iosr-JCE   Volume-26 ~ Issue-1 ~ Jan. - Feb. 2024

Abstract: In Wireless Sensor Network, micro sensors operate collectively to gather data from environment and send to base station. Because of advancement in Wireless Sensor Network technology, these sensors can be used in a variety of applications, like Health Care Monitoring, Environment Sensing, Natural Disaster prevention and Chemicals sensing etc. Yet, one of the fundamental challenges for this technology, due to limited power energy of sensors autonomy, is to improve network lifetime. Different methods have been proposed......

Keywords: Clustering- Fuzzy-TOPSIS model- Multi Decision- Energy Lifetime

[1]. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E. A Survey On Sensor Networks. IEEE Communications Magazine. 2002a;40(8):102-114
[2]. C. Li, Y. Wnng, X. Guo, In 2010 Second International Conference On Multimedia And Information Technology, Vol. 2 (IEEE, 2010), Vol. 2, Pp. 89-92
[3]. I. L'hadi, M. Rifai, Y.S. Alj, In 2014 5th International Conference On Information And Communication Systems (ICICS) (IEEE, 2014), Pp. 1-6
[4]. S. Murugaanandam, V. Ganapathy, IEEE Access 7, 87357 (2019)
[5]. Aghdaie, M.H. (2015), "Target Market Selection Based On Market Segment Evaluation: A Multiple Attribute Decision Making Approach", International Journal Of Operational Research

Abstract: Security has become a major challenge with the significant increase in online and offline transactions; various financial fraud crimes occur daily. Fraud detection is a critical measure in today's digitalized world and on e-commerce platforms. Financial fraud has seriously affected the health of economies and damaged the welfare of consumers, investors, as well as financial institutions. Considering the significant advantages that Deep Learning (DL) methods bring to the world of machine learning, it seems necessary to examine their potential usage in the field of fraud detection. This is especially important for e-commerce transactions, where we need to assess whether DL methods.....

Key Word: Deep Learning; CNN; GoogLeNet; DenseNet; VGG, ResNet, Ensemble

[1]. Song Y., Escobar O., Arzubiaga U., And De Massis A. The Digital Transformation Of A Traditional Market Into An Entrepreneurial Ecosystem. Review Of Managerial Science. 2022; 16(1):65–88.
[2]. Rodrigues V. F., Policarpo L. M., Da Silveira D. E., Da Rosa Righi R., Da Costa C. A., Barbosa J. L. V., Antunes R. S., Scorsatto R., And Arcot T. Fraud Detection And Prevention In E-Commerce: A Systematic Literature Review. Electronic Commerce Research And Applications. 2022; 101207.
[3]. A. Darem, "Anti-Phishing Awareness Delivery Methods," Engineering, Technology & Applied Science Research. 2021; 11(6):7944–7949.
[4]. African Corporates Face Rising Cybercrime Risks. Emerald Expert Briefings. 2021; Https://Doi.Org/10.1108/Oxan-Db262652.
[5]. Best M., Krumov L., And Bacivarov I. Cyber Security In Banking Sector. International Journal Of Information Security And Cybercrime. 2019; 8(2): 39-52

Abstract: Background: Voice Similarity Analysis, is useful to identify and compare voices and is crucial in various applications, including speaker identification, music analysis, and surveillance. Traditional methods rely on features like pitch and formants, but they can be susceptible to variations in speaking style and environment. Spectrogram analysis offers a robust alternative by capturing the overall frequency distribution of the voice signal. Proposed a GUI application that utilizes cosine similarity on Mel-spectrograms to compare voice samples. User upload audio files, and the application extracts, resizes, and compares their spectrograms. A predefined similarity threshold determines whether......

Keywords: Mel-spectogram; Frequency; Libraries; Cosine Similarity, matplot lib; librosa; tkinter; Scikit-learn

[1]. Librosa: Mcfee, B., Raffel, C., Liang, D., Ellis, D. P., Mcvicar, M., & Battenberg, E. (2015). Librosa: Audio And Music Signal Analysis In Python. In Proceedings Of The 14th Python In Science Conference (Pp. 18-25).
[2]. Tkinter:Python Software Foundation. (2023). Tkinter Documentation. Retrieved From
Https://Docs.Python.Org/3/Library/Tkinter.Html
[3]. Matplotlib:Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing In Science & Engineering, 9(3), 90-95.
[4]. Numpy: Harris, C. R., Millman, K. J., Van Der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., ... & Oliphant, T. E. (2020). Array Programming With Numpy. Nature, 585(7825), 357-362.
[5]. Scikit-Learn (For Cosine_Similarity): Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Vanderplas, J. (2011). Scikit-Learn: Machine Learning In Python. Journal Of Machine Learning Research, 12(Oct), 2825-2830

Abstract: The agricultural output of the country is impacted by pest-infested plants and crops. Generally, in order to identify and detect diseases in plants, farmers or specialists keep a close watch on them. Generally speaking, this technique is costly, time-consuming, and imprecise. Identifying plant diseases may be aided by observing distinct indications or areas on the leaves of the diseased plant. Building a disease classification model supported by leaf image categorization is the aim of this study. Plant disease identification is accomplished through the use of Convolution Neural Network....

Keyword: Convolutional Neural Network (CNN), Plant Disease Identification, Automated Diagnosis

[1]. J. Koli, D.M. Vamsi, V.M. Manikandan, Plant Disease Detection Using Convolutional Neural Network, 2021 Ieee Bombay Signature Conference (Ibssc), Gwalior, India.
[2]. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi Fe. A Survey Of Deep Neural Network Architectures And Their Applications, Neurocomputing, Elsevier 2017.
[3]. L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Ye Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie & L. Farhan, Review Of Deep Learning: Concepts, Cnn Architectures, Challenges, Applications, Future Directions, Springer 2021.
[4]. Mingyuan Xin & Yong Wang, Research On Image Classification Model Based On Deep Convolution Neural Network, Springer 2019.
[5]. A.A.M. Al-Saffar, H. Tao, M.A. Talab, Review Of Deep Convolution Neural Network In Image Classification, International Conference On Radar, Antenna, Microwave, Electronics, And Telecommunications. Ieee, Jakarta, 2018

Abstract: A very important part of making sure food is safe around the world is rice, which is one of the most important main crops. Rice plants can get a lot of different diseases, and any one of them can severely lower the yield or grade of the crop. Early diagnosis of these disorders and proper classification are both important for the best management of these diseases. This review paper offers a comprehensive overview of recent advancements in rice disease detection methods, with a particular focus on the application of machine learning and deep learning techniques. The primary objective is to provide a consolidated summary of research findings, methodologies, and.....

Keywords: Machine learning, Rice Disease, CNN, Feature Extraction, Segmentation

[1]. Tian, L.; Xue, B.; Wang, Z.; Li, D.; Cheng, T. Spectroscopic Detection Of Rice Leaf Blast Infection From Asymptomatic To Mild Stages With Integrated Machine Learning And Feature Selection. Remote Sens. Environ. 2021, 257, 112350.
[2]. Becker-Ritt, A.B.; Carlini, C.R. Fungitoxic And Insecticidal Plant Polypeptides. Pept. Sci. 2012, 98, 367–384.
[3]. Mondal, K.; Mani, V. Emergence Of Bacterial Panicle Blight Caused By Burkholderia Glumae In North India. Plant Dis. 2015, 15, 150311115126000.
[4]. Karthikeyan, I. A Review Of Remote Sensing Applications In Agriculture For Food Security: Crop Growth And Yield, Irrigation, And Crop Losses. J. Hydrol. 2020, 586, 124905.
[5]. Jiang, F.; Lu, Y.; Chen, Y.; Cai, D.; Li, G. Image Recognition Of Four Rice Leaf Diseases Based On Deep Learning And Support Vector Machine. Comput. Electron. Agric. 2020, 179, 105824

Abstract: this paper provides an overview of deep learning-based methods for enhancing low-resolution images. We explore the evolution of deep learning techniques in this context, from early approaches like CNN to state-of-the-art architectures. Deep learning models, particularly convolutional neural networks (CNNs), have demonstrated remarkable success in up scaling low-resolution images, effectively capturing intricate patterns and textures. We discuss the underlying principles of these DL-based approaches, highlighting their ability to leverage contextual information and learn complex image representations. Additionally, we delve into the various loss functions....

Keywords: image super-resolution; deep learning; remote sensing; model design; evaluation methods

[1]. Wang, Z.H.; Chen, J.; Hoi, S.C.H. Deep Learning For Image Super-Resolution: A Survey. Ieee Trans. Pattern Anal. Mach. Intell. 2021, 43, 3365–3387.
[2]. Chen, H.G.; He, X.H.; Qing, L.B.; Wu, Y.Y.; Ren, C.; Sheriff, R.E.; Zhu, C. Real-World Single Image Super-Resolution: A Brief Review. Inf. Fusion 2022, 79, 124–145.
[3]. Yang, W.M.; Zhang, X.C.; Tian, Y.P.; Wang, W.; Xue, J.H.; Liao, Q.M. Deep Learning For Single Image Super-Resolution: A Brief Review. Ieee Trans. Multimed. 2019, 21, 3106–3121.
[4]. Zhang, N.; Wang, Y.C.; Zhang, X.; Xu, D.D.; Wang, X.D.; Ben, G.L.; Zhao, Z.K.; Li, Z. A Multi-Degradation Aided Method For Unsupervised Remote Sensing Image Super Resolution With Convolution Neural Networks. Ieee Trans. Geosci. Remote Sens. 2022, 60, 5600814.
[5]. Xiang, P.; Ali, S.; Jung, S.K.; Zhou, H.X. Hyperspectral Anomaly Detection With Guided Autoencoder. Ieee Trans. Geosci. Remote Sens. 2022, 60, 5538818.

Abstract: Steganography, as one of the fundamental approaches, conceals data within other seemingly innocuous data or media to obscure its presence from unauthorized individuals. This technique stands in contrast to encryption, which relies on mathematical algorithms to encode information, making it indecipherable without the appropriate decryption key. While both approaches serve the overarching goal of information security, they differ fundamentally in their methodologies. In the realm of steganography, researchers have explored various techniques and methodologies, aiming to enhance the effectiveness and security of data concealment. These techniques encompass......

Keywords –Steganography, deep learning, machine learning, encryption and decryption

[1]. Cheddad, A.; Condell, J.; Curran, K.; Mc Kevitt, P. Digital Image Steganography: Survey And Analysis Of Current Methods. Signal. Process. 2010, 90, 727–752.
[2]. Anderson, R.; Petitcolas, F. On The Limits Of Steganography. Ieee J. Sel. Areas Commun. 1998, 16, 474–481
[3]. Srikumar, R.; Malarvizhi, C.S. Strong Encryption Using Steganography And Digital Watermarking. In Proceedings Of The 22nd Picture Coding Symposium, Seoul, Korea, 25–27 April 2001; Pp. 425–428.
[4]. Al-Daweri, M.S.; Abdullah, S.; Ariffin, K.A.Z. A Homogeneous Ensemble Based Dynamic Artificial Neural Network For Solving The Intrusion Detection Problem. Int. J. Crit. Infrastructure. Prot. 2021, 34, 100449.
[5]. Majeed, M.A.; Sulaiman, R. An Improved Lsb Image Steganography Technique Using Bit-Inverse In 24 Bit Colour Image. J. Theor. Appl. Inf. Technol. 2015, 80, 2.

Abstract: The integration of ChatGPT, a state-of-the-art natural language processing model, presents a transformative opportunity to enhance tutoring systems and personalized learning experiences within the realm of education. This abstract explores the diverse applications of ChatGPT in educational settings, focusing on its potential to augment traditional tutoring methodologies and tailor learning experiences to individual student needs. By leveraging ChatGPT's natural language understanding capabilities, tutoring systems can engage students in interactive dialogues, provide real-time feedback, and offer personalized learning paths tailored to each learner's strengths, weaknesses, and learning preferences......

Keywords –ChatGPT; Education; Tutoring; Personalized Learning; Natural Language Processing

[1]. Adıgüzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology. [2]. Albdrani, R. N., & Al-Shargabi, A. A. Investigating the Effectiveness of ChatGPT for Providing Personalized Learning Experience: A Case Study.
[3]. Brown, D., & Jones, E. (2023). Leveraging ChatGPT for personalized tutoring in educational settings. Journal of Educational Technology, 10(2), 45-67.
[4]. Chen, L., & Wang, Q. (2023). Leveraging ChatGPT for personalized learning experiences in online education platforms. Computers in Human Behavior, 25(4), 67-82.
[5]. Garcia, M., & Martinez, L. (2024). ChatGPT: Transforming tutoring systems in higher education. Journal of Higher Education, 32(1), 56-78.