Recent Developments in Biopolymeric Biodegradable Mulching Film for Agriculture Applications – A Literature Review
This paper reviews the recent developments (last two decades) in biodegradable mulching film for agriculture applications in terms of their formulation development, process understanding, properties, field performance, and identifying gaps that earlier researchers did not do. Advancements in the method of application of such biodegradable mulch film on various vegetables and crops, problems of existing conventional mulch film, and new technological developments in terms of mulch film chemistry are briefly discussed. This review focuses on various testing methodologies and characterization techniques required for mulch film. Overall, the objective of this review is to provide insights into bio- polymeric mulching film formulation technologies which may guide future research toward sustainable agricultural bio-mulch film.
Published by: Vikas Bhausaheb Mhaske, Dr. Dilip D. Sarode
Author: Vikas Bhausaheb Mhaske
Paper ID: V8I2-1144
Paper Status: published
Published: March 7, 2022
Automated Soil Nutrients Monitoring and Irrigation using IoT
The process of cultivating crops along with livestock raising termed agriculture is one of the fields in this world that need more accurate and advanced technologization. Agriculture is the most crucial part of every individual’s daily life and has been in existence for thousands of years, and still isn’t at the expected peaks of technology. Making agriculture smart and advanced may upgrade the level of agricultural technologization to a certain peak. It will have positive ef ects on the yield of crops and also decrease the manual labor that is put into them. More accuracy and precision will be provided while farming and thus proving to be an ef icient way of farming. In earlier days, water level, humidity level, moisture condition, pH condition was not a great point of focus by the farmers and hence the produce was also not always up to the maximum possible. But as a researched fact, these above-mentioned condition plays a vital role in increasing productivity and healthy farming. IoT had played a role of importance in the development of smartness, accuracy, and precision in smart farming. And newly evolved technologies will also in support of IoT help take the advancing level of agriculture to its new heights. In this report, a model has been proposed that aims at smart farming with the help of a sensor network, which helps in detecting soil nutritional level with pH values, moisture of soil using moisture sensor, and temperature using suitable temperature sensor. Also, an automated irrigation system will be implemented using AWS cloud computation.
Published by: Devansh Choudhary, Devansh Kumar Singh, Arpit Jain, Dr. N. S. Raghava
Author: Devansh Choudhary
Paper ID: V8I1-1475
Paper Status: published
Published: March 2, 2022
Density Functional Theory (DFT) simulations on fullerene/polymer blends for organic photovoltaic systems
This research uses computational simulations using SIESTA (Spanish Initiative for Electronic Simulations with Thousands of Atoms) package based on Density Functional Theory (DFT) to explore the free energies, interaction energies, relative stability, stability of bonds, and charges transfer abilities of Organic Photovoltaic (OPV) systems comprising of bis[methano- fullerene (6,6)-phenyl-C61-butyric acid methyl ester] (bisPCBM) as the acceptor and eight different donor polymers. The novel donor polymers are computationally designed based on poly[4,8-bis-substituted-benzo [1,2-b:4,5-b’]dithiophene-2,6-diyl-alt-4-substituted-thieno[3, 4-b]thiophene-2,6-diyl] (PBDTTT) polymer, the main structural changes being the incorporation of Se and F atoms. From the results, it was clear that PCBM makes a wiser choice as the donor compared to bisPCBM for computer simulations. The influence of F atoms was observed in the stability of the system both energetically and structurally. The incorporation of Se negatively affects the feasibility of acceptor-donor interaction.
Published by: Rina Muhammad Faisal, Rohini De Silva, K. M. Nalin De Silva
Author: Rina Muhammad Faisal
Paper ID: V8I1-1466
Paper Status: published
Published: March 2, 2022
Transfer learning-based machine learning models for heart disease prediction in an earlier stage
Anticipating and identifying heart affliction has ordinarily been an intense and tedious task for specialists. To adapt to heart problems, medical clinics and explicit centers are giving steeply-evaluated rebuilding procedures and activities. Thus, hanging tight for a heart disorder in its initial degrees is most likely valuable to individuals from one side of the planet to the other, allowing them to take required treatment ahead of time before it transforms into genuine. Heart ailment has been the main issue in state-of-the-art years, with the essential intentions being unreasonable liquor use, tobacco use, and an absence of actual work. Machine gaining knowledge has proven to be beneficial in making selections and predictions from a huge set of information created via way of means of the healthcare enterprise over time. Artificial neural networks (ANN), choice trees (DT), random forests (RF), and Naive Bayes) are a number of the supervised gadget gaining knowledge of strategies hired on this prediction of coronary heart ailment (NB).In addition, the results of various algorithms are summarized. This paper attempts to forecast cardiac disease at an early stage. We will compare the four algorithms with their accuracy score and will conclude which algorithm is best.
Published by: Ganaga Muneeswari M., Soniya V., Aishwaryalakshmi R. K., Abisha D.
Author: Ganaga Muneeswari M.
Paper ID: V8I1-1473
Paper Status: published
Published: March 1, 2022
Clustering Algorithms and Classification Method for the Analysis of the Crop Yielding Dataset
Prediction of the crop is a dominant element of agriculture and even for farmers[1]. Currently, India is in the second position in the world for agricultural produce. The main economic sector of India is agriculture, which plays an important role in the growth of the economy[1]. Prediction of a crop is a challenging issue, so comprehensive varieties of crop prediction methods are used. In this paper the essential data is collected from districts of Karnataka and Tamilnadu with many parameters like year, district, area, temperature, rainfall, crop, and yield in tons for the year 2005 to 2016. For crop prediction, methods like K-Means and the J48 algorithm are applied in the existing system for clustering and classification. In the proposed system for clustering, the PAM (PartitionaroundMedoid) is applied and for classification, J48 is applied. A dataset is collected and clustered as stated by the attribute of the PAM algorithm by using the Euclidean formula, calculatingthe distance between the points. In this work tools like eclipse mars.2 and the programming language, Java is applied. The result acquired through an existing algorithm such as K-Means & J48 in comparison with a proposed algorithm like PAM and J48 gives better results in terms of accuracy and time complexity.
Published by: Vani Yelamali
Author: Vani Yelamali
Paper ID: V8I1-1439
Paper Status: published
Published: March 1, 2022
A critical review: On the design of microstrip patch antenna and analysis using real-time satellite data
Nowadays the study of microstrip patch antennas has a great impact on the communication systems of the recent world. If we compare these antennas with our conventional antennas, they we will see that microstrip patch antennas are far better than those conventional antennas which we used to use in our past years. In recent years we have developed many different types of microstrip patch antennas for different purposes. The main contribution of this work is studying different types of microstrip patch antennas and the advancement of technologies for future purposes. This study shall help you to understand different types of microstrip patch antennas and their current research.
Published by: Harshit Poddar
Author: Harshit Poddar
Paper ID: V8I1-1477
Paper Status: withdrawn
Submitted: March 1, 2022
