BIOGRAPHY

Muhamad Firdaus Syahmi bin Sam-On is a dedicated and accomplished Senior Lecturer specializing in Microbiology, with a profound passion for research and academic excellence. He obtained his Bachelor of Science in Microbiology in 2020 and subsequently finished a PhD in the same field from Universiti Putra Malaysia in 2023. Additionally, his expertise in farming and biotechnology has equipped him with valuable skills in pests and pathogens management, laboratory protocols, and problem-solving. Throughout his academic journey, Muhamad Firdaus Syahmi has exhibited a keen interest in exploring microbial pathogens and their potential applications in various fields, particularly in aquaculture and food safety. His doctoral research focused on evaluating the efficacy of Bacillus spp. as probiotics against pathogens causing Vibriosis and Aeromonosis, which culminated in several high-impact publications in esteemed journals like Microbial Pathogenesis and Food Bioscience. Serving as a Senior Lecturer at Universiti Kebangsaan Malaysia, Muhamad Firdaus Syahmi continues to improve his passion for teaching and research. His interdisciplinary approach and innovative methodologies have earned him recognition in the field of microbiology, and his target is to make a lasting impact on scientific knowledge and understanding. He is fluent in both Malay and English languages and always eager to learn more from his colleagues and peers in the industry. Please feel free to contact Muhamad Firdaus Syahmi Sam-on on this platform for any comments or questions related to microbiology and biotechnology.

Abstract:

Technological advancement has transformed the lives of people in every aspect. The disruption can be reflected in the employment relationship between employer and employee in digital labour platform. Digital labour platform has existed before the COVID-19 pandemic and continues to evolve in the world of work. Due to the flexibility offered in digital labour platform, it has attracted workers to work in digital labour platform, predominantly, young workers. However, the lack of labour rights and social security protection for workers in digital labour platform has withdrawn the young workers from the labour market. This study aims to investigate the factors influencing young worker to work in digital labour platforms and secondly, to examine the legal framework on labour rights and social security protection for young workers working in digital labour platform. In this study, qualitative approach in form of semi-structured interview is adopted to reach the objectives of this study. Five young workers between the age of 18-24 years old working in digital labour platform interviewed for this research. Thematic analysis is used to analyse the data. Research shows that young workers attracted to work in digital labour platform because of flexible working arrangement, high income, and self-employment. The findings of this study contribute to strengthening the protection of young workers working in digital labour platforms.

Abstract:

In higher education, Educational Statistics is one of the core courses required to fulfill the graduation standards of study programs in the Faculty of Education. This course is widely recognized as challenging due to its demand for higher-order thinking skills, leading educators to propose case-based learning (CBL) as a teaching model. However, several studies indicate that the effective implementation of CBL still relies heavily on the use of appropriate learning media to achieve the learning process and objective, including at Universitas Negeri Surabaya. This study aims to develop a hypermedia-based module to reinforce case-based learning (CBL) in the context of learning Education Statistics. This study employed a research and development design based on the Lee and Owens Model, which involved the participation of media experts, material experts, learning design experts, and 78 students as research subjects. The data were collected through questionnaires and analyzed using descriptive quantitative methods. The results demonstrated a significant relationship between the hypermedia-based module and CBL, particularly in supporting each phase of the CBL process. Additionally, the developed hypermedia-based module improved student engagement, interest, and understanding, thereby establishing its feasibility for use in the Educational Statistics course.

Abstract:

In today’s educational landscape, technology plays a crucial role in transforming how we teach and learn, with tools like WhatsApp enhancing collaboration and promoting active engagement. Particularly in English for Specific Purposes (ESP), it is essential to make language learning both relevant and practical for students. Techniques such as roleplay, supported by digital resources like WhatsApp bots, create engaging, interactive environments that cater specifically to students’ needs for language development. This study focuses on the perceptions of 35 ESP students at SMK PGRI 1 Gresik regarding the use of WhatsApp bots as a learning medium within a roleplay method. A mixed-methods approach was employed, combining quantitative surveys and qualitative interviews to gather comprehensive insights into students’ experiences with this innovative learning tool. By exploring these valuable insights, this research aims to assess the effectiveness, accessibility, and user-friendliness of the learning medium, contributing to the ongoing discourse on technology in education and its potential to foster more interactive, engaging, and enriching learning experiences.

Abstract:

The regeneration of agriculture in rural areas faces numerous challenges, one of the most prominent being the declining interest among rural youth to work in the agricultural sector. Teachers play a crucial role in ensuring the continuity of agricultural regeneration. This study aims to answer three key research questions: (1) Do teachers in rural schools have farming experience? (2) Do rural teachers teach agriculture to their students? and (3) Have teachers ever engaged students in practical fieldwork? The research was conducted in Ciasmara Village, Bogor Regency, Indonesia, from January 2024 to September 2024. Ciasmara Village is one of the major rice-producing centers in Bogor, which holds a key position in maintaining food security for the Jakarta region. A total of 60 teachers were selected from two senior high schools in Ciasmara. The study found that only 16.67% of teachers had practical agricultural experience, while 83.3% did not. Furthermore, only 30% of teachers involved students in practical agricultural activities, while 70% had never taken students to the fields or gardens for hands-on practice. Only 33.3% of teachers had taught agricultural subjects to their students. These findings present new challenges that must be addressed by stakeholders to ensure the sustainability of agricultural education in rural areas.

Abstract:

This study examines the vital role of village leadership in coordinating ancillary services to ensure sustainable community-based tourism (CBT) development in Indonesia. This study aims the research explore how village leaders have effectively handled various ancillary components to support rural tourism and generate socio-economic advantages for local communities. Conducted in 2023 and 2024, focusing on the case of Ciasmara Village which is located within the Geopark Pongkor area, Ciasmara is a notable tourist destination in West Java Province, Indonesia. Key challenges identified through interviews and document analysis include limited financial resources, shortages in human capital, and coordination issues among stakeholders. Despite these obstacles, the study highlights innovative strategies such as securing grants, fostering inter-sector collaborations, and establishing community tourism organizations that village leaders have employed to navigate these challenges. The findings underscore the importance of leadership initiatives and strategic management of ancillary aspects in contributing to the long-term success and sustainability of CBT. This research provides valuable insights for policymakers, tourism practitioners, and community leaders dedicated to advancing inclusive and sustainable tourism in rural areas.

Abstract:

The objective of this investigation is to predict the start and progression of Multiple Sclerosis (MS) using an enhanced gradient boosting trees algorithm, taking into account a wide range of clinical and demographic factors. The study made use of Dataset, an openly available dataset from a prospective cohort study of people of Mexican mestizo heritage who were given an identification of Clinically Isolated Syndrome (CIS). From 2006 to 2010, a methodical gathering and evaluation of data on individual characteristics was conducted in order to examine possible relationships with the development of multiple sclerosis. The gradient boosting trees algorithm was applied when creating models for forecasting, harnessing patient-specific characteristics, including demographic factors and clinical data. With a small standard deviation and a mean accuracy of 99.63%, the classifier behaved quite well. There was only one false positive and no false negatives compared to the confusion matrix. Important metrics like recall, accuracy, and AUC all got within range of 1, showing how well the classifier could confidently distinguish between the two classes. Superior performance was found when compared to previous research in the literature, demonstrating the classifier's efficacy and accuracy in predicting MS. The direction of the gradient boosting trees technique presents a viable path for early diagnosis and customized treatment by diagnosing MS based on clinical and socioeconomic factors.

Keywords:

Multiple Sclerosis (MS), Clinically Isolated Syndrome (CIS), Confusion matrix

Abstract:

The Thyroid gland is a key organ in the body responsible for releasing and regulating hormones that control metabolism. These hormones influence nearly every tissue in the human body. In healthcare, machine learning models play a vital role in disease analysis and prediction. With the vast amount of data available in the medical field today, machine learning algorithms are essential for extracting valuable insights from high-dimensional datasets and identifying diseases in their early stages. This aids healthcare professionals in making more informed treatment decisions. In this study, machine learning techniques are employed to predict the likelihood of patients developing thyroid disease, using benchmark data from the UCL Machine Learning Repository. The research applies various machine learning algorithms, including Naive Bayes, Logistic Regression, and Multilayer Perceptron, to accurately diagnose hypothyroidism based on patient information.

Keywords:

Thyroid, Analysis, Feature Engineering, Machine Learning.

Abstract:

Breast carcinoma is the process of growth abnormal lump in the breast. It begins in the cell and it scatter all over the body. Breast carcinoma is first detected by diagnostic tests and procedures that may be used to confirm the presence of cancer and to determine if it has been Spread or not. In Worldwide, breast carcinoma is the majority frequently recognized mortal cancer in women and the leading source of carcinoma death among the women. Early analysis of carcinoma on discovers symptomatic patients as premature as feasible so the medical supervisor has able to provide the successful treatment. When the carcinoma has diagnosis on later then the possibility of survivance is less, cost of the treatment will be high. This research has focused on the type of the carcinoma at the early stage of cancer to save the life of the patients. Breast carcinoma types of benign/malignant prediction throughout the proper machine learning techniques. WBCO dataset are utilized in the study. The breast carcinoma mortality ratio is increasing has to be noticeable. This research actuates the deep learning techniques in the breast carcinoma prediction. The feature selection is based on the proposed OPFF algorithm to select the features of high significance in the algorithm which helps to improve the classification accuracy. The classification algorithm of XGBMLP are based on the deep learning neural networks to improve the classification accuracy with and without feature selection and concluded the proposed classification algorithm have higher accuracy when compared to the various existing algorithm.

Keywords:

carcinoma, Prediction, Feature Selection, Deep Learning

Abstract:

Southeast Asia has the highest prevalence of micronutrient deficiencies worldwide, primarily for six micronutrients: vitamin A, iron, zinc, iodine, vitamin B9 (folic acid), and vitamin B12. In 2019, the prevalence of moderate to severe food insecurity in the ASEAN community was 18,6 percent, which led to approximately 24 percent of the population consuming inadequate amounts of essential vitamins, minerals, and trace elements. Micronutrient deficiency could affect a country's individual and human development, mainly regarding global health and economic outcomes. The present study aimed to synthesize and critically analyze the multiplying effect of micronutrient deficiency on human development in Southeast Asia countries. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Bibliometric software such as VosViewers and Nvivo were used to map and analyze the qualitative data. The initial sample was 231 articles indexed in Scopus, PubMed, Web of Science, and Embase databases. This study focused on finding the prevalence and the varied effect of each micronutrient deficiency and how it impacts human development measured in Southeast Asia countries. It was expected that micronutrient deficiency affects not only the Human Development Index (HDI) but also the Socio-Demographic Index (SDI) and Healthcare Access and Quality Index (HAQ). This study will contribute to the literature as the first systematic review exploring the multiplying effect of micronutrient deficiency on human development in Southeast Asia. In addition, the findings of this study can provide recommendations for improving food and nutrition security policies, especially in ASEAN communities.

Keywords:

Micronutrients deficiency, Human development, South east Asia, ASEAN, Systematic literature review

Abstract:

One well-known hazardous chemical that negatively impacts living things is cyanide. However, it is one of the active ingredients used by businesses across the globe in the food processing, pharmaceutical, mining, and cosmetics sectors. A significant amount of cyanide-containing pollutants are produced during the beneficiation of gold and other precious metals from ore and are released into the environment. Since the beginning of metal extraction from ore, the prevalence of cyanide contaminants from these industries has raised concerns about public health. This study discusses a variety of techniques, including new, chemical, biological, and natural ones, for cleaning the waste water from gold mines. Although there are no residual wastes and minimal operating costs with the natural detoxification method, metal-cyanide complexes are formed in the pond when cyanide reacts with various metals. Chemical methods, while now widely accepted, have several drawbacks, including the inability to completely degrade stable cyanide complexes, the need for reactants, specialized equipment, payment of royalties, and the generation of undesired secondary compounds. Because the bacteria can be easily and cheaply reproduced, and because their biotransformation ability allows cyanide to be converted into less toxic compounds, using acclimated bacteria to the contaminant is an appealing alternative.

Abstract:

This dissertation explores the factors affecting international sales and growth impact for Mrembo Naturals in Rwanda. As the cosmetic industry continues to evolve, understanding the contextual factors influencing a brand’s success becomes paramount. The study was guided by the following objectives: to identify the factors affecting MNLtd’s product sales in the international markets with a focus on Rwanda, to determine the factors that impacts the future growth of MNLtd in the international market with a focus on Rwanda, to determine strategies which MNLtd can adopt to overcome the challenges it faces in the international market and to provide recommendations on measures to improve sales factors and growth for MNLtd in East Africa. The study establishes the significance of investigating the factors that contribute to the success of MNLtd in the Rwandan market. A thorough review of existing literature on sales and growth factors within the cosmetics industry provides a theoretical framework for the study. The literature identifies key concepts and success factors relevant to the enterprise laying the groundwork for subsequent analysis. A quantitative research methodology was adopted to capture the different aspects of MNLtd’s market presence, consumer behavior, and competitive landscape. The data was analysed using the statistical software called STATA, Version 18. The data were analysed by using frequency tables, percentages, mean and standard deviations. Tabulations were made to present results of the research. These aspects were scrutinised to unveil opportunities and challenges faced by the enterprise. An in-depth analysis of the different factors was presented where findings revealed that there are different strategies that can help enterprises in the beauty and cosmetics industry to understand the key success factors that a business should focus on while working on other factors that can hinder its growth. In conclusion, the study has demonstrated that expanding into other countries could create opportunities for the enterprise. It is recommended that for MNLtd to approach the implementation of the findings of the study, the enterprise should consider the unique characteristics of the East African market and adapting strategies as needed, based on ongoing feedback and market dynamics.

Abstract:

The use of nano-biofertilizers (NBF) has emerged as a key innovation in addressing the increasingly complex challenges of agricultural production, including soil quality degradation and inefficient fertilizer use. This review article thoroughly explores the role of NBF in enhancing crop productivity, with a focus on the mechanisms of interaction between nanoparticles and microorganisms that facilitate nutrient uptake. Additionally, it discusses various NBF synthesis techniques, including physical, chemical, and biological methods, and how this technology can address environmental challenges such as drought, salinity, and pathogen attacks. The application of NBF to different types of crops, under both stress and normal conditions, has demonstrated significant improvements in growth and yield. NBF holds great potential for promoting sustainable and environmentally friendly agriculture by optimizing natural resources, improving soil quality, and reducing the negative impacts of chemical fertilizers. This literature review aims to provide a comprehensive overview of the opportunities and challenges in the development and application of NBF in the future

Keywords:

Agroecosystem, Nano-biofertilizer, Nanoparticles, Microorganisms, Crop stress.

Abstract:

This paper applies the Transformer model to topic modeling in Prajna Paramita Heart Sutra. Then, by pre-processing the original text and vernacular notes, it uses the Transformer model for theme modeling, optimizes the number of themes, optimizes the weight of keywords, and finally worked out five key topics: Prajna Paramita Heart Sutra, Buddhist theory, cultivation and practice, Buddha and Bodhisattva, Emptiness and Truth. These themes are quite consistent across various model outputs. These themes are categorized, keywords are extracted, and relationships are matched using the Transformer model at a quite high level of precision, recall, and accuracy. Meanwhile, this research confirms that, in the topic modeling process based on complex texts, the Transformer model is indeed effective and reliable, providing new technical support and a reference for studies in the field of classical Buddhist studies and text topic modeling. This is basic research work that can provide the basis for future investigation.

Keywords:

transformer deep learning, topic modeling, content analysis

Abstract:

Interior design education currently requires greater emphasis on the conservation aspects of historic buildings and their associated values. However, there is a notable deficiency in interactive learning methods that can enhance awareness among students and the broader community regarding the significance of interior design education in supporting the preservation of local historic structures and cultural heritage. Literature reviews indicate that students can grasp the complexities and historical importance of buildings and their contents through rich visualizations and comprehensive narratives. This research seeks to investigate the potential of documentary films as an educational resource that can effectively integrate architectural and interior design information related to historic buildings, utilizing immersive technology as an interactive learning tool. The methodology employed is qualitative, utilizing a case study approach focused on documentary films that emphasize the conservation of historic buildings and their artifacts. Data was gathered through observation, literature review, and content analysis of relevant documentary films. The findings reveal that incorporating documentary films into interior design education offers substantial advantages, such as enhancing contextual understanding, refining analytical skills, illustrating practical challenges and creative solutions, and providing inspiration for students in their design endeavors.

Abstract:

Traditional voltage source inverters (VSIs) and current source inverters (CSIs) have been widely used in industry for a long time. However, neither the voltage source nor the current inverter can function as a buck-boost inverter. To perform as a buck- boost inverter, they need an additional boost or buck DC-DC converter. Due to this major drawback of VSI and CSI inverters, Z-source inverters (ZSI) have been getting attention over the last two decades. Different types of single- and three-phase Z-source inverters have been analyzed over the years. By addressing the above matter, this article represents a new single-phase cascaded quasi-Z-source impedance (C-QZSI) network inverter containing a modified unipolar SPWM switching scheme. In this study, the working principle of the modified unipolar SPWM technique and the proposed cascaded qZSI are discussed. It is observed that modified unipolar PWM schemes having an additional boost switch employed on a qZSI inverter circuit result in a larger boost voltage/current and improved THD at its load. The article also deals with the analysis, simulation results, and comparison of the proposed method with the QZSI topology having different control schemes. MATLAB/Simulink simulation software was used to perform all the simulations. The presented technique results in a voltage boost with a value of 417.6 V, which is 39% larger than the QZSI topology with a modified switch technique. In addition to that, it gives us a lower THD than the traditional qZSI topologies, with a value of 0.91%

Index Terms

cascaded QZSI, MATLAB/Simulink, THD, unipolar PWM.

Abstract:

This project concerns designing and elaborating a magnetic field strength calibration and detection system engaging Hall effect sensors, MOSFETs (operating as a switch), and operational amplifiers (non-inverting configuration) within an analog circuit. The Hall effect sensor is used for detecting variations in magnetic fields by measuring the voltage difference initiated by the magnetic flux. The output signal from the sensor is amplified and analyzed through an operational amplifier, with a MOSFET being integrated for signal modulation and control. It has been calibrated to measure various strengths of the magnetic field, ensuring precise detection. Since the output can be so accurate, the system is suitable for applications encompassing magnetic field monitoring, material sorting, and industrial automation. It is also suited for task featuring time-keeping the speed of wheels and shafts to units for tachometers and anti-lock braking systems. The primary challenges remain in noise reduction during signal processing and high accuracy in calibration. The system developed here can serve as a pillar for more complex magnetic field sensing applications. This research helps to tackle these challenges and provides a reliable framework for further developments in magnetic field sensing technologies. Such research outcomes not only add value to the Art of Magnetic Field Measurement but also lays the groundwork for innovative applications across various industries leading to enhanced operational productivity and safety.

Keywords –

Magnetic Field Strength detection, Hall Effect Sensor, MOSFET, Operational Amplifier, Calibration.

Abstract:

Innovation is the “cause of the economic evolution” according to Schumpeter’s theory (Schumpeter, 1911). Organizations have to innovate their products, services, but also their managerial practices which refers to the expression “managerial innovation” used for the first time by Kimberly (1981). Managerial innovation has seen a renewed interest in going beyond the organizational and administrative innovation (Damanpour & Aravind, 2012). However, research on managerial innovation remains inferior to that on technological one, On the one hand, studies related to managerial innovation remain rare (Damanpour & Aravind, 2012; Duboulouz & Bocquet, 2013; Volberda et al., 2014; Walker et al., 2015). On the other hand, the existent and scattered literature highlights the difficulty regarding the identification of managerial innovation due to its abstract nature. It is in this perspective that our research aims to fill the gap related to the analysis centered on the distinctive characteristics of managerial innovation. Through a review of literature, the research aims to answer the following questions: 1. What are the intrinsic characteristics of managerial innovation? 2. What are the determinants and factors influencing the adoption of managerial innovation? 3. What are the main managerial innovations adopted so far? The review contributes to the state of art by offering a cartography of managerial innovation with concrete examples of practices qualified as managerial innovations thus making its conceptual delimitation and practical application easier.

Abstract:

This project looks at an alternative method for improving solar energy capture efficiency by incorporating a solar tracking mechanism with piezoelectric plates. Solar trackers increase energy output by ensuring that photovoltaic (PV) panels always face the sun as it moves across the sky. However, we think there is still room for further enhancing efficiency by capturing the electrical energy that static electricity can generate through the use of piezoelectric materials. Our design addresses this complexity by enabling our dual-function solar tracker to not only ensure that the panels of the PV solar system tend to the movement of the sun but also harness the energy of vibration forces due to wind and other movement tendencies. A micro-controller-actuated tracking system allows maneuverability of the panels in the direction of the sunlight at all times. Consequently, PZT plates at certain locations harvest mechanical energy from the surroundings and convert it into additional electrical energy. In order to test our concept, the solar tracker structure was compared in energy output with our additional piezoelectric system and without it. Key metrics included total generation energy plant PV system, available derivable and insuperable from the piezoelectric devices brought additional power gained. The results so far are insightful and show that there is an improvement in the efficiency of energy harvested, which offers hope for this merger of technology. Our project assists in the continuing development of such systems for other implementing states by pointing out how this multi-functional system is beneficial in increasing the generation of solar energy.

Keywords –

Solar tracker, Piezoelectric

Abstract:

The cultivation of oyster mushrooms demands skillful control over environmental conditions and timely harvesting to optimize yield as well as quality. By using the IoT system, key growth parameters — temperature, humidity, air quality, and light intensity — are monitored. Simultaneously, a machine learning framework that uses Convolutional Neural Networks (CNNs) and object detection models analyze and capture patterns related to environmental factors that influence mushroom growth stages. This offers system integration with solar power to enhance sustainability and automates the environmental monitoring and control solutions thus reducing operation costs while keeping the quality of yield at a higher level. This novel study not only speeds up the growing process but also helps predict exactly when the crop is ready to harvest. Harvesting oyster mushrooms at the right time ensures they reach the best size and weight, leading to larger yields while preserving their taste, texture, and nutritional value for customer satisfaction. Accurate predictions of harvest readiness help farmers plan better to meet market demand and increase their profits. The result of this study used a machine learning model to predict if oyster mushrooms can be harvested. The trained model achieved 85% accuracy, with 97% precision and 82% recall, resulting in an F1 score of 89%. Cohen's Kappa analysis showed a strong match between the model's predictions and the farmer's judgment, with a Kappa value of 0.654 and a p-value of 0.000, meaning the model is reliable

Keywords –

oyster mushroom cultivation, solar-powered IoT, machine learning, harvest prediction, smart agriculture

Abstract:

Healthcare and insurance systems face significant challenges in ensuring the secure portability and transfer of sensitive records due to fragmented infrastructures, data privacy concerns, and inefficiencies in data sharing. Current centralized systems that are aware of breaches, they are not compatible and reduce the degree of patient control or autonomy which in turn escalates the transfer between care giving institutions and insurance firms. To deal with these challenges, in this research, the BCIF-EHR model integrating the IoT Healthcare Security Dataset and the US Health Insurance Dataset is proposed. This new concept combines clinical IoT-based ICU data and financial insurance information into one format that supports secure decentralized record transfer. Blockchain network is applied for storing data in a decentralised and extremely secure manner, thereby ensuring that data cannot be manipulated once recorded while cloud systems ensure availability of large datasets in a real- time basis. Thus, the BCIF-EHR framework safeguards patient ownership of data, includes embedded smart contract for claim validation, and adheres to relevant rules such as HIPAA and GDPR for the elements of privacy. The approach is about preparing the data to standardization by using synthetic IDs, storing them using blockchain, and the use of cloud services for handling Big Data. Measures include the ability to transfer data within a stated time, execute transactions per time, or measure up to the set security standards. As the results presented indicate, there is a 30% decrease in operational downtime, and improved data security and integration capabilities of the framework prove the ability of such a system to eliminate existing shortcomings. As the technological framework of protecting disparate datasets while enabling efficiency in managing and transferring health and insurance records, BCIF-EHR optimizes heterogeneous datasets. It is with the view of redressing this situation that this study presents a novel perspective towards the advancement of current conventional health care and insurance systems technology

Abstract:

The project involves detecting traffic signal violations by vehicles and issuing e-challans through a combination of programming and hardware. Using RFID technology, which includes tags that store data and transmit it wirelessly to readers, the system identifies vehicles that break signals. The microcontroller compares the information from the reader with the stored details of the vehicle, and if a violation is detected, an e-challan is automatically sent to the vehicle owner's registered mobile number and the Regional Transport Office (RTO). The owner can pay the fine either in person at the RTO or online if linked to an electronic payment system. This system also facilitates toll collection using the same RFID tags. Through vehicle-to-roadside communication, electronic monetary transactions are made between the vehicle and the toll station. The RFID tags store a unique ID and related information for each vehicle. When a vehicle approaches a toll gate, the reader picks up the tag's data, and the toll fee is deducted from the user’s account. The system also checks for outstanding fines, road taxes, or insurance issues. If any charges are due, the toll gate remains closed until payment is made. Additionally, if the system identifies a stolen vehicle, a notification is sent to the nearest police station. All vehicle-related documents, such as ownership and insurance, are verified using the RFID tag.

Keywords –

E-challan, RFID technology, vehicle-to- roadside communication

Abstract:

The accurate detection of defects in carrots is critical for ensuring quality control in the design of processing equipment. This study focuses on the development of an advanced artificial intelligence model for detection of defects on the surface of carrots. A comprehensive dataset of carrot defect images was prepared in uniform conditions ensuring robust model training. Feature extraction and preprocessing techniques were applied to enhance defect visibility and improve classification accuracy. Various machine learning approaches, including convolutional neural networks, were evaluated for their performance in detecting defects with high precision and recall. The results demonstrated the model's capability to achieve superior accuracy compared to conventional methods. The proposed artificial intelligence system offers a reliable, efficient, and scalable solution for the design of carrot processing equipment.

Keywords –

carrots, surface defect, artificial intelligence model, image dataset, detection