Development or environmental sustainability? Investigating the relationship between poverty rate and carbon emissions using the K-nearest neighbor algorithm

This study contributes to the current discourse on the relationship between environmental sustainability and economic growth by examining the connections between poverty, carbon emissions, and the value generated in the agricultural sector. We assess the agricultural sectors of China and India, analyzing their roles in global carbon emissions through empirical data, econometric models, and machine learning techniques. We find a nonlinear relationship between economic growth and emissions; initially, development elevates carbon output, while subsequent progress encourages sustainability through technological innovations and policy initiatives. Among the machine learning methods employed, the K-Nearest Neighbors (KNN) algorithm demonstrated the highest predictive accuracy. The results indicate that China’s agricultural sector is the dominant contributor to carbon emissions, accounting for 78.2%, while India’s sector contributes 21.8%. These findings underscore the disparities in agricultural carbon footprints between the two nations and highlight the necessity of integrated strategies that balance economic growth with environmental protection. Policy implications involve implementing focused poverty reduction initiatives, renewable energy strategies, and sustainable farming methods to lessen the effects of climate change while promoting economic growth. The study highlights how artificial intelligence and machine learning can improve predictive accuracy and guide effective climate policies.

Background: This study introduces a prediction system for lung cancer that uses the K-Nearest Neighbors (KNN) algorithm, one of the most important cancer-related diseases worldwide. Early detection and prediction can significantly improve survival rates. This study is one of the leading causes of cancer death worldwide. Objective: This study aims to develop a prediction system for lung cancer utilizing the K-Nearest Neighbors (KNN) algorithm, addressing the critical need for early detection in combating one of the leading causes of cancer-related mortality worldwide. Method: Leveraging a dataset comprising 10,000 patient records encompassing demographics, medical history, and radiographic features, we conducted preprocessing and normalization before training, validating, and testing the KNN model. Optimal parameter selection facilitated the achievement of a 95% accuracy rate in predicting lung cancer, highlighting the efficacy of KNN in this context. Findings: Our study underscores the potential of machine learning, specifically KNN, in enhancing medical diagnostics. By integrating machine learning techniques into medical practice, we can facilitate early detection and prompt intervention, thereby potentially improving patient outcomes. Novelty: This research contributes to the ongoing integration of machine learning into medical diagnostics, particularly in the realm of cancer prediction. Our findings demonstrate the utility of KNN in accurately predicting lung cancer, thereby offering a promising avenue for enhancing early detection strategies. Result and Discussion: The effectively demonstrate the potential of the K-Nearest Neighbors (KNN) algorithm in achieving a remarkable accuracy rate of 95.0% in predicting lung cancer, as evidenced by rigorous preprocessing and optimization of a dataset comprising 10,000 patient records. The integration of machine learning techniques, exemplified by the KNN algorithm, holds significant promise for improving early detection and subsequent treatment outcomes in lung cancer. By leveraging large datasets and advanced algorithms, we can pave the way for more effective diagnostic tools in combating this devastating disease. Keywords: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Lung cancer detection, Medical imaging, Early diagnosis, Machine learning, Patient outcomes

The security and integrity of computer systems and networks highly depend on malware detection. In the realm of malware detection, the K-Nearest Neighbors (KNN) algorithm is a well-liked and successful machine learning algorithm. However, the choice of an acceptable distance metric parameter has a significant impact on the KNN algorithm's performance. This study tries to improve malware detection by adjusting the KNN algorithm's distance metric parameter. The distance metric greatly influences the similarity or dissimilarity between instances in the feature space. The KNN algorithm for malware detection can be more accurate and effective by carefully choosing or modifying the distance metric. This paper analyzes multiple distance metrics, including Minkowski distance, Manhattan distance, and Euclidean distance. These metrics account for the traits of malware samples while capturing various aspects of similarity. The effectiveness of the KNN algorithm is evaluated using the MalMem-2022 malware dataset, and the results are broken down into these three-distance metrics. The experimental findings show that, among the three distance metric parameters, the Euclidean and Minkowski distance metric parameters considerably produced the best outcomes with binary classification. While with multiclass classification, the KNN algorithm has achieved the highest outcomes using Manhattan distance.

Among the classification algorithms in machine learning, the KNN (K nearest neighbor) algorithm is one of the most frequent used methods for its characteristics of simplicity and efficiency. Even though KNN algorithm is very effective in many situations while it still has two shortcomings, not only is the efficiency of this classification algorithm obviously affected by redundant dimensional features, but also the categorization accuracy is seriously influenced by the distribution of training samples. In this paper, we proposed a stepwise KNN algorithm based on kernel methods and attribute reduction which can effectively tackle with the problems above. We calculated the accuracy rate of the proposed algorithm and compared it with basic KNN algorithm in the experiments with use of four UCI datasets. The experiment results show that the stepwise KNN algorithm (denoted by SWKNN) performs better than the original KNN algorithm with the improvement of average 13.8% in accuracy.

The article presents a detailed exploration of using the k-Nearest Neighbors (KNN) algorithm to classify and identify various types of cyberattacks, particularly SQL Injection (SQLi) attacks, within SCADA (Supervisory Control and Data Acquisition) systems. This work addresses the need to enhance server infrastructure security in SCADA systems by mitigating the risks posed by harmful requests, such as SQL injections. SCADA systems are crucial in managing industrial processes, making their servers prime cyberattack targets. Attackers often exploit vulnerabilities in server applications by injecting malicious requests through input fields or URLs, potentially gaining access to sensitive data or disrupting system operations. To address this issue, the study proposes a machine learning-based approach using the k-nearest neighbors (KNN) algorithm to detect and block harmful SQL requests. The KNN algorithm is employed to classify and identify different types of cyberattacks by comparing new attack attempts with previously observed attack patterns. By analyzing specific attributes related to each attack, the KNN method evaluates the level of threat based on proximity metrics. The proposed approach helps classify SQL injection attempts, which involve manipulating SQL code to bypass authentication or extract unauthorized data. The study demonstrates how KNN can effectively distinguish harmful SQL requests from benign ones by calculating the Euclidean distance between the new attack and historical cases. Furthermore, the article emphasizes the importance of implementing rapid and accurate detection methods for protecting server infrastructure in industrial environments. The KNN algorithm, in this context, offers a flexible and efficient solution as it adapts to various attack scenarios, improving the overall resilience of SCADA systems to cyber threats. The study’s findings contribute to the ongoing efforts in cybersecurity, focusing on integrating machine learning models to strengthen the protection of critical assets in industrial control systems. This work aims to develop protection tools for server-based industrial control systems used in SCADA systems against dangerous requests based on SQL injections, using an ML-trained model for blocking harmful requests through the k-nearest neighbors method.

Abstract. Cervical cancer impacts the female reproductive organs and stands as the second most common cancer among women worldwide. The World Health Organization (WHO) reports that annually, approximately 500,000 women are diagnosed with cervical cancer, and about 300,000 die from it. Many of these deaths result from insufficient early detection and preventive measures. There are four primary screening techniques for detecting cervical cancer cells: Hinselmann, Schiller, Cytology, and Biopsy. In this study, patient health history data is analyzed using the KNN algorithm, which is further optimized with Adaboost and PSO techniques. These optimization strategies are evaluated to identify the most precise model for detecting patterns in cervical cancer patients and predicting their screening outcomes. This study employs the RapidMiner tool. Findings reveal that the KNN algorithm effectively performs multilabel classification, and when optimized with PSO, there is a slight improvement in accuracy.Purpose: The aim of this research is to assess the performance of the K-Nearest Neighbor (KNN) algorithm in multilabel classification of cervical cancer and to optimize it using Adaboost and Particle Swarm Optimization (PSO) techniques. This research is significant as it offers a potentially more accurate diagnostic method for detecting cervical cancer using medical records.Methods/Study design/approach: The Cervical Cancer Risk Classification dataset from Kaggle was used in this study. Data preprocessing was conducted before applying the KNN algorithm. The KNN algorithm's performance was evaluated using a 10-fold cross-validation method, and results were measured using the Confusion Matrix. Additionally, the KNN algorithm was optimized using Adaboost and PSO to assess improvements in accuracy.Result/Findings: Experimental results indicated that the KNN algorithm achieved optimal accuracy with k=5, reaching 95.81%, 91.26%, 94.64%, and 93.01% for Hinselmann, Schiller, Cytology, and Biopsy targets, respectively. Adaboost did not significantly improve accuracy, while PSO slightly enhanced the Hinselmann target accuracy from 95.81% to 95.92%. The average training time for this experiment was around two minutes. These results demonstrate the effectiveness of the KNN algorithm in conducting multilabel classification for cervical cancer diagnosis.Novelty/Originality/Value: This research demonstrates that optimizing the KNN algorithm with PSO can enhance accuracy, though not significantly. This suggests potential for further development to improve cervical cancer diagnostic accuracy. Testing the model with the latest data and optimizing parameters may lead to better models and useful tools for early cervical cancer diagnosis.

در این مطالعه به منظور برآورد شاخص های عملکرد هیدرولیکی سیستم آبیاری تحت فشار، جایگزینی مدل های هیدرولیکی با استفاده از مدل-های آماری و شبکة عصبی مصنوعی مورد ارزیابی قرار گرفت. ضریب یکنواختی کریستیانسن به عنوان شاخص عملکرد هیدرولیکی، در نظر گرفته شد و با استفاده از یک الگوریتم، مقادیر این شاخص به صورت تابعی از ترکیب های مختلف فشار ورودی، تعداد خروجی ها، فاصلة خروجی ها، ضریب زبری لوله، قطر داخلی، شیب، دبی اسمی خروجی ها، فشار کارکرد خروجی ها و توان معادلة دبی خروجی ها محاسبه گردید (4320 ترکیب مختلف). دو مدل شبکة عصبی مصنوعی شامل شبکة پرسپترون چندلایه (MLP) و شبکة رگرسیون تعمیم‌یافته (GRNN) و نیز روش K نزدیک ترین همسایه (KNN) به عنوان یک مدل رگرسیون ناپارامتری برای شبیه سازی شاخص عملکرد هیدرولیکی لاترال ها مورد آزمون قرار گرفتند. نتایج نشان داد که مدل شبکة عصبی مصنوعی MLP قادر است با کمترین خطا (3-2%) مقادیر ضریب یکنواختی لاترال های آبیاری تحت فشار را با استفاده از مشخصات هیدرولیکی و فیزیکی لاترال برآورد نماید. عملکرد مدل GRNN نیز به ویژه در ارتباط با کل داده ها در سطح نسبتا مطلوبی ارزیابی گردید. اما روش KNN علیرغم شبیه سازی دقیق متوسط مقادیر CU، قادر به شبیه سازی دقیق انحراف استاندارد این مقادیر نبوده و خطای آن در مرحلة آزمون بسیار بیشتر از دو مدل دیگر برآورد گردید. در روش KNN، کمترین مقدار شاخص های خطا مربوط به رگرسیون ناپارامتری با 10 و 15 همسایه می‌باشد. نتایج این مطالعه نشان داد که امکان ساده سازی مدل های پیچیدة هیدرولیکی با جایگزینی کل یا بخشی از این مدل ها با مدل-های ساده تر آماری و شبکه عصبی وجود دارد و این مسئله با توجه به پیچیدگی مدل های هیدرولیکی، به ویژه در فرآیند بهینه سازی سیستم های آبیاری، می تواند مورد توجه قرار گیرد.

Effectively addressing today’s energy challenges requires advanced technologies along with policies that influence economic markets while advancing the public good.

The Study of Handwritten Character Prediction: Comparative Analysis Between K-Nearest Neighbour and Naive Bayes Algorithms This research aims to predict handwritten characters by employing the K-Nearest Neighbour (KNN) algorithm and comparing its feature extraction precision with the Naive Bayes (NB) algorithm. Both algorithms were evaluated using a sample size of n = 25 and iterated 15 times to enhance accuracy and reduce errors, with a statistical power of 80% (G power) and an alpha value of 0.05.The experimental results demonstrated a significant improvement in the accuracy of handwritten character recognition, achieving a significance value of 0.165 through the comparison of NB and KNN algorithms. Comparative analysis revealed that the NB algorithm (97.45) outperformed the KNN algorithm (97.3). These findings highlight the superiority of the NB algorithm in predicting handwritten characters over the KNN algorithm.

In English writing, English learners will inevitably make a variety of grammatical mistakes, especially in English subordinate clause connective. To alleviate high error rate of connective in subordinate clauses of Chinese students' English writing, an automatic error correction model for English subordinate clause connective is studied and implemented from the perspective of machine learning — genetic algorithm (GA) and k-nearest neighbor (KNN) algorithm combination model. Firstly, an automatic feature selection algorithm based on GA is adopted to reduce time consuming and space cost, and to improve the accuracy of connective error correction. Secondly, through comparing the Naive Bayes, decision tree, maximum entropy and KNN algorithm, KNN algorithm is found better while classifying the connectives. Finally, we compared the performance of several hybrid models, which combine different machine learning algorithms with GA. This proves that the combination of GA and KNN algorithm is optimal.

The K-Nearest Neighbor (KNN) algorithm is a classical machine learning algorithm. Most KNN algorithms are based on a single metric and do not further distinguish between repeated values in the range of K values, which can lead to a reduced classification effect and thus affect the accuracy of fault diagnosis. In this paper, a hybrid metric-based KNN algorithm is proposed to calculate a composite metric containing distance and direction information between test samples, which improves the discriminability of the samples. In the experiments, the hybrid metric KNN (HM-KNN) algorithm proposed in this paper is compared and validated with a variety of KNN algorithms based on a single distance metric on six data sets, and an HM-KNN application method is given for the forward gait stability control of a bipedal robot, where the abnormal motion is considered as a fault, and the distribution of zero moment points when the abnormal motion is generated is compared. The experimental results show that the algorithm has good data differentiation and generalization ability for different data sets, and it is feasible to apply it to the walking stability control of bipedal robots based on deep neural network control.

The Hu-Bao-O-Yu urban agglomeration is an important energy exporting and high-end chemical base in China, and is an important source of carbon emissions in China. The early achievement of peak carbon emissions in this region is particularly crucial to achieving the national carbon emission reduction targets. However, there is a lack of multi-factor system dynamics analysis of resource-dependent urban agglomerations in Northwest China, as most studies have focused on single or static aspects of developed urban agglomerations. This paper analyses the relationship between carbon emissions and their influencing factors, constructs a carbon emission system dynamics model for the Hu-Bao-O-Yu urban agglomeration, and sets up different single regulation and comprehensive regulation scenarios to simulate and predict the carbon peak time, peak value, and emission reduction potential of each city and urban agglomeration under different scenarios. The results show that: (1) Hohhot and Baotou are expected to reach peak carbon by 2033 and 2031 respectively, under the baseline scenario, while other regions and the urban agglomeration will not be able to reach peak carbon by 2035. (2) Under single regulation scenarios, the effect of factors other than the energy consumption varies across cities, but the energy consumption and environmental protection input are the main factors affecting carbon emissions in the urban agglomeration. (3) A combination of the economic growth, industrial structure, energy policy, environmental protection, and technology investment is the best measure to achieve carbon peaking and enhance the carbon emission reduction in each region as soon as possible. In the future, we need to coordinate the economic development, energy structure optimisation and transformation, low-carbon transformation of industry, strengthen research on carbon sequestration technology, and further increase the investment in environmental protection to make the Hu-Bao-O-Yu urban agglomeration a resource-saving urban agglomeration with an optimal emission reduction.

Can we live within environmental limits and still reduce poverty? Degrowth or decoupling?

Renewable energy, carbon emission and economic growth: A revised environmental Kuznets Curve perspective

Education is important nowadays because universities need to improve their students' skills so they can compete in the globalization era. Education can be obtained through both formal and informal channels, and knowledge is available everywhere, especially in today's world where information tools are rapidly evolving. Inactive students are students who do not participate in a course for a maximum of two consecutive semesters. Students who are not active have the opportunity to drop out of university studies. Students who drop out of college are usually motivated by economic factors, and the cessation of the lecture process can cause inactivity and administrative costs. Therefore, this research was conducted using the C4.5 algorithm method and the K-Nearest Neighbor (KNN) algorithm to compare and predict data on inactive students at Universitas Prima Indonesia. The research continued with the data collection and data preprocessing stages, after which the data mining process was carried out to get the final results of this research. The testing process follows the process of comparing the C4.5 algorithm and the K-Nearest Neighbor (KNN) algorithm with K-fold crossing. This evaluation step is compared by considering the comparison values of the confusion matrix (precision, precision, recall). The accuracy results obtained by each algorithm provide information about the effectiveness of using these techniques in processing the specified dataset. The accuracy of the Decision Tree C4.5 algorithm is 99.12% and the K-Nearest Neighbors algorithm is 99.14%. Based on research conducted using the K-Nearest Neighbors and C4.5 algorithms to predict inactive students, the KNN algorithm is more accurate than the C4.5 algorithm.