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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Mihir Narayan Mohanty
Siksha ‘O’ Anusandhan (Deemed to be University)
Computer Science & IT

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Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning Satyaprakash Swain; Binod Kumar Pattnayak; Mihir Narayan Mohanty; Suvendra Kumar Jayasingh; Kumar Janardan Patra; Chittaranjan Panda
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1192-1203

Abstract

Cattle diseases can significantly impact on livestock health and agricultural productivity is substantial. Timely detection and prognosis of these diseases are essential for prompt interventions and preventing their spread within the herd. This study delved into employing machine learning models to anticipate cattle diseases based on relevant parameters. These parameters encompass milk fever, milk clots, milk watery, milk flake, blisters, lameness, stomach pain, gaseous stomach, dehydration, diarrhea, vomiting, abdominal issues, and alkalosis. A dataset of 2,000 samples from diverse cattle populations was amassed, each tagged with the presence or absence of specific diseases. The primary goal was to compare the efficacy of five well-known machine learning models: Naïve Bayes multinomial (NBM), lazy-IBk, partial tree (PART), random forest (RF), and support vector machine (SVM). The findings underscored the consistent superiority of RF in comparison to the other models, boasting the highest accuracy in predicting cattle diseases. The RF model exhibited an accuracy rate of 88% on the test dataset. This achievement can be ascribed to its capacity to handle intricate interactions among input features and mitigate over fitting through ensemble learning. These insights can furnish valuable information about early indicators and risk factors associated with diverse cattle diseases.
Inverse modeling of few-mode fiber for high-speed optical communication networks Bhagyalaxmi Behera; Shailendra Kumar Varshney; Mihir Narayan Mohanty
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 2: April 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i2.23773

Abstract

Few-mode fibers have been used in contemporary communication with mode multiplexing and space-division-multiplexing techniques to enhance the capacity crunch in recent communication links. The design parameters of the proposed fiber are predicted through machine learning-based inverse design approach, using regression model. The proposed few-mode fibre profile parameters are predicted with an accuracy of 99.95% to guide five to ten modes with weak coupling among the guided modes. In second phase of this work, the authors used a finite difference method-based solver to obtain the modal characteristics of the proposed fibre with predicted parameters for six guided modes, namely, LP01, LP11, LP21, LP31, LP41, and LP51. The numerical simulation results show that the predicted profile parameter maximizes effective mode-area and minimizes the inter-channel crosstalk for mode division multiplexing transmission over C-band. Besides this, the proposed ring-core few-mode fiber also exhibits nearly zero-dispersion for LP01 mode at 1550 nm along with low dispersion for other higher-order modes. Finally, an intensity-modulation and direct-detection mode multiplexed transmission link without erbium-doped fiber amplifier is established with six-spatial channels over 50 km and an attenuation of 0.18 dB/km to achieve minimum bit-error-rate of 4.45×10-9.
Co-Authors Bhagyalaxmi Behera Binod Kumar Pattnayak Chittaranjan Panda Kumar Janardan Patra Satyaprakash Swain Shailendra Kumar Varshney Suvendra Kumar Jayasingh