Khongdet Phasinam
@psru.ac.th
Assistant Professor of Agricultural and Agro-Industry Engineering, Faculty of Food and Agricultural Technology
Pibulsongkram Rajabhat University
EDUCATION
Ph.D. (Agricultural and Food Engineering)
M.Eng. (Energy Management Engineering)
B.A. (Information Science)
B.Eng. (Agricultural Engineering)
RESEARCH INTERESTS
Measurement and Instrumentation in Agriculture, Machine Learning, Precision Agriculture, IoT
Scopus Publications
Scopus Publications
Samrat Ray, Khongdet Phasinam, and Ghada Elkady
Apple Academic Press
Kanokwan Promjeen, Thanwamas Phasinam, and Khongdet Phasinam
Learning Gate
Confectionery products, particularly popular among individuals under 17, include treats such as jellies and gummies. Traditionally, the manual process of dropping gummy jelly into molds, one piece at a time, is labor-intensive and time-consuming. This method is susceptible to the gummy jelly's physical properties, which can coagulate when exposed to air below its melting point. This study presents the development of a semi-automatic gummy jelly dropping machine to streamline the handmade confectionery production process. The machine, fabricated from stainless steel with dimensions of 36.00 centimeters (width) x 56.00 centimeters (height) x 47.00 centimeters (length), utilizes an aluminum piston mechanism (34 millimeters diameter and 142 millimeters length) for controlled gummy jelly dispensing. A 950-watt heater warms the liquid gummy jelly in a basin located at the piston's back, with tests conducted at temperatures of 80, 100, and 120 degrees Celsius. Results indicate that the optimal temperature for the semi-automatic gummy jelly dropping machine is 120 degrees Celsius, achieving an average dispensing time of 99.35 seconds. The machine's production capacity is 36.66 kilograms per hour, with a maximum efficacy of 73.75 percent. The produced gummy jelly exhibits a moisture content of 12.07 percent, lightness (L) of 52.63±4.67.63, red/green (a*) of 0.27±0.11, and yellow/blue (b*) of 4.08±3.13. Its hardness measures 1027.75 grams, its gumminess is 916.25 grams, and its water activity (aw) is 0.80. The study is significant since it verifies that there is no microbial development.
Thanwamas Kassanuk and Khongdet Phasinam
AIP Publishing
Sittichai Choosumrong, Rhutairat Hataitara, Gitsada Panumonwatee, Venkatesh Raghavan, Chatchawin Nualsri, Thanwamas Phasinam, and Khongdet Phasinam
Springer Science and Business Media LLC
Arodh Lal Karn, Sharnil Pandya, Abolfazl Mehbodniya, Farrukh Arslan, Dilip Kumar Sharma, Khongdet Phasinam, Muhammad Nauman Aftab, Regin Rajan, Ravi Kumar Bommisetti, and Sudhakar Sengan
Springer Science and Business Media LLC
Prashant Kumar Gangwar, Jifara Chimdi Bikila, Ravindra Pathak, G. Arul Jothi, Anupam Singh, and Khongdet Phasinam
AIP Publishing
Binaya Patnaik, Renu Mavi, Suraj Goswami, Pandurang Y. Patil, Khongdet Phasinam, and M. Z. M. Nomani
AIP Publishing
Makendran C., Binaya Patnaik, Nilofer Hussaini, Jifara Chimdi Bikila, Ravindra Pathak, and Khongdet Phasinam
AIP Publishing
Guna Sekhar Sajja, Kantilal Pitambar Rane, Khongdet Phasinam, Thanwamas Kassanuk, Ethelbert Okoronkwo, and P. Prabhu
Elsevier BV
Harikumar Pallathadka, Alex Wenda, Edwin Ramirez-Asís, Maximiliano Asís-López, Judith Flores-Albornoz, and Khongdet Phasinam
Elsevier BV
Edwin Hernan Ramirez-Asis, Miguel Angel Silva Zapata, A. R. Sivakumaran, Khongdet Phasinam, Abhay Chaturvedi, and R. Regin
Springer International Publishing
Thanwamas Kassanuk and Khongdet Phasinam
IGI Global
The cloud platform is becoming one of the fastest-rising environments in human activities, connecting the whole world in the upcoming decades. The three crucial aspects of cloud computing that enhance the quality of service are load balancing, task scheduling, and resource allocation. To address these issues, the research proposed dynamic degree balance with CPU_based VM allocation policy integrated with hybrid bird swarm optimization (BSO) and dragonfly algorithm (DA). The proposed algorithm focuses on improving the overall performance of the system by limiting DoI, execution time, and response time, while also maintaining system balance. In the CloudSim tool, D2B_CPU based BSO-DA is implemented and evaluated. The simulation results, on the other hand, show that the proposed BSO and DA-based load balancing scheme is significantly more effective in balancing load optimally among virtual machines more quickly than existing algorithms. The proposed method's efficiency is evaluated by comparing it to other existing techniques.
Dilip Kumar Sharma, A. Dharmaraj, Alim Al Ayub Ahmed, K. Suresh Kumar, Khongdet Phasinam, and Mohd Naved
Springer Nature Singapore
Ms. Gazala Masood, C. Indhumathi, Pacha. Malyadri, Krishna Mayi, B. K. Sumana, and Khongdet Phasinam
Springer Nature Singapore
Roy Setiawan, Vidya Sagar Ponnam, Sudhakar Sengan, Mamoona Anam, Chidambaram Subbiah, Khongdet Phasinam, Manikandan Vairaven, and Selvakumar Ponnusamy
Springer Science and Business Media LLC
Mamoona Anam, Roy Setiawan, Sathiya Kumar Chinnappan, Nik Alif Amri Nik Hashim, Abolfazl Mehbodniya, Cherry Bhargava, Pardeep Kumar Sharma, Khongdet Phasinam, V. Subramaniyaswamy, and Sudhakar Sengan
World Scientific Pub Co Pte Ltd
COVID-19 outbreaks are the critical challenge to the administrative units of all worldwide nations. India is also more concerned about monitoring the virus’s spread to control its growth rate by stringent behaviour. The present COVID-19 situation has huge impact in India, and the results of various preventive measures are discussed in this paper. This research presents different trends and patterns of data sources of States that suffered from the second wave of COVID-19 in India until 3rd July 2021. The data sources were collected from the Indian Ministry of Health and Family Welfare. This work reacts particularly to many research activities to discover the lockdown effects to control the virus through traditional methods to recover and safeguard the pandemic. The second wave caused more losses in the economy than the first wave and increased the death rate. To avoid this, various methods were developed to find infected cases during the regulated national lockdown, but the infected cases still harmed unregulated incidents. The COVID-19 forecasts were made on 3rd July 2021, using exponential simulation. This paper deals with the methods to control the second wave giving various analyses reports showing the impact of lockdown effects. This highly helps to safeguard from the spread of the future pandemic.
Khongdet Phasinam, Mohammed Usman, Sumona Bhattacharya, Thanwamas Kassanuk, and Korakod Tongkachok
Springer International Publishing
Mutyalaiah Paricherla, Sallagundla Babu, Khongdet Phasinam, Harikumar Pallathadka, Abu Sarwar Zamani, Vipul Narayan, Surendra Kumar Shukla, and Hussien Sobahi Mohammed
Hindawi Limited
An IoT system is a smart network that connects all items to the Internet and exchanges data using Internet Engineering Task Force established protocols. As a consequence, everything is instantly accessible from any place and at any time. The Internet of Things (IoT) network is built on the backbone of tiny sensors embedded in common objects. There is no need for human intervention in the interactions of IoT devices. The Internet of Things (IoT) security risk cannot be ignored. Untrusted networks, such as the Internet, are utilized to provide remote access to IoT devices. As a result, IoT systems are susceptible to a broad range of harmful activities, including cyberattacks. If security problems are not addressed, critical information may be hacked at any time. This article describes a feature selection and machine learning-based paradigm for improving security in the Internet of Things. Because network data are inherently abundant, it must be reduced in size before processing. Dimension reduction is the process of constructing a subset of an original data collection that removes superfluous content from the essential data set. Dimension reduction is a data mining approach. To minimize the number of dimensions in a dataset, linear discriminant analysis (LDA) is used. Following that, the data set with fewer dimensions is put into machine learning predictors as a training set. The effectiveness of machine learning approaches has been assessed using a range of criteria.
Khongdet Phasinam and Thanwamas Kassanuk
The Electrochemical Society
A human's ability to survive would be impossible without agriculture. Agriculture provides a means of subsistence for a huge proportion of the global population. It also provides a large number of work opportunities for the locals. Poor yields are a consequence of farmers' desire to rely on old-fashioned farming methods. The long-term development and success of the economy will continue to depend on agricultural and related industries. Crop monitoring and tracking, disease identification and management, and other such issues are among agriculture's most difficult difficulties. In many cases, smart farming is a viable answer. The Internet of Things (IoT) and machine learning methods has made it feasible to implement smart agriculture. This article presents a framework for real-time picture categorization in agriculture. Everything from IoT cameras to mobile applications to machine learning methods is featured. Arduino Uno, sensors, and Wi-Fi devices make up the hardware. Computers may "learn" from previous examples and detect patterns from noisy or complicated datasets using machine learning, a technique that uses a variety of statistical, probabilistic, and optimization methodologies to allow computers to "learn." Because of this, machine learning algorithms are increasingly being utilized to classify photos. Real-time photos linked to smart agriculture were classified using a combination of SVM, K-nearest neighbors, and probabilistic neural network classifiers. Real-time picture classification will assist in the diagnosis of leaf disease, the monitoring of farm staff, the classification of crops, and the tracking of farm product progress.
Guna Sekhar Sajjaa, Harikumar Pallathadka, Mohd Naved, and Khongdet Phasinam
The Electrochemical Society
Protecting networks and data requires an effective Intrusion Detection System (IDS). Contextual knowledge processing may be used to identify attacks that are specific to certain applications and networks, which is becoming more common due to the fast advancement of network technology. A hybrid intrusion detection system may help overcome this problem (IDS). Packet flooding is a common tactic employed in DoS attacks, which aims to overload the victim's infrastructure. It is now possible to interrupt networks of virtually any size with these kinds of assaults. Testing a high-performance hybrid IDS is hampered by having to deal with massive amounts of data with many characteristics. In order to identify dangerous patterns, a high number of features might make it harder, leading to a protracted training and testing procedure, an increased resource demand, and a lower detection rate. As a result, minimizing and deleting irrelevant features from the benchmark dataset is needed. Preprocessing removes out-of-range values, unlikely data combinations, and missing values from the dataset. Feature Selection (FS) methods are used to reduce the dimensionality of a dataset by deleting irrelevant and redundant attributes, thus improving classifier accuracy. To solve this problem, the AODV protocol, in conjunction with soft computing techniques, is very useful.
Harikumar Pallathadka, Mohd Naved, Khongdet Phasinam, and Myla M. Arcinas
The Electrochemical Society
Even in rural parts of many nations, coronary heart disease has emerged as the main cause of mortality. More than 23 million people will die from cardiovascular disease by 2030, according to the World Health Organization (WHO). With the use of cardiovascular disease prediction, healthcare practitioners may check the characteristics necessary for diagnosis, such as blood pressure and diabetes, which are vital. Many data mining methods are currently used in the medical industry, but additional research is needed to evaluate how well these categorization approaches function in real-world settings. The project's purpose is to quickly identify the best candidates for constructing heart disease prediction models, and to do it in a timely manner. The goal of this study is to increase the accuracy of cardiac disease prediction by addressing and overcoming the issues in the area (CVDs). CAD systems, which help physicians make choices, are often developed as a result of breakthroughs in machine learning technology. The categorization and prediction of cardiac disease are discussed in this article. The algorithms explored include ANN, KNN, and CNN. To conduct the evaluation, we utilised the UCI Cleveland database. For these algorithms, a thorough evaluation of the utility and consistency of data mining approaches found that CNNs performed best. Usefulness and consistency of data mining techniques for these algorithms revealed that the CNN was the most reliable process.