Volume-2 | Issue-1 [January to June]
Supervised Probabilistic Approach for Drug Target Prediction
Renato R. Maaliw III Haewon Byeon Suchitra Bala
[1] Southern Luzon State University, Lucban, Quezon, Philippines
[2] Department of Digital Anti-Aging Healthcare, Inje University, Gimhae, 50834, Republic of Korea
[3] ICT & Cognitive Systems, Sri Krishna Arts and Science College, Tamil Nadu, India
Correspondence should be addressed to Renato R. Maaliw III; rmaaliw@slsu.edu.ph
Abstract
Bayesian ranking based drug-target relationship prediction has achieved good results, but it ignores the relationship between drugs of the same target, which affects the accuracy. Aiming at this problem, a new method is proposed—drug-target relationship prediction based on grouped Bayesian ranking. According to the reality that the drugs interacting with a specific target have similarities, a grouping strategy is introduced to make these similar drugs interact. A theoretical model based on the grouping strategy is derived. The method is compared with five typical methods on five publicly available datasets and produces results superior to the compared methods.
IEEE : R. R. Maaliw III, H. Byeon, and S. Bala, “Supervised Probabilistic Approach for Drug Target Prediction,” Journal of Engineering, Science and Mathematics, vol. 2, no. 1, pp. 57–67, 2023, [Online]. Available: https://jesm.in/wp-content/uploads/2023/08/Supervised-Probabilistic-Approach-for-Drug-Target-Prediction.pdf
APA : Maaliw III, Byeon, & Bala. (2023). Supervised Probabilistic Approach for Drug Target Prediction. Journal of Engineering, Science and Mathematics, 2(1), 57–67. https://jesm.in/wp-content/uploads/2023/08/Supervised-Probabilistic-Approach-for-Drug-Target-Prediction.pdf
Ensuring Food Security and Agriculture Demand using Machine Learning and Sensor based Technologies
Rijwan Khan Santosh Kumar Ankur Seem Arpit Kumar Chauhan Anubhav Gupta
[1],[3],[4],[5] ABES Institute of Technology, Ghaziabad, Affiliated to AKTU Lucknow, India
[2] University of Dar Es Saalam, Tanzania
Correspondence should be addressed to Santosh Kumar; drsengar2002@gmail.com
Abstract
Agriculture is important for sustaining human life. Contribution of agriculture to the Indian economy is around 20%. It is estimated that by the year 2050, Indian will need 60% more food to feed the population of 9.3 billion. However, due to limited resources and land, there is a challenge of food insecurity, need to enhance the efficiency of current farms, plan, smartly grow crops according to the demand to decrease wastage and ensure food security. Technologies like Machine Learning, IoT, Blockchain, Data Analytics, Big Data, and Cloud Computing hold the key to solving this problem. With these technologies, the authors can analyze the real-time and past data of agriculture and make the best decision for problems like crop selection, demand prediction, weather prediction, and many more. Machine Learning algorithms use data and by doing complex computation, try to give an accurate result. In this paper, authors review different research done in the field of food security agriculture using ML (Machine Learning) by using past data and avoiding live data, which makes the model more affordable by decreasing the cost of IoT devices needed for live data.
IEEE : R. Khan, S. Kumar, A. Seem, A. K. Chauhan, and A. Gupta, “Ensuring Food Security and Agriculture Demand using Machine Learning and Sensor based Technologies ,” Journal of Engineering, Science and Mathematics, vol. 2, no. 1, pp. 40–56, 2023, [Online]. Available: https://jesm.in/wp-content/uploads/2023/08/Ensuring-Food-Security-and-Agriculture-Demand-using-Machine-Learning-and-Sensor-based-Technologies.pdf
APA : Khan, Kumar, Seem, Chauhan, & Gupta. (2023). Ensuring Food Security and Agriculture Demand using Machine Learning and Sensor based Technologies . Journal of Engineering, Science and Mathematics, 2(1), 40–56. https://jesm.in/wp-content/uploads/2023/08/Ensuring-Food-Security-and-Agriculture-Demand-using-Machine-Learning-and-Sensor-based-Technologies.pdf
Nanoparticles of Ag-Doped ZnO for Ethanol Gas Sensing
Nancy Mahendru Neha Verma Rahul Sharma
[1] Department of Physics, Sikh National Collage Banga, Punjab
[2] Department of Physics, KRM DVA Collage Nakodar, Punjab
[3] Department of Computer Science and Engineering, Lovely Professional University Phagwara, India
Correspondence should be addressed to Neha Verma; nv0027@gmail.com
Abstract
Nanoparticles of Ag-doped ZnO have been synthesized, characterized, and used to detect ethanol gas. Hydrothermal synthesis and several characterization methods were used to produce the nanoparticles. Nanoparticles created using Ag-doped ZnO have an average diameter of 217nm, are well-crystalline, and display excellent optical characteristics, according to the comprehensive characterizations. An effective ethanol gas sensor was built using the nanoparticles that had been manufactured using Ag-doped ZnO nanoparticles. At 3500C, the recorded gas response for 200 ppm of ethanol gas was 35.815, according to the thorough gas sensing studies.
IEEE : N. Mahendru, N. Verma, and R. Sharma, “Nanoparticles of Ag-Doped ZnO for Ethanol Gas Sensing,” Journal of Engineering, Science and Mathematics, vol. 2, no. 1, pp. 32–39, 2023, [Online]. Available: https://jesm.in/wp-content/uploads/2023/08/Nanoparticles-of-Ag-Doped-ZnO-for-Ethanol-Gas-Sensing.pdf
APA : Mahendru, Verma, & Sharma. (2023). Nanoparticles of Ag-Doped ZnO for Ethanol Gas Sensing. Journal of Engineering, Science and Mathematics, 2(1), 32–39. https://jesm.in/wp-content/uploads/2023/08/Nanoparticles-of-Ag-Doped-ZnO-for-Ethanol-Gas-Sensing.pdf
CNN Based Self Attention Mechanism for Cross Model receipt Generation for Food Industry
Ismail Keshta Mukesh Soni
[1] Computer Science and Information Systems Department, College of Applied Sciences, Al Maarefa University, Riyadh, Saudi Arabia
[2] Department of CSE, University Centre for Research & Development, Chandigarh University, Mohali, Punjab-140413, India
Correspondence should be addressed to Ismail Keshta; imohamed@mcst.edu.sa
Abstract
Diet management requires keeping track of what you eat. The researchers presented a recipe retrieval technique based on food photos that retrieves the related recipes from the taken images and creates nutritional information accordingly, making recording more convenient. The retrieval of recipes is an example of a cross-modal retrieval challenge. Still, as compared to other challenges, the main challenge is that recipes explain a succession of modifications from raw ingredients to completed goods rather than immediately apparent characteristics. As a result, the model must have a thorough understanding of the raw materials processing process. Current recipe retrieval research, on the other hand, uses a linear approach to text processing, which makes it difficult to capture long-range relationships during recipe processing. A cross-modal recipe retrieval model that is based on the self-attention mechanism is currently being developed in order to overcome this difficulty. The model makes use of the Transformer model’s self-attention mechanism to effectively capture long-distance interactions in recipes. Additionally, the model improves upon the attention mechanisms of prior techniques in order to mine the semantics of recipes more effectively. The approach enhances the recall rate of the recipe retrieval task by 22% over the baseline strategy, according to experimental data.
IEEE : I. Keshta and M. Soni, “CNN Based Self Attention Mechanism for Cross Model receipt Generation for Food Industry,” Journal of Engineering, Science and Mathematics, vol. 2, no. 1, pp. 11–31, 2023, [Online]. Available: https://jesm.in/wp-content/uploads/2023/08/CNN-Based-Self-Attention-Mechanism-for-Cross-Model-receipt-Generation-for-Food-Industry.pdf
APA : Keshta, & Soni. (2023). CNN Based Self Attention Mechanism for Cross Model receipt Generation for Food Industry. Journal of Engineering, Science and Mathematics, 2(1), 11–31. https://jesm.in/wp-content/uploads/2023/08/CNN-Based-Self-Attention-Mechanism-for-Cross-Model-receipt-Generation-for-Food-Industry.pdf
Applying Machine Learning Algorithms to Analyse Parkinson’s Disease in the Age Group of 50+
Rijwan Khan Mansi Gupta Khushi Patel Kashish Gupta
[1][2],[3],[4] Department of Computer Science and Engineering, ABES institute of technology, Ghaziabad, India
Correspondence should be addressed to Rijwan Khan; rijwankhan786@gmail.com
Abstract
The use of machine learning techniques in telemedicine to identify Parkinson’s disease (PD) in its early stages is explored in this paper. PD is a neurodegenerative condition that primarily affects older people. Early detection is essential for effective management and treatment, but for patients, physical visits can be difficult due to mobility and communication issues. The study used the MDVP (Multidimensional Voice Program) audio data from thirty PD patients and healthy individuals to train four classification results from Support Vector Machine (SVM), Random Forest, K-Nearest Neighbour (KNN), Logistic Regression, AdaBoost classifier, Decision tree classifiers, and stacking classifier of ensemble learning technique were compared. On balanced data, the stacking classifier ensemble learning technique has 97% detection accuracy. Furthermore, these outcomes outperform recent literature-based research. The most reliable Machine Learning (ML) method for the detection of Parkinson’s disease was discovered
IEEE : R. Khan, M. Gupta, K. Patel, and K. Gupta, “Applying Machine Learning Algorithms to Analyse Parkinson’s Disease in the Age Group of 50+,” Journal of Engineering, Science and Mathematics, vol. 2, no. 1, pp. 1–10, 2023, [Online]. Available: https://jesm.in/wp-content/uploads/2023/08/Applying-Machine-Learning-Algorithms-to-Analyse-Parkinsons-Disease-in-the-Age-Group-of-50.pdf
APA : Khan, Gupta, Patel, & Gupta. (2023). Applying Machine Learning Algorithms to Analyse Parkinson’s Disease in the Age Group of 50+. Journal of Engineering, Science and Mathematics, 2(1), 1–10. https://jesm.in/wp-content/uploads/2023/08/Applying-Machine-Learning-Algorithms-to-Analyse-Parkinsons-Disease-in-the-Age-Group-of-50.pdf