Human Life Research Articles

‘Until I go back home, I do not see justice’: Construction of justice among internally displaced farming and herding communities in Benue State, Nigeria

ABSTRACT Recurrent violent clashes between pastoral herders and farming communities in Nigeria have resulted in human fatalities, livelihood losses and displacements. However, studies have yet to interrogate how displaced farming communities construct justice as a way to restore peace. Using Internal Displaced Persons (IDPs) camp in Abegana, Makurdi Local Government Area (LGA) of Benue State, 25 participants responded to questions on their experiences and how they see justice from their victimhood as a way to peace restoration. I also balanced this with how herders construct justice. Findings show that justice is constructed as returning to their ancestral homeland where they were displaced by conflict; compensation to rebuild burnt houses and restart businesses and implementation of open grazing by government to check recurrence. Herders see anti-open grazing laws as injustice, exclusionary and persecutory rather than see them as victims with human and material losses that need to be protected. This study recommends injecting victims’ perspectives into peace building interventions in conflict zones.

Problems Encountered in Marketing Sustainable Products and Suggested Solutions in Turkey

The growth of the consumer economy in the last century has led to several significant issues that endanger human life. Overexploitation of nature and soils, the use of pesticides and growth stimulants have had a negative impact on the environment and water resources. The current situation has necessitated that societies worldwide adopt new production approaches to immediately halt soil degradation and improve water quality. One production approach that aims to protect public health and the environment is sustainable production. Sustainable production is an ecological management system that promotes biodiversity, biological cycles, and soil biological activity. The farming system is based on minimal use of off-farm inputs and management practices that restore, maintain or enhance ecological harmony. The aim of this study is to explain the importance of sustainable products and production, to address the problems and suggestions encountered in the marketing of sustainable products in Turkey, and to propose solutions.

Kompetensi Literasi Digital Mahasiswa Program Studi Pendidikan Geografi FKIP Universitas Lambung Mangkurat

The development of science and technology (IPTEK) which is very massive has a very broad influence on all aspects of human life. One of them is education and teaching that can facilitate the exchange of information so that it is not bound by space and time. This is of course very useful, especially for students, groups who always need information as reference material for their daily assignments. Therefore, students really need information literacy skills, to support the lectures they follow. There are several models used to measure a person's literacy ability, one of which is the Empowering 8 model, where this model was produced from a workshop conducted in Sri Lanka in 2004. The purpose of this study was to analyze the digital literacy competence of students of the geography education study program, FKIP Lambung Mangkurat University. This study uses a descriptive method with a quantitative approach. Based on the results of research that has been carried out, digital digital literacy students of geography education FKIP Lambung Mangkurat University are at a good or mastered level. Where from the respondents' answers, 17% were very dominant, 71% controlled, 11% did not master, and 1% did not master. Respondents who are very mastering, not mastering, and very not mastering are in the low category, respondents who are mastering are in the high category.

ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological images

Background and objective:Lung cancer (LC) has a high fatality rate that continuously affects human lives all over the world. Early detection of LC prolongs human life and helps to prevent the disease. Histopathological inspection is a common method to diagnose LC. Visual inspection of histopathological diagnosis necessitates more inspection time, and the decision depends on the subjective perception of clinicians. Usually, machine learning techniques mostly depend on traditional feature extraction which is labor-intensive and may not be appropriate for enormous data. In this work, a convolutional neural network (CNN)-based architecture is proposed for the more effective classification of lung tissue subtypes using histopathological images. Methods:Authors have utilized the first-time nonlocal mean (NLM) filter to suppress the effect of noise from histopathological images. NLM filter efficiently eliminated noise while preserving the edges of images. Then, the obtained denoised images are given as input to the proposed multi-headed lung cancer classification convolutional neural network (ML3CNet). Furthermore, the model quantization technique is utilized to reduce the size of the proposed model for the storage of the data. Reduction in model size requires less memory and speeds up data processing. Results:The effectiveness of the proposed model is compared with the other existing state-of-the-art methods. The proposed ML3CNet achieved an average classification accuracy of 99.72%, sensitivity of 99.66%, precision of 99.64%, specificity of 99.84%, F-1 score of 0.9965, and area under the curve of 0.9978. The quantized accuracy of 98.92% is attained by the proposed model. To validate the applicability of the proposed ML3CNet, it has also been tested on the colon cancer dataset. Conclusion:The findings reveal that the proposed approach can be beneficial to automatically classify LC subtypes that might assist healthcare workers in making decisions more precisely. The proposed model can be implemented on the hardware using Raspberry Pi for practical realization.

Munir: Weakly Supervised Transformer for Arabic Computational Propaganda Detection on Social Media

The intentional manipulation of public opinion has become prevalent in the realm of computational propaganda. The swift spread of misinformation through social networking sites poses significant challenges for governments and society, impacting various aspects of human life. Arab countries are considered among the most affected countries. Accurately identifying and countering computational propaganda is crucial, especially given the impracticality of manually annotating large volumes of social media-generated data. Moreover, the constant propagandists' evolving tactics pose a challenge to accounting models, making the immediate preparation of responsive training data difficult. To address this issue, this research proposes a novel weakly supervised learning approach, leveraging programmatic labeling to label training data in a systematic and timely manner. New labeling functions (LFs) are introduced, where experts' heuristics, knowledge, new proposed lexicons, different fine-tuned pre-trained models are turned into rules to label the data. Leveraging these LFs, we fine-tune a deep learning model for computational propaganda detection. The proposed model achieves a remarkable 94% accuracy and 86% precision in the minority class, outperforming a fine-tuned, fully supervised deep learning model. This research contributes a substantial dataset, a robust weakly supervised model, and lexicons, offering valuable tools for combating computational propaganda on Arabic social media. The code and the dataset are publicly available at https://github.com/Bmalmotairy/Arabic-Propaganda-Detection.

Risk analysis for groundwater intakes based on the example of neonicotinoids

Neonicotinoids are a class of broad-spectrum insecticides that are dominant in the world market. They are widely distributed in the environment. Understanding the sources, distribution, and fate of these contaminants is critical to mitigating their effects and maintaining the health of aquatic ecosystems. Contamination of surface and groundwater by neonicotinoids has become a widespread problem worldwide, requiring comprehensive action to accurately determine the mechanisms behind the migration of these pesticides, their properties, and their adverse effects on the environment. A new approach to risk analysis for groundwater intake contamination with emerging contaminants was proposed. It was conducted on the example of four neonicotinoids (acetamiprid, clothianidin, thiamethoxam, and imidacloprid) in relation to groundwater accessed by a hypothetical groundwater intake, based on data obtained in laboratory tests using a dynamic method (column experiments). The results of the risk analysis conducted have shown that in this case study the use of acetamiprid and thiamethoxam for agricultural purposes poses an acceptable risk, and does not pose a risk to the quality of groundwater extracted from the intake for food purposes. Consequently, it does not pose a risk to the health and life of humans and other organisms depending on that water. The opposite situation is observed for clothianidin and imidacloprid, which pose a higher risk of groundwater contamination. For higher maximum concentration of neonicotinoids used in the risk analysis, the concentration of clothianidin and imidacloprid in the groundwater intake significantly (from several to several hundred thousand times) exceeds the maximum permissible levels for drinking water (<0.1 μg/L). This risk exists even if the insecticides containing these pesticides are used according to the information sheet provided by the manufacturer (lower maximum concentration), which results in exceeding the maximum permissible levels for drinking water from several to several hundred times.

TRANSITION FROM MATERNAL CHRONIC RENAL FAILURE TO PERITONEAL DIALYSIS TREATMENT

Chronic kidney failure (CKF) has become an increasingly important public health issue in our country and worldwide. According to data systems in our country, as of the end of 2022, there were a total of 86,665 patients with chronic kidney failure, and 4.1% of these patients undergo peritoneal dialysis treatment. Peritoneal dialysis requires active participation of the patient in the treatment, and it can lead to negative effects on body image, self-esteem, and sexual relations due to the peritoneal dialysis catheter, causing anxiety and depression in patients and negatively affecting their quality of life. Throughout human life, individuals undergo changes in identity, roles, responsibilities, abilities, and behaviors due to various reasons (illness, pregnancy, death, birth). One of the fundamental duties of nurses is to assist individuals undergoing various transitions in their lives, to support healthy transitions, and to minimize unhealthy transitions. In this study, the process of transition experienced by a healthy woman transitioning to the role of motherhood due to health problems during pregnancy, and transitioning to chronic kidney failure and peritoneal dialysis treatment, is examined based on Meleis' Transition Theory

Tailoring supramolecular solvents with phosphoryl groups for highly efficient extraction of chlorophenols in natural waters

BackgroundChlorophenols are routinely determined in aquatic systems to check compliance with the restrictive international legislations set for protection of human and aquatic life. Their control requires affordable analytical methods, particularly in labs at low- and medium-income countries. Liquid chromatography-UV detection is a convenient technique for this purpose, but the availability of suitable sample processing remains pending. Organic solvents are inefficient for extracting the whole range of chlorophenols whereas solid-phase extractions are expensive and labour-intensive. So, an efficient, fast and cheap extraction of chlorophenols, amenable to any lab, would help to cope with their worldwide analytical control in natural waters. ResultsA supramolecular solvent (SUPRAS) was tailored for providing mixed interaction mechanisms aimed at the efficient extraction of chlorophenols prior to LC-UV. The SUPRAS was synthesized from the self-assembly of hexylphosphonic acid under acidic conditions and consisted of sponge-like nanostructures made up of amphiphile and water. The phosphoryl (PO) group was selected as the major driver of the extraction because of its ability to act as halogen and hydrogen bond acceptor for chlorophenols. Additional interactions were hydrogen bonds from O–H amphiphilic groups and the surrounding water, and dispersion and CH-π interactions in the hydrocarbon chains. The number of binding sites in the SUPRAS could be modulated by addition of salt. The SUPRAS formed in situ in the sample, the extraction took 5 min, the concentration factor was around 220, quantification limits (0.1–0.3 μg L−1) were below the EU standards, and the method worked for natural waters. SignificanceA fast, low-cost, and organic solvent-free sample processing only requiring conventional lab equipment (stirrers and centrifuges) provided SUPRAS extracts that could be directly analyzed by LC-UV. SUPRAS synthesis occurred spontaneously in the water sample under addition of hexylphosphonic acid and the whole process required low skills. The method meets the analytical and operational performances for the analytical control of chlorophenols in natural waters and it is within the reach of any lab.

Self-floating and long-term stable Ti3C2TX/polyurethane composite membranes with highly efficient photothermal conversion performances for multiple applications

Photothermal conversion has emerged as a highly effective method for converting solar energy into thermal energy for a variety of applications. In this study, we developed Ti3C2TX/PU membranes with exceptional photothermal conversion performance using a simple casting process. The Ti3C2TX/PU membranes are flexible, self-floating, and possess suitable strength and hydrophilicity. Additionally, they exhibit enhanced sunlight absorption capability and excellent thermal stability. When these membranes were applied for solar-driven seawater evaporation, they achieved an evaporation rate of 1.35 kg∙m−2 h−1 and a conversion efficiency of 84.85 % under 1 sun. In real outdoor conditions, they also displayed satisfactory evaporation performance. Impressively, the Ti3C2TX/PU membranes exhibited stable salt resistance and remarkable self-desalting properties, which are crucial for practical seawater evaporation applications. Furthermore, we observed that the photothermal performance of the Ti3C2TX/PU membranes improved with a higher Ti3C2TX content. Particularly noteworthy is the significant reduction in ice melting time when these membranes are wrapped around power wires, demonstrating their excellent photothermal deicing capabilities. The Ti3C2TX/PU membranes also showed promising application potential in low-temperature photothermal therapy treatment. We believe that such efficient and stable photothermal membranes will provide a sustainable solution for addressing challenges related to human health, life, and survival.

Smart Waste Management System Using IoT

Waste collection and management are integrated parts of both city and village life. Lack of an optimized and efficient waste collection system vastly affects public health and costs more. The prevailing traditional waste collection system is neither optimized nor efficient. The Internet of Things (IoT) has been playing a great role in making human life easier by making systems smart, adequate, and self-sufficient. Thus, this paper proposes an IoT-based, efficient waste collection system with smart bins. India generates tons of waste annually. Conventional garbage collection is not efficient since the authorities are not notified until the waste bin is full, and this leads to an overflow of waste material. Efficient waste disposal and collection are essential for a sustainable and clean India. A smart waste management system uses an IoT-based waste bin for collection and monitoring the level of waste inside the bin. The system does real-time monitoring of the waste bins and determines which are to be emptied. The system is implemented using two ultrasonic sensors. One of the ultrasonic sensors detects the level of waste in the bin, and the other detects the person approaching the bin to dispose of the waste. This detection helps with the automatic opening and closing of the lid. A servo motor is connected to the lid, which serves the action of closing and opening the lid. In this system, the level of waste in the bin will be sent to the concerned authorities. Thus, it reduces fuel costs and human labor, making the system optimized and efficient by enabling real-time monitoring and enhanced navigation.

Islamic Fintech Research Worldwide: A Bibliometric Analysis

Islamic Fintech continues to grow as innovation in the field of technology accelerates human life. Although relatively new, research on Islamic Fintech continues to grow and has been quite attractive to researchers in recent years. Seeing this phenomenon, it is necessary to anticipate the increasing interest of academics in researching Islamic Fintech. This paper aims to facilitate researchers in finding out the development of Islamic Fintech research, find topics that can be developed, and know the main contributors and sources that shape research in the field of Islamic Fintech. This research analysis is a Bibliometric analysis with Biblioshiny and VOSviewer software, using metadata consisting of 98 Scopus-indexed data published in 10 years, namely 2013-2022. The results of the study found that the potential for developing research on the theme of Islamic Finance is huge, topics that can be developed for further research include "Crowdfunding" and "Crowdsourcing.” Meanwhile, Muslim-majority countries dominate Islamic Finance research, including with their authors and affiliations.

Smart Vehicle Load Management with Arduino Sensors

In the context of road safety, vehicle overloading remains a persistent concern, posing a risk of accidents, infrastructure damage, and potential harm to human life. This project introduces an innovative solution for mitigating this issue through an Arduino-based smart vehicle load management The system incorporates load cell, Ultrasonic sensor, an LCD display, and a buzzer to effectively detect and prevent vehicle overloading, contributing to improved road safety. The smart vehicle load management with arduino sensors is designed to monitor the weight distribution on a vehicle using strategically placed load cells and detect variations in vehicle obstacle detection through Ultrasonic sensors. Upon detecting an overload condition, the system promptly triggers a response by stopping the motor, which prevents further loading, and immediately displays a warning on the LCD screen. Simultaneously, it activates an audible alert through the buzzer. This rapid reaction not only averts potential accidents resulting from overloading but also extends the vehicle's operational life by minimizing wear and tear. The Arduino-based Vehicle Overload Detection System represents a cost-effective and efficient approach to addressing the critical issue of vehicle overloading. Its monitoring capabilities, coupled with instant warnings and alerts, establish it as a valuable tool in enhancing road safety and reducing the risks associated with overloading.

Slower-decaying tropical cyclones produce heavier precipitation over China

The post-landfall decay of tropical cyclones (TC) is often closely linked to the magnitude of damage to the environment, properties, and the loss of human lives. Despite growing interest in how climate change affects TC decay, data uncertainties still prevent a consensus on changes in TC decay rates and related precipitation. Here, after strict data-quality control, we show that the rate of decay of TCs after making landfall in China has significantly slowed down by 45% from 1967 to 2018. We find that, except the warmer sea surface temperature, the eastward shift of TC landfall locations also contributes to the slowdown of TC decay over China. That is TCs making landfall in eastern mainland China (EC) decay slower than that in southern mainland China (SC), and the eastward shift of TCs landfall locations causes more TCs landfalling in EC with slower decay rate. TCs making landfall in EC last longer at sea, carry more moisture upon landfall, and have more favorable dynamic and thermodynamic conditions sustaining them after landfall. Observational evidence shows that the decay of TC-induced precipitation amount and intensity within 48 h of landfall is positively related to the decay rate of landfalling TCs. The significant increase in TC-induced precipitation over the long term, due to the slower decay of landfalling TCs, increases flood risks in China’s coastal areas. Our results highlight evidence of a slowdown in TC decay rates at the regional scale. These findings provide scientific support for the need for better flood management and adaptation strategies in coastal areas under the threat of greater TC-induced precipitation.

Increasing Fire Activity in African Tropical Forests Is Associated With Deforestation and Climate Change

AbstractFires were historically rare in tropical forests of West and Central Africa, where dense vegetation, rapid decomposition, and high moisture limit available fuels. However, increasing heat and drought combined with forest degradation and fragmentation are making these areas more susceptible to wildfires. We evaluated historical patterns of Moderate Resolution Imaging Spectroradiometer active fires in African tropical forests from 2003 to 2021. Trends were mostly positive, particularly in the northeastern and southern Congo Basin, and were concentrated in areas with high deforestation. Year‐to‐year variation of fires was synchronized with increasing temperature and vapor pressure deficit. There was anomalously high fire activity across the region during the 2015–2016 El Niño. These results contrast with the drier African woodlands and savannas, where fire has been decreasing. Further attention to fires in African tropical forests is needed to understand their global impacts on carbon dynamics and their local implications for biodiversity and human livelihoods.

The Yogavāsiṣṭha and the Philosophy of the Absurd: A Hindu Response to the Heart’s Cry

AbstractIn this article, I call attention to the Yogavāsiṣṭha’s depiction of Rāma’s existential crisis and Vasiṣṭha’s various methods for resolving it. However, I illuminate this crisis and Rāma’s lengthy monologue by turning to an altogether different frame of reference: Albert Camus’s philosophy of the absurd as developed in his absurd-oriented works, from The Myth of Sisyphus to The Rebel. Arguing that Rāma’s existential crisis is strongly reminiscent of Camus’s awakening to life’s absurdity, and that his monologue may be considered among the most successful meditations on this predicament, I examine not only Rāma’s quandary but also what the Yogavāsiṣṭha deems the ultimate response to it. In this way, I throw into sharp relief two contrasting responses to the absurd condition in human life that effectively challenge one another. Considering some of the Indian classical works, such as the Yogavāsiṣṭha, in this light suggests that they too acknowledge the absurd as their starting point and, at least to a certain extent, develop doctrines that aid their practitioners in facing it. Nevertheless, the Yogavāsiṣṭha also enables us to develop our thinking about absurdity and expand the discussion of constructive responses to the reality of the absurd.

Soft computing machine learning applications for assessing regional-scale landslide susceptibility in the Nepal Himalaya

PurposeThis research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.Design/methodology/approachUsing the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).FindingsThe validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.Research limitations/implicationsLand cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.Practical implicationsIt helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.Social implicationsThe social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.Originality/valueLandslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

Assessing the relationship between landslide susceptibility and land cover change using machine learning

Landslides are natural disasters most frequent in the mountain region of Vietnam, producing critical damage to human lives and assets. Therefore, precisely identifying the landslide occurrence probability within the region is essential in supporting decision-makers or developers in establishing effective strategies for reducing the damage. This study is aimed at developing a methodology based on machine learning, namely Xgboost (XGB), lightGBM, K-Nearest Neighbors (KNN), and Bagging (BA) for assessing the connection of land cover change to landslide susceptibility in Da Lat City, Vietnam. 202 landslide locations and 13 potential drivers became input data for the model. Various statistical indices, namely the root mean square error (RMSE), the area under the curve (AUC), and mean absolute error (MAE), were used to evaluate the proposed models. Our findings indicate that the Xgboost model was better than other models, as shown by the AUC value of 0.94, followed by LightGBM (AUC=0.91), KNN (AUC=0.87), and Bagging (AUC=0.81). In addition, urban areas increased during 2017-2023 from 25 km² to 30 km² in very high landslide susceptibility areas. Our approach can be applied to test the other regions in Vietnam. Our findings might represent a necessary tool for land use planning strategies to reduce damage from natural disasters and landslides.

Korelasi Sains Dan Teknologi Dengan Alkitab Dan Iman Kristen Di Tengah Kehidupan Orang Percaya

Science and technology are often opposed to the Bible and faith, both by Christians and non-Christians. Many people think that the Bible and Christian faith have no correlation. Even more so, secular people tend to despise the values ​​contained in the Bible and the Christian faith. Various methods and methods were developed and carried out to doubt and discredit the Bible and the Christian faith. Moreover, in the current context, the Bible and the Christian faith are seen as outdated and no longer relevant to the current context of human life. They think that the Bible and the Christian faith cannot answer human needs today because they have no correlation at all. To answer the view above, the researcher attempts to show that in the real world, both in the past and in the present, the Bible and the Christian faith remain relevant to the context of human life in all times. That the Bible and Christian faith have a close correlation with science and technology. The truth of science and technology is not only supported by the Bible, but the Bible can also be used as a foundation in efforts to think and search for truth in science and technology.This research was conducted using qualitative methods and literature study, namely research with descriptive analytical presentation. Data was compiled and obtained through literature studies sourced from research results, books and journals which were then analyzed descriptively and presented in writing in the form of a scientific journal about The Correlation Of Science And Technology With The Bible And Christian Faith In The Lives Of Believers.

Taste and its receptors in human physiology: A comprehensive look

AbstractIncreasing evidence shows that food has significance beyond traditional perception (providing nutrition and energy) in maintaining normal life activities. It is indicated that the sense of taste plays a crucial part in regulating human life activities. Taste is one of the basic physiological sensations in mammals, and it is the fundamental guarantee for them to perceive, select, and ingest nutrients in order to survive. With the advances in electrophysiology, molecular biology, and structural biology, studies on the intracellular and extracellular transduction mechanisms of taste have made great progress and gradually revealed the indispensable role of taste receptors in the regulation and maintenance of normal physiological activities. Up to now, how food regulates life activities through the taste pathway remains unclear. Thus, this review comprehensively and systematically summarizes the current study about the sense of taste, the function of taste receptors, the taste–structure relationship of gustatory molecules, the cross‐talking between distinctive tastes, and the role of the gut–organ axis in the realization of taste. Moreover, we also provide forward‐looking perspectives on taste research to afford a scientific basis for revealing the scientific connotation of taste receptors regulating body health.

Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts' total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards.