated text,” International Journal for Educational Integrity, Vol.19, No.26, pp.1-39, 2023. https://doi.org/10.1007/s40979-023-00146-z
  [3] J. Otterbacher, “Why technical solutions for detecting AI-generated content in research and education are insufficient,” National Library Of Medicine, Vol.4, No.7, pp.1-9, 2023. https://doi.org/10.1016/j.patter.2023.100796
  [4] R. Sable, V. Baviskar, S. Gupta, D. Pagare, E. Kasliwal, D. Bhosale, and P. Jade, “AI Content Detection,” International Advanced Computing Conference, IACC, pp.267-283, 2024. https://doi.org/10.1007/978-3-031-56700-1_22
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  [7] F. Ufuk, H. Peker, E. Sagtas, and A. B. Yagci, “Distinguishing GPT-4-generated Radiology Abstracts from Original Abstracts: Performance of Blinded Human Observers and AI Content Detector,” pp.1-7, 2023. https://doi.org/10.1101/2023.04.28.23289283
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  [9] V. Bellini, F. Semeraro, J. Montomoli, M. Cascella, and E. Bignami, “Between human and AI: assessing the reliability of AI text detection tools,” Current Medical Research and Opinion, Vol.30, No.3, pp.353-358, 2023. https://doi.org/10.1080/03007995.2024.2310086
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  Citation
  Sarthak Gupta, "Can We Trust Them? A Critical Evaluation of AI-Generated Content Detection Tools," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.3, pp.56-65, 2024

• Open Access   Article

  Converging Pathways: Exploring the Integration of SDN and IoT for Enhanced Networking Capabilities

  Sowndharya, WinniDeLeo, Maheshwari, Jeeva

  Review Paper | Journal-Paper (IJSRCSE)

  Vol.12 , Issue.3 , pp.66-73, Jun-2024

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  Abstract
  The convergence of Software-Defined Networking (SDN) and the Internet of Things (IoT) represents a paradigm shift in network architecture, offering unprecedented opportunities for enhanced networking capabilities. This paper explores the integration of SDN and IoT technologies, investigating novel approaches to network management, resource optimization, and security. One of the key benefits of integrating SDN and IoT is enhanced network management capabilities. SDN controllers provide a centralized view of the network, allowing administrators to monitor and configure IoT devices from a single point of control. This centralized approach simplifies network management tasks, reduces operational overhead, and improves visibility into network traffic patterns. Additionally, SDN enables policy-based management, allowing administrators to enforce security policies and Quality of Service (QoS) requirements across the IoT ecosystem. Resource optimization is another area where the integration of SDN and IoT offers significant advantages. SDN controllers can intelligently allocate network resources based on the requirements of IoT applications, ensuring optimal performance and efficient utilization of bandwidth. By dynamically adjusting network configurations in response to changing traffic patterns, SDN enhances scalability and agility, enabling IoT deployments to scale seamlessly as demand fluctuates.

  Key-Words / Index Term
  Software-Defined Networking, Internet of Things, integration, network management, resource optimization, Quality of Service (QoS), scalability.

  References
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  Citation
  Sowndharya, WinniDeLeo, Maheshwari, Jeeva, "Converging Pathways: Exploring the Integration of SDN and IoT for Enhanced Networking Capabilities," International Journal of Scientific Research in Computer Science and Engineering, Vol.12, Issue.3, pp.66-73, 2024