Volume 12 | Issue 7 |

 Abstract

The early and accurate detection of plant diseases is crucial for ensuring agricultural productivity and food security. Traditional methods of disease detection are often labor-intensive, time-consuming, and prone to human error. In recent years, machine learning (ML) has emerged as a powerful tool to enhance the precision and efficiency of plant disease detection. This paper presents a comprehensive review of the application of machine learning techniques in identifying plant diseases. We explore various ML algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), and random forests, and their roles in analyzing complex plant disease patterns from diverse data sources such as images, spectral data, and environmental factors. The integration of ML with advanced imaging technologies and the Internet of Things (IoT) enables real-time monitoring and rapid diagnosis, significantly improving response times and reducing crop losses. We discuss the challenges associated with implementing ML solutions in agricultural settings, such as data acquisition, model training, and scalability. Additionally, we highlight case studies and recent advancements that demonstrate the effectiveness of ML in disease detection across different types of crops. Our findings underscore the potential of machine learning to revolutionize plant disease management, paving the way for more resilient and sustainable agricultural practices.

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 Keywords

Plant disease detection, machine learning, convolutional neural networks (CNNs), support vector machines (SVMs), random forests, agricultural technology, real-time monitoring, IoT, precision agriculture, crop management.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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: A difficult threat known as "crypto-ransomware" encrypts user files and demands a ransom to be paid by the victim in order to obtain the decryption key. Cybercriminals can make a significant profit from this kind of malware, as it generates millions of dollars in income annually. Due to the inefficiency of traditional detection-based defenses like antivirus and anti-malware software in thwarting attacks, ransomware is becoming increasingly widespread. Moreover, this particular infection employs advanced encryption algorithms and targets a broader spectrum of file formats. Cybercriminals benefit from this, and no one is safe from becoming their next victim. File encryption ransomware targets both small and large enterprises as well as regular home users. In this research, we look at how ransomware spreads by analyzing the factors that put an individual or an organization at risk of falling victim to its demands. Finally, we make some recommendations based on our projections regarding the future evolution of ransomware.

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Ransomware, Cybersecurity, Antivirus, Malware, Ransomware prevention, Ransomware detection

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Creative Commons Attribution 4.0 and The Open Definition

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Recent decades have seen a transformation in the tobacco industry thanks to the electronic cigarette, or "e-cigarette," which has been viewed as an effective replacement for standard cigarettes. Because e-liquid heats as opposed to burning tobacco in e-cigarettes, several manufacturers have claimed that smoking e-cigarettes have fewer adverse respiratory effects than utilizing tobacco. Many customers have been won over by additional revolutionary characteristics including the option to customize the nicotine dosage and the variety of delicious flavours. But since there is no proof to support either position, there is still controversy concerning the safety of e-cigarette use and its ability to serve as a way to quit smoking strategy. Furthermore, the heating process itself has been found to have the potential to produce distinct, potentially hazardous breakdown products. Multiple investigations have been conducted both in vivo and in vitro for better insight of the effects.

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 Keywords

Electronic Cigarettes, Cessation, Tobacco, Battery-powered, Nicotine-replacement

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Creative Commons Attribution 4.0 and The Open Definition

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Half-Heusler semiconductor alloys have emerged as a compelling class of materials renowned for their extraordinary electronic, thermoelectric, and magnetic properties. This research paper provides a comprehensive analysis of Half-Heusler alloys, encompassing their crystal structure, electronic band properties, physical characteristics, synthesis methodologies, and potential applications. Computational simulations employing Wien2k software and DFT (Density Functional Theory) have been utilized extensively to elucidate these properties. These alloys, typically denoted by the chemical formula XYZ where X, Y, and Z represent different elements, exemplify the versatility of composite materials. Specifically, the CoVSn alloy, comprising cobalt (Co), vanadium (V), and tin (Sn), showcases synergistic properties that surpass those of its individual constituents. The crystal structure of Half-Heusler alloys, often cubic with XYZ ordering, contributes to their robust mechanical stability and favorable electronic band structures for various applications in solid-state electronics and thermoelectric devices. Moreover, their tunable electronic properties, dictated by the choice and arrangement of constituent elements, offer a promising avenue for designing materials with tailored functionalities. Advances in synthesis techniques, including vacuum arc melting and spark plasma sintering, have enabled precise control over alloy composition and microstructure, further enhancing their applicability in high-performance electronic and energy conversion devices. Ongoing research continues to explore novel Half-Heusler compositions and nanostructuring approaches to optimize their thermoelectric efficiency and magnetic properties, promising breakthroughs in sustainable energy and advanced electronics.

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Half-Heusler Alloy, Thermo-electric Material, Semiconductor Alloy, Composite Material.

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Creative Commons Attribution 4.0 and The Open Definition

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The Dental-X project is an integrated platform designed to streamline dental appointment management and enhance the accuracy of dental X-ray image classification. In many dental practices, using separate systems for image capture and patient management leads to inefficiencies and potential errors in data transfer. Dental-X addresses these challenges by combining image classification and appointment management functionalities into a single, user-friendly platform.The primary objective of Dental-X is to provide a comprehensive solution for dental professionals to manage their appointments, analyze X-ray images for caries detection, and perform tooth segmentation. This platform allows dentists to set their availability, manage their profiles, and view patient histories. Patients can register, book appointments, select time slots, and view past treatments and medications.Dental-X leverages machine learning models to analyze dental X-ray images, assisting dentists in detecting caries and identifying fillings, cavities, or implants. By integrating these features into one platform, Dental-X aims to improve the efficiency and accuracy of dental practices, ultimately enhancing patient care and streamlining administrative processes .Key features of Dental-X include appointment scheduling, slot availability checking, caries detection through image analysis, tooth segmentation, and comprehensive patient history management. The platform also provides statistical insights for dentists to track and analyze treatment outcomes. Dental-X is designed to be a robust, scalable, and user-friendly solution that meets the needs of modern dental practices.By integrating advanced machine learning techniques with efficient appointment management, Dental-X aims to set a new standard in dental practice management, offering both convenience and precision to dental professionals and their patients.

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Machine Learning, Administrative Efficiency, Image Analysis, Statistical Insights.

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Creative Commons Attribution 4.0 and The Open Definition

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The purpose of this research is to solve this constraint by designing a transdermal drug delivery system for quercetin that may be administered once daily. This system will provide greater patient compliance, increased bioavailability, and prolonged release compared to other methods. On the other hand, its therapeutic value is restricted due to difficulties associated with poor solubility, low bioavailability, and unpredictable pharmacokinetics when it is administered orally. For the purpose of determining drug flow and permeation parameters, skin permeation experiments are carried out. This is followed by the use of in vitro or in vivo models for the purpose of evaluating the potential for skin irritation caused by the prepared patches. The study aims to achieve several objectives: to formulate a transdermal delivery system capable of sustained release of quercetin over 24 hours. A once-daily transdermal delivery system for quercetin that has sustained release properties is one of the outcomes that is anticipated. The formulation included namely dichloromethane and methanol in a ratio of 1:1, using a method called solvent evaporation. Therefore, linseed oil and eugenol were added to the polymer solution in order to improve the penetration. Polyethylene glycol was employed as a plasticizer, and menthol was added in order to counteract the irritation caused by the polymer. Following the preparation of the films in this manner, they attached to the layer of adhesive bandage that had been acquired from the neighbourhood store.

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Storage conditions, Accelerated aging, adhesive properties, Drug content uniformity, Controlled release.

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Objective: Khashkhash (Papaver somniferum seed) is one of the important drug mentioned in Unani literature. The description of Poppy seeds is mentioned in various classical Unani books. Renowned Unani scholar Avicenna had also described poppy seeds in his famous book "The Canon of Medicine". According to Unani philosophy, Khashkhash is in the second-degree category according to its temperamental nature. Poppy seed oil is also a good source of essential fatty acids, especially linoleic acid, as compared to the other edible oilseeds. Pantothenic acid in poppy seeds was found in the greatest amount, followed by the vitamin's niacin and thiamin. Methods: Unani classical literature was searched from recent to past available in different libraries. For phytochemistry, pharmacology and preclinical studies to prove the importance computerized databases such as Medline, PubMed, Google Scholar and Science-direct were searched. All the information of plant available in Urdu, Persian, Arabic and studies published abstract were included in the study. Results: The action of Khashkhash mentioned in Unani classical literature are Mukhaddir (Anesthetic), Q?bi? (Astringent), Munawwim (Sedative), Musakkin-i-Alam (Analgesic), H?bis-i-Dam (hemostyptic), Muqaww?-i-Dim?gh (Brain tonic), Mun?ij (concoctive), Mudirr-i-Bawl (Diuretic), Mu?arrik (Stimulant), H??im (Astringent), Mubarrid (Refrigerant) etc and useful in Sahar (Insomnia), Malankh?liya (Melancholia), Nafth al-Dam (Haemoptysis), Ish?l (Purgation), Zah?r (Dysentery), Nazla (Catarrh), Su'?l(Cough), Hirqatul Math?na (Burning in bladder) etc. Papaver somniferum seed showed many pharmacological effects included Antitussive, Anti-diarrheal, Antibacterial activity, Hypoglycemic, Anticarcinogenic and Antiallodynic activity activities in different clinical and experimental studies. Conclusion: This presentation is an attempt to showcase the action, uses mentioned in Unani literature, chemical constituent and pharmacological and toxicological effects in one place. It may also be observed that the drug has many actions which may be beneficial in diseases.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Khashkhash, Papaver somniferum seed, Unani medicine

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This compilation analyzes the application of artificial intelligence in web indexing, covering diverse sectors including crawler classifications, profound learning models, insightful frameworks, and moral contemplations. This investigation features key discoveries identifying with the viability of unique crawler sorts, the utilization of factual models for anticipating out connections, and the advancement of occasion focused looking frameworks. The audit additionally stresses the significance of clearness, pertinent data, achievability, quantifiability, and good conduct consistency in web scraping ventures. All in all, this wide ranging assessment shows the huge potential for AI to upgrade web looking and information investigation for an assortment of uses. The report incorporates both long, intricate passages and short, direct sentences to catch various points of view on this creating field.

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Crawler, CAIMANS, NLP

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Creative Commons Attribution 4.0 and The Open Definition

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Accurately predicting taxi ride durations is crucial for optimizing the efficiency of taxi dispatch systems in ride-hailing services. This research aims to develop and evaluate predictive models that estimate taxi ride durations before the start of the trip, leveraging historical ride data. Various machine learning techniques, including linear regression, K-Nearest Neighbors (KNN), Ridge regression, and Lasso regression, are explored using data from New York City taxi trips.The study aims to understand the key factors influencing ride durations and build models that can provide accurate estimates. Accurate predictions can improve dispatch decisions, reduce idle times for drivers, and decrease wait times for passengers, thus enhancing operational efficiency and service quality in the ride-hailing industry.We collect a comprehensive dataset of taxi trips, enriched with external data such as weather conditions and traffic information. The data undergoes preprocessing steps like cleaning, normalizing, and feature engineering. Key variables influencing ride durations include trip distance, pickup time, day of the week, weather conditions, and traffic patterns.The modeling approach involves developing predictive models, including a benchmark linear regression model, a KNN model, and regularized linear models such as Ridge and Lasso regression. Hyperparameter tuning optimizes model performance, and cross-validation ensures robustness. Performance is evaluated using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared. The linear regression model serves as a baseline, capturing some variability in ride durations but limited in modeling non-linear interactions. The KNN model improves performance by considering data point similarity, while Ridge and Lasso regression models enhance accuracy by incorporating regularization terms. Lasso also performs feature selection by setting some coefficients to zero.This study highlights the importance of feature engineering and real-time data integration. Future research should focus on integrating real-time data sources, such as live traffic updates and weather conditions, to improve prediction accuracy further. Advanced machine learning techniques, such as ensemble methods and deep learning, should be explored to capture more complex relationships in the data.Ethical considerations and data privacy are critical in the use of predictive models in ride-hailing services. Ensuring passenger and driver privacy, and maintaining transparency and fairness in algorithmic decision-making, are essential for responsible use of data-driven models.

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 Keywords

data science, Machine learning, Taxi duration prediction

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Creative Commons Attribution 4.0 and The Open Definition