Random forest algorithm use for crop recommendation

The proposed method seeks to assist Indian pleasant in selecting the optimum crop to produce based on the characteristics of the soil as well as external factors like temperature and rainfall by using an intelligent system called Crop Recommender. The Indian economy is significantly impacted by the agricultural sector. Whether publicly or covertly, the bulk of Indians are relying on agriculture for their living. As a result, it is undeniable that agriculture is significant to the country. The majority of Indian farmers believe that they should trust their intuition when deciding on a crop to grow in a particular season or they simply employ the methods they have been doing from the beginning of time. They are more at ease just adhering to conventional agricultural practices and standards than truly appreciating how crop yield is influenced by the present weather and soil conditions. The farmer can unintentionally lose money if he makes one bad decision, which would hurt both him and the surrounding agricultural industry. As the agriculture business is the foundation of the entire lateral system. Using the machine learning algorithm, this problem can be resolved. A crucial perspective for identifying a practical and workable solution to the crop production issue is machine learning (ML). Machine learning (ML) may predict a target or outcome from a set of predictors using supervised learning. A recommendation system is implemented using decision trees. The major goals of this system are to provide farmers with recommendations regarding the best crops to sow based on their soil and local rainfall patterns. We have employed the Random Forest Machine Learning technique to forecast the crop. Crop prediction is assessing the crop based on historical data from the past that includes elements like temperature, humidity, ph, and rainfall. It gives us a broad picture of the best crop that can be raised in light of the current field weather conditions. These predictions can be made by Random Forest, a machine learning technique. The highest level of accuracy, up to 90%, will be possible for crop predictions. The random forest algorithm achieved the accuracy about 99.03%

Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images

High-quality labeled datasets are essential for deep learning. Traditional manual annotation methods are not only costly and inefficient but also pose challenges in specialized domains where expert knowledge is needed. Self-supervised methods, despite leveraging unlabeled data for feature extraction, still require hundreds or thousands of labeled instances to guide the model for effective specialized image classification. Current unsupervised learning methods offer automatic classification without prior annotation but often compromise on accuracy. As a result, efficiently procuring high-quality labeled datasets remains a pressing challenge for specialized domain images devoid of annotated data. Addressing this, an unsupervised classification method with three key ideas is introduced: 1) dual-step feature dimensionality reduction using a pre-trained model and manifold learning, 2) a voting mechanism from multiple clustering algorithms, and 3) post-hoc instead of prior manual annotation. This approach outperforms supervised methods in classification accuracy, as demonstrated with fungal image data, achieving 94.1% and 96.7% on public and private datasets respectively. The proposed unsupervised classification method reduces dependency on pre-annotated datasets, enabling a closed-loop for data classification. The simplicity and ease of use of this method will also bring convenience to researchers in various fields in building datasets, promoting AI applications for images in specialized domains

Few-Shot Learning for Post-Earthquake Urban Damage Detection

Koukouraki, E., Vanneschi, L., & Painho, M. (2022). Few-Shot Learning for Post-Earthquake Urban Damage Detection. Remote Sensing, 14(1), 1-20. [40]. https://doi.org/10.3390/rs14010040 ------------------------------ Funding: This study was partially supported by FCT, Portugal, through funding of projects BINDER (PTDC/CCI-INF/29168/2017) and AICE DSAIPA/DS/0113/2019). E.K. would like to acknowledge the Erasmus Mundus scholarship program, for providing the context and financial support to carry out this study, through the admission to the Master of Science in Geospatial Technologies.Among natural disasters, earthquakes are recorded to have the highest rates of human loss in the past 20 years. Their unexpected nature has severe consequences on both human lives and material infrastructure, demanding urgent action to be taken. For effective emergency relief, it is necessary to gain awareness about the level of damage in the affected areas. The use of remotely sensed imagery is popular in damage assessment applications; however, it requires a considerable amount of labeled data, which are not always easy to obtain. Taking into consideration the recent developments in the fields of Machine Learning and Computer Vision, this study investigates and employs several Few-Shot Learning (FSL) strategies in order to address data insufficiency and imbalance in post-earthquake urban damage classification. While small datasets have been tested against binary classification problems, which usually divide the urban structures into collapsed and non-collapsed, the potential of limited training data in multi-class classification has not been fully explored. To tackle this gap, four models were created, following different data balancing methods, namely cost-sensitive learning, oversampling, undersampling and Prototypical Networks. After a quantitative comparison among them, the best performing model was found to be the one based on Prototypical Networks, and it was used for the creation of damage assessment maps. The contribution of this work is twofold: we show that oversampling is the most suitable data balancing method for training Deep Convolutional Neural Networks (CNN) when compared to cost-sensitive learning and undersampling, and we demonstrate the appropriateness of Prototypical Networks in the damage classification context.publishersversionpublishe

An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset

With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection

Electronic Word of Mouth (Ewom) and the Travel Intention of Social Networkers Post-COVID-19: A Vietnam Case

Purpose: The purpose of this study is to quantitatively determine the impact of electronic word of mouth (eWOM) on the travel intention of Vietnamese social network users. The study proposes solutions to develop online marketing strategies in tourism to quickly restore the tourism industry after the COVID-19 pandemic.   Theoretical framework: This study combines two theoretical models, the Information Acceptance Model (IAM) and the Theory of Reasoned Action (TRA), to explore how electronic Word-of-Mouth (eWOM) impacts travel intentions in Vietnam, particularly in the context of the COVID-19 pandemic. IAM focuses on factors like information quality and credibility, while TRA addresses additional behavioral factors. By integrating these models, the study aims to provide a comprehensive understanding of eWOM's influence on travel intentions among social media users in Vietnam during the pandemic.   Design/Methodology/Approach: The study was conducted in Vietnam. The questionnaire was administered to respondents via an online survey. The results were 262 valid feedback forms conducted in 2023. Linear structural modeling (SEM) was used to measure the relationship between factors in the research model.   Findings: Research results show that all factors have a positive impact on travel intention through other factors, or will directly impact travel intention of social network users in Vietnam.   Research,  Practical  &  Social implications: It is critical to focus on building marketing channels that support electronic word-of-mouth (eWOM) in order to successfully promote trip ambitions. The Tourism Office has adopted digital marketing as a cutting-edge strategy for promoting the region's tourism potential (Burhan, 2023). Furthermore, because travel intentions are positively related to information acceptance, these channels must prioritize improving the quality and dependability of eWOM information. Furthermore, channel managers should keep a careful eye on customers' opinions regarding eWOM-related concerns. Finally, administrators should create unique, context-based solutions that might increase travel intentions among social network members while taking Vietnam's cultural context into consideration. This proactive strategy enables rapid revisions and customized solutions to successfully engage with the Vietnamese audience.   Originality/Value: The unique contribution of this paper lies in its innovative introduction of a modified theoretical model of rational action. This model aims to elucidate factors influencing tourist travel behavior in a way that has not been explored in previous research. Specifically, it amalgamates elements from three existing models (TRA, IAM, and TPB) and introduces an additional eWOM factor to enhance the understanding of behavioral intentions. This novel factor, devised by the authors in their research model, holds particular relevance for a country like Vietnam, characterized by its developing technological landscape and strong traditional culture. This novelty significantly enhances our ability to analyze the impact of eWOM on travel intentions among social media users in Vietnam, especially during the prolonged effects of the COVID-19 pandemic

MULTIMODAL PERFORMANCE ANALYSIS DURING JOB INTERVIEWS

Emotion recognition based on multimodal data has become an important research topic with a wide range of applications, including online interviews. The study of respondents’ performance through the analysis of multiple modes of data is essential for a deep understanding of their emotions and communication patterns. To solve this problem, this thesis proposes a new method of analyzing multimodal interviews that uses deep learning techniques to extract meaningful information from various sources, such as video, audio, and textual data. The proposed approach uses late fusion to integrate information from different sources and generate an overall sum mary of the interviews. The effectiveness of the proposed method is evaluated on the whole MIT interview dataset, which includes 138 mock job interviews conducted with MIT undergraduates. The experimental results demonstrate that our framework can efficiently analyze multimodal data to produce promising results. The proposed approach identifies and captures critical aspects of communication, such as tone, facial expressions, and language use, which can provide valuable information to inter viewers to improve the overall interview process. This research has implications for improving understanding of communication patterns in various contexts, including job interviews, and may have practical applications in other field

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Annual Report 2017-18

Not AvailableI am extremely happy and privileged to present the annual report of ICAR-CRIDA for the year 2017- 18. During the reporting year, ICAR-CRIDA has made eloquent progress in technology development and dissemination associated with climate change in rainfed agriculture and dealing contingencies in agriculture and allied sector. The institute has received copyright for “Unreaped yield potentials in major rainfed crops and scope for bridging yield gaps - A decision support system”. ICAR-CRIDA along with SAUs and KVKs prepared contingency plans at district level for all the 126 agro-climatic zones of the country (623 districts) to deal with weather related aberrations. An IFS module with cotton, vegetables, fodder and small ruminants with farm pond using portable raingun at Chenchu tribal farmer field implemented in Petrallachenu village of Nagarkurnool district showed positive impact on socio economic condition of the farmer with total net income of Rs. 96,605/- over the traditional system of growing only rainfed cotton, which gave negative returns of Rs. (-) 3600. A small scale solar powered micro-irrigation system was designed and installed for small farmers having one acre or less land under farm pond system for growing vegetables. The assessment based on daily rainfall dataset, annual average effective rainfall and runoff percentages helped in developing the expected runoff in various rainfall zones, which could be used to estimate the runoff in meso-scale watersheds. Seven inbreds of maize (DTL2, SNJ2011- 03, SNJ2011-37, SNJ2011-26, Z101-15, Z32-12 and HKI7660) were found to be promising for use in crop improvement programme under rainfed conditions. 4:4 strip intercropping system of sorghum and pigeonpea with relay horse gram performed better compared to traditional 2:1 intercropping system. In a study on resource conserving technologies, conventional tillage recorded 15% lower maize yields as compared to conservation agriculture practices. Intensive system of rearing livestock not only improved the profitability but also significantly reduced methane emissions as compared to semi-intensive and extensive systems. Heat Load Index (HLI) and Temperature Humidity Index (THI) was found to be better choice for comparing heat stress in extensively and intensively reared sheep, respectively. A rotary implement for weeding operation was developed to effectively utilize low horse power tractor for field applications. A raised bed planter cum herbicide applicator was developed and the design was transferred to Avanthi Bufa Industries Ltd., Jahirabad. Farmers’ first project, envisaged to transfer rainfed technologies with objective of doubling farmers income is being implemented in 4 villages of Pudur mandal of Vikarabad district. Among 12 pigeonpea genotypes AKT-8811, PUSA-33, GRG-276-1 and RVK-274 were the high yielders in both unstressed and rainfed conditions. An econometric analysis of impact of climate change on crop yields showed that the impacts would be more severe and widespread towards the end of the century. Under changing climatic scenarios, runoff is not expected to vary much in Vijayapura district under low or medium emission scenarios, but the high runoff potential available under the present scenario itself shows substantial scope for rainwater harvesting and its utilization for supplemental irrigation. Decreased grub duration with increased predation capacity of M. sexmaculatus on A. craccivora with elevated CO2 indicated increased predation in future climate change scenarios. For assessing the real time climate change impacts on crop water requirements, SCADA Preface based rainfall simulator and precision lysimeter was designed and developed by using state of art process automation instrumentation in climate change research complex at Hayathnagar. Rotavator, cultivator and disc plough + harrow recorded higher GHG emissions and global warming potential, whereas animal drawn implements recorded lower emissions. Evaluation of the performance of different crops under organic, inorganic and integrated production systems showed that yield of sunflower was 14 and 7% higher under integrated management (1374 kg/ha) than that of under inorganic and organic management, respectively. Supplementation of chromium propionate @ 200 ppb can help in mitigation of heat stress in grazing lambs. An experiment to evaluate 36 elite clones of short rotation and high biomass yielding multipurpose tree species (M. dhubia, Casuarina, B. balcoa, D. sisoo and Eucalyptus) was established at Hayathnagar Research Farm. The KVK under technology assessment and refinement has assessed 17 technologies through 115 trials on crop varieties, integrated crop management, horticulture and livestock management. 269 Frontline demonstrations on 19 technologies were conducted in different disciplines. It also organized 115 need based and skill oriented training programmes on various aspects of improved technologies to 3005 clientele farmers and filed level extension workers. Two special skill development programmes allotted by Department of Horticulture, Government of Telangana in the disciplines of “ farm pond construction and lining” were organized for 520 rural youths. Exemplary performance of its scientists were visible as two scientists attended trainings/exposure visit outside the country and 52 graduate and post graduate students carried out research work at ICAR-CRIDA. Sustained performance of its scientists were exhibited in terms of 20 scientists of Institute receiving several awards, fellowships, copyright and recognition from national academies, professional societies and other institutions. The scientists of the institute published a total of 116 research articles in international and national journals, 29 books/bulletins including 2 in Hindi and 112 book chapters. The contributions of scientists also appeared in the form of a number of policy papers, bulletins, popular articles, presentations in conferences, e-publications and radio and television programmes. The collaborations with several Ministries and Departments, SAUs, NGOs and Private Industries reflect its commitment to work hand-to-hand with grow together and finding the technological solutions to the problems of farmers in rainfed regions of India. I would like to place on record my sincere gratitude to Indian Council of Agricultural Research for its continued guidance and support. I appreciate all the committee members of annual report for their timely compilation and shaping this report in time.Not Availabl