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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Theerthagiri, Prasannavenkatesan
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Computer Science & IT Engineering

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Review of image processing and artificial intelligence methodologies for apple leaf disease diagnosis Tabassum, Husna; Theerthagiri, Prasannavenkatesan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2459-2471

Abstract

Apple leaf disease (ALD) potentially affects the apple tree's health by reducing fruit yield and its capability to grow healthy. The prime purpose of the proposed study is to review and assess the strengths and weaknesses associated with the frequently exercised methods of ALD diagnosis using image processing and artificial intelligence (AI). Although these are widely adopted in recent studies, the core notion is to find the pros and cons associated with the practical viability. A desk research methodology is undertaken to carry out proposed review work where a database of recent scientific manuscripts is collected and studied very closely. The existing approaches are reviewed concerning identified problems, adopted solutions, advantages, and limitations. Finally, the paper contributes towards offering insight into potential research gap which will guide the upcoming researchers to make wise decisions for planning their models. The results acquired from this review work show that generalized challenges of ALD are not addressed, less emphasis on illumination variability, reduced target to minimize complexity, lesser evidence towards real-time processing, no evidence towards interpretability, limitation of available dataset, and tradeoff-between image processing and AI.
Hybrid intrusion detection model for hierarchical wireless sensor network using federated learning Mani, Sathishkumar; Kishoreraja, Parasuram Chandrasekaran; Joseph, Christeena; Manoharan, Reji; Theerthagiri, Prasannavenkatesan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp492-499

Abstract

The applications of wireless sensor networks are vast and popular in today’s technology world. These networks consist of small, independent sensors that are capable of measuring various physical quantities. Deployment of wireless sensor networks increased due to immense applications which are susceptible to different types of attacks in an unprotected and open region. Intrusion detection systems (IDS) play a vital part in any secured environment for any network. IDS using federated learning have the potential to achieve better classification accuracy. Usually, all the data is stored in centralized server in order to communicate between the systems. On the other hand, federated learning is a distributed learning technique that does not transfer data but trains models locally and transfers the parameters to the centralized server. The proposed research uses a hybrid IDS for wireless sensor networks using federating learning. The detection takes place in real-time through detailed analysis of attacks at different levels in a decentralized manner. Hybrid IDS are designed for node level, cluster level and the base station where federated learning acts as a client and aggregated server.
Co-Authors Joseph, Christeena Kishoreraja, Parasuram Chandrasekaran Mani, Sathishkumar Manoharan, Reji Tabassum, Husna