Volume-12 , Issue-2 , Apr 2024, ISSN 2321-3256 (Electronic Version)

• Open Access   Article

  Credit Card Fraud Identification Using Calibrated K nearest Neighbor

  Amit S. Vaishnav, Namrata S. Shroff

  Research Paper | Journal (IJSRNSC)

  Vol.12 , Issue.2 , pp.1-5, Apr-2024

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  Abstract
  Credit card fraud has become a significant concern in today`s digital world, leading to substantial financial losses for individuals and businesses alike. Detecting fraudulent transactions accurately and efficiently is crucial for maintaining the security of financial systems. The proposed method combines the power of KNN, a popular classification algorithm, with calibration techniques to enhance the fraud identification performance. Calibration is employed to adjust the probabilities assigned by the KNN algorithm, allowing for more accurate classification decisions and better control over the false positive rate. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a benchmark credit card fraud dataset. The results demonstrate that the calibrated KNN method outperforms the traditional KNN classifier in terms of both accuracy and other performance parameters. The calibrated KNN approach achieves higher fraud detection rates and produces well-calibrated probability estimates, reducing the risk of false alarms or missed fraud cases. This research contributes to the advancement of credit card fraud detection systems and provides valuable insights for financial institutions and individuals concerned with safeguarding against fraudulent activities.

  Key-Words / Index Term
  Calibrated KNN, KNN classifier, Fraud, Fraudulent and Credit card

  References
  [1] Srinivasa Rao Dammavalam, Md Mukheed, “Credit Card Fraud Detection Using Machine Learning”, International Journal of Advances in Engineering and Management, Vol.5, Issue.1, pp.147-154, 2023.
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  [9] Vaishnavi Nath Dornadula, S Geetha, "Credit Card Fraud Detection using Machine Learning Algorithms”, Elsevier, Vol.165, pp.631-641, 2019.
  [10] S P Maniraj, Aditya Saini, Swarna Deep Sarkar, Shadab Ahmed, “Credit Card Fraud Detection using Machine Learning and Data Science”, International Journal of Engineering Research & Technology, Vol.8, Issue.9, pp.110-115, 2019.
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  Citation
  Amit S. Vaishnav, Namrata S. Shroff, "Credit Card Fraud Identification Using Calibrated K nearest Neighbor," International Journal of Scientific Research in Network Security and Communication, Vol.12, Issue.2, pp.1-5, 2024

• Open Access   Article

  A Cloud-Based Machine Learning Approach for Blood Cell Classification using YOLOv5

  K. Krishna Jyothi, G. Kalyani, A. Sri Karan Chandra, Akhil Velati, C. Srujan

  Research Paper | Journal (IJSRNSC)

  Vol.12 , Issue.2 , pp.6-10, Apr-2024

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  Abstract
  Checking blood cell counts is crucial for diagnosing health issues. Traditionally, this involves manually counting cells under a microscope, a slow and tiring process. This research explores a new method using machine learning. A machine learning approach for automatic identification and counting of three types of blood cells using ‘you only look once’ (YOLO) object detection and classification algorithm. YOLO framework has been trained with a modified configuration BCCD Dataset of blood smear image to automatically identify and count red blood cells, white blood cells, and platelets. Moreover, this study with other convolutional neural network architectures considering architecture complexity, reported accuracy, and running time with this framework and compare the accuracy of the models for blood cells detection. Overall, the computer-aided system of detection and counting enables us to count blood cells from smear images in less than a second, which is useful for practical applications. Among the state-of-the-arts object detection algorithms such as regions with convolutional neural network (R-CNN), you only look once (YOLO), we chose YOLO framework which is about three times faster than Faster R-CNN with VGG-16 architecture. YOLO uses a single neural network to predict bounding boxes and class probabilities directly from the full image in one evaluation. We retrained YOLO framework to automatically identify and count RBCs, WBCs, and platelets from blood smear images. Also, the trained model has been tested with images from another dataset to observe the precision and accuracy to be around 95% with the recall-confidence to be 0.99.

  Key-Words / Index Term
  RBC; WBC; PLATELETS; CNN; SPPF

  References
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  [6] Maitra, Mausumi, Rahul Kumar Gupta, and Manali Mukherjee. "Detection and counting of red blood cells in blood cell images using Hough transform." International journal of computer applications , Vol.53, Issue.16, 2012.
  [7] Y. He, "Automatic Blood Cell Detection Based on Advanced YOLOv5s Network," in IEEE Access, vol.12, pp.17639-17650, 2024.
  [8] B. Venkatalakshmi and K. Thilagavathi, "Automatic red blood cell counting using hough transform," 2013 IEEE Conference on Information & Communication Technologies, Thuckalay, India, pp.267-271, 2013.
  [9] Wencheng Gu, Kexue Sun,AYOLOv5: “Improved YOLOv5 based on attention mechanism for blood cell detection”,Biomedical Signal Processing and Control, Vol.88, Part C, 2024.

  Citation
  K. Krishna Jyothi, G. Kalyani, A. Sri Karan Chandra, Akhil Velati, C. Srujan, "A Cloud-Based Machine Learning Approach for Blood Cell Classification using YOLOv5," International Journal of Scientific Research in Network Security and Communication, Vol.12, Issue.2, pp.6-10, 2024

• Open Access   Article

  A Review on Harmonizing Psychological Factors into Cyber Space

  Kritika

  Review Paper | Journal (IJSRNSC)

  Vol.12 , Issue.2 , pp.11-18, Apr-2024

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  Abstract
  The rapidly evolving digital environment has changed how people think, communicate, and behave, calling for an interdisciplinary understanding of how people interact with machines. This manuscript investigates the newly-emerging discipline of cyberpsychology, which combines sociology, technology, and psychology to study how individuals interact with the digital environment. The review follows the evolution of cyberpsychology through time, starting in the early days of the internet and ending in the present day with digital mental health interventions and widespread connectedness. The "digital modulation hypothesis," which contends that fundamental human abilities like emotional acuity, empathy, and impulse control continue to exist online but are expressed adaptively within the constraints and affordances of virtual environments, is the main thesis. The literature on subjects like moral reasoning, self-regulation in digital environments, and cyber-disinhibition is critically analysed in this work. It also emphasises how they interact. It also discusses issues like risk perception, emotional resilience, and algorithmic bias, highlighting the interaction between psychology and cybersecurity. The analysis concludes by outlining potential future paths for cyberpsychology, including the influence of new technologies, the development of digital health, and the moral use of behavioural data in cybersecurity.

  Key-Words / Index Term
  Cyberpsychology, digital modulation hypothesis, cyber security, emotional resilience, human-computer interaction, digital well-being

  References
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  Citation
  Kritika, "A Review on Harmonizing Psychological Factors into Cyber Space," International Journal of Scientific Research in Network Security and Communication, Vol.12, Issue.2, pp.11-18, 2024

• Open Access   Article

  HeathDetect7: Multiple Disease Identification Using Machine Learning

  A. Chandana, S. Sai Ashrith, R. Jayanth, M. Prashanth

  Research Paper | Journal (IJSRNSC)

  Vol.12 , Issue.2 , pp.19-26, Apr-2024

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  Abstract
  Numerous machine learning models in healthcare focus on single disease detection, yet there`s a growing need for systems that predict multiple diseases using a unified interface. This research addresses this gap by leveraging machine learning techniques to analyse diverse medical datasets and provide personalized risk assessments for diseases such as COVID-19, brain tumours, breast cancer, heart disease, diabetes, Alzheimer`s, and pneumonia. These diseases are causing many deaths globally, often due to the lack of timely check-ups and medical interventions. This problem is intensified by inadequate medical infrastructure and a low ratio of doctors to the population. By incorporating medical imaging data and clinical parameters, this study offers a comprehensive approach to disease identification, enabling early intervention and improved health outcomes. The project`s user-friendly interface allows individuals to input their medical information easily and receive timely assessments. Various classification algorithms, such as Random Forest, eXtreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNN), and Visual Geometry Group-16 (VGG-16), are explored to achieve accurate disease prediction. The ultimate goal is to create a web application that leverages machine learning to forecast several diseases, contributing to proactive healthcare management, and empowering individuals to monitor their health proactively and make informed decisions about their well-being.

  Key-Words / Index Term
  Unified interface, Personalized risk assessments, Clinical parameters, User-friendly interface, Proactive healthcare management, Informed decisions, Medical imaging data, Random Forest, XGBoost, CNN, VGG-16.

  References
  [1] Parshant, A. Rathee, “Multiple Disease Prediction Using Machine Learning”, Iconic Research and Engineering Journals (IRE), Vol.6, Issue.12, pp.411-415, 2023.
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  Citation
  A. Chandana, S. Sai Ashrith, R. Jayanth, M. Prashanth, "HeathDetect7: Multiple Disease Identification Using Machine Learning," International Journal of Scientific Research in Network Security and Communication, Vol.12, Issue.2, pp.19-26, 2024

• Open Access   Article

  Copy-Move Image Forgery Detection Using CNN and SIFT Algorithm

  S. Neha Nikhitha, R. Bhavya, K. Krishna Jyothi, G. Kalyani

  Research Paper | Journal (IJSRNSC)

  Vol.12 , Issue.2 , pp.27-30, Apr-2024

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  Abstract
  The widespread use of digital image alteration emphasizes how urgently reliable detection methods are needed to protect the originality and integrity of visual content. In this paper, we present an original technique for copy-move image forgery detection that combines Scale-Invariant Feature Transform (SIFT) with Convolutional Neural Networks . Our approach uses SIFT for reliable key-point descriptor extraction and Error Level Analysis (ELA) preprocessing to improve potentially changed regions. In parallel, a CNN model is trained using characteristics extracted from ELA representations to distinguish between modified and unmanipulated images. Although our hybrid methodology shows promising results in identifying copy-move forgeries, it is important to recognize the limits of current methods and systems.These drawbacks include the inability to grasp the results, scalability problems, dependency on handcrafted characteristics, computational complexity, limited generalization, partial copy-move vulnerabilities, and lack of interpretability. Our suggested method`s incorporation of SIFT is essential for identifying forgeries, especially in situations where copy-move manipulation is involved. By offering robust and unique descriptors that are independent of scale, rotation, and translation, SIFT features provide precise recognition of replicated areas in a picture. This method improves the model`s capacity to identify minute changes and visualise the location of forgery by utilizing SIFT in conjunction with CNN. This helps to maintain the visual authenticity and reliability of digital content.

  Key-Words / Index Term
  Copy-move,CNN,ELA,SIFT

  References
  [1] K.H. Rhee, “ Generation of Novelty Ground Truth Image Using Image Classification and Semantic Segmentation for Copy-Move Forgery Detection”, IEEE access Vol.10, pp.2783-2796, 2022.
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  Citation
  S. Neha Nikhitha, R. Bhavya, K. Krishna Jyothi, G. Kalyani, "Copy-Move Image Forgery Detection Using CNN and SIFT Algorithm," International Journal of Scientific Research in Network Security and Communication, Vol.12, Issue.2, pp.27-30, 2024