Volume 14 | Issue 5 |

 Abstract

Mental health disorders such as depression, anxiety, and stress are increasing rapidly due to modern lifestyle changes, academic pressure, and workplace stress. Early identification of mental health conditions is essential for proper treatment and support. This paper presents a Mental Health Prediction System using Machine Learning techniques to predict mental health conditions effectively. The proposed system uses preprocessing techniques, feature extraction, and classification algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine for prediction. The experimental analysis shows that Random Forest achieved the highest prediction accuracy. The developed system can help healthcare professionals and institutions in early mental health assessment.

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 Keywords

- Mental Health, Machine Learning, Depression Prediction, Anxiety Detection, Artificial Intelligence

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????????? ???????? ?????? ?????? ??? ?? ???? ?????? ????????? ??? ?? ?? ?? ??? ??? ???? ??, ?? ???? ?? ??????? ?? ???? ?? ??? ?? ?? ????????? ??????? ?? ?????? ?? ???? ??? ??? ??? ??? ?? ??? ???? ????????? ???????? ?? ??????? ?? ??????? ?? ??? ????? ??, ??? ?? ???? ???? ?? ?? ??????? ?? ???? ??, ?????? ?? ?????? ?? ???????? ??? ?????? ?????? ??? ??? ????? ????????? ???????? ?? ?????? ???????? --?????????? ?? ???????? ?? ??????? ????? ??? ?????? ?? ???? ????? ?? ??????? ???? ?? ?????? ??-- ?? ????? ?? ?? ??, ?????? ?? ???????? ?? ????? ?? ?? ?????? ???? ?? ????????? ??????? ??? ??? ?? ??? ??? ????? ??? ???? ?? ???? ?????, ????? ?? ??????-????? ???? ????? ???? ???????? ????????? ??????? ?? ?? ??? ?? ????? ???? ??, ?? ?? ??? ?? ?????? ??? ?????? ??? ?? ????? ?? ????? ??? ????????, ???????????, ??? ?? ???? ???? ?? ???? ?? ?? ????????????? ????? ???????, ???? ???????? ?? ????? ???????? ???? ???????? (???????) ?? ?????? ???? ???? ?? ?????? ????? ?? ???????? ????, ?????? ??? ????? ?? ???????? ?? ?????? ??????? ?? ?????? ????? ??? ?????? ?????? ?? ???, ??? ???????? ?? ????? ?????? ????? ?? ??? ??? ????????? ??????? ???????, ????? ??????? ?? ?????? ??????? ?? ????? ?? ??? ???? ??? ???????? ????? ??? ?? ??????? ??????? ?????? ???, ????? ???????? ???????? ???????? ?? ???????? ?? ?????? ???? ??? ????? ??--???? ??????????, ???????? ?? ????? ?????????? ????????? ???? ?? ?????? ??? ?? ???? ??? ?????, ?? ???? ??? ??????????, ?????? ?? ???? ?????? ??????? ?????? ?? ?????? ???? ??? ??, ???? ????????? ???????? ?? ???? ?? ????? ????????? ?? ????? ??????? ?? ????? ?????????? ?? ?????? ????? ?? ??? ?

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????????? ????????, ??? ?????, ??????, ??????? ????? ?

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This project presents an automated waste segregation system that integrates Arduino technology with machine learning for classifying waste into three categories: metal, plastic, and glass. The system uses inductive sensors, ultrasonic sensors, and a camera module for detection and classification. Metallic objects are identified using the inductive sensor, while non-metallic items are analyzed through image processing and classified using a trained machine learning model. The Arduino microcontroller controls servo motors to sort the items into the appropriate bins. This combination of automation and artificial intelligence improves waste segregation accuracy, reduces manual labor, and promotes sustainable recycling practices.

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o To develop an automated system for segregating waste into metal, plastic, and glass categories. o To integrate sensors (inductive, IR, vibration/piezo) with an Arduino microcontroller for material detection. o To use machine learning algorithms to improve the accuracy of waste classification

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The present study was undertaken to examine the effect of phenthoate (50% EC), an organophosphate pesticide, on the respiratory metabolism of the freshwater fish Ctenopharyngodon idella. Oxygen consumption was used as an indicator to evaluate the physiological stress caused by sublethal and lethal exposure over a period of 24 hours. At the beginning of the experiment (0 h), no noticeable variation in oxygen consumption was observed between the control and treated groups. In the sublethal exposure group, oxygen consumption showed an initial increase and reached a peak at 4 h (17.92%), followed by a gradual decline during the later stages of exposure. In the lethal exposure group, oxygen consumption decreased continuously throughout the experimental period, with the maximum reduction observed at 22 h (37.98%). The decline in oxygen consumption suggests respiratory stress and disturbance in normal metabolic activity due to phenthoate exposure. The findings of the present study indicate that phenthoate can significantly affect the respiratory physiology of Ctenopharyngodon idella, particularly under lethal exposure conditions. The study also suggests that changes in oxygen consumption may serve as a useful biomarker for assessing pesticide-induced stress in aquatic organisms.

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Phenthoate (50% EC), Ctenopharyngodon idella, oxygen consumption, respiratory metabolism, organophosphate pesticide, aquatic toxicology

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Early problem identification is highly important as it can help guarantee effective process functioning, reduce expenses and support decision-making in areas like medicine, cyber security, banking, farming, IoT technologies and many others. The previous methods involved developing rules manually, determining thresholds and using elementary statistical methods that turned out to be insufficient to work with huge volumes of complex data. Artificial intelligence transformed this area with deep learning gaining unprecedented popularity and productivity. The computer algorithms are now able to analyse raw data without any human intervention and identify some patterns and anomalies that cannot be detected by humans. Also, the models based on deep learning can generate an extra layer of analysing numerous fragments of information allowing detecting unusual behaviour much faster than with traditional methods. It appears there are plenty of terms that should be familiar to you including CNNs, RNNs, ANNs and LSTMs, and many more models used to predict events, detect anomalies and patterns, and perform feature analysis in real life. This research paper will examine the use of artificial intelligence in identifying problems at their early stages paying attention to innovations in deep learning algorithms in particular.

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AI Based Early Detection Using Deep Learning

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The ability to notice the initial signs of potential problems can play a key role, as it can ensure effective operation of the process itself, minimize costs and improve the decision making process within the sphere of healthcare, cyberspace, finance, agriculture, IoT devices and much more. In previous approaches the rules needed to be created manually, threshold value to be set up and basic statistical methods were utilized which proved ineffective for large amounts of complex data. The emergence of artificial intelligence revolutionized the industry bringing the idea of deep learning to the new level of popularity and efficiency. Today's computer-based algorithms are capable of analyzing vast amounts of data independently, revealing patterns and anomalies invisible to humans. Moreover, due to the deep learning models the data can be additionally analyzed with the aim of quicker detection of abnormalities. There are numerous concepts that one must be aware of such as CNN, RNN, ANN and LSTMs, not mentioning other deep learning-based models utilized to forecast events, detect problems and analyze features in real-life situations. This research paper aims to explore how artificial intelligence helps detect issues during the very early stages focusing on innovative deep learning techniques. In this paper, there are several parts that discuss neural networks that have been employed by deep learning techniques. The math processes that help train neural networks, the structure and activation function of neural networks, along with ways of improving training processes in terms of efficiency will be examined. In addition, there are some popular data sets used by researchers, software packages and their advantages and disadvantages, along with the issues related to deep learning technology today. Moreover, feature learning via deep learning can bring about better prediction results, while large data management becomes easy with artificial intelligence.

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AI Based Early Detection Using Deep Learning

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The development of digital infrastructures has led to computer networks becoming an integral part of modern societies. Digital infrastructures serve many roles in organizations, including facilitating communication, conducting monetary transactions, storing vital data, and managing businesses. With the wide use of digital infrastructures, the probability of cyber-attacks targeting confidentiality, integrity, and availability of information systems has risen. Traditional security applications primarily use static rules and manually developed attack signatures to detect cyber-attacks. This method does not easily adapt to emerging attacks on computer networks. Hackers are always changing their tactics to avoid detection, hence rendering traditional detection methods ineffective. The growth in both volume and complexity of data in computer networks necessitates the need for automated systems to detect malicious activities. Machine learning offers a smart approach by detecting data patterns and unusual behaviour in data streams. Machine learning models undergo constant learning and enhance their ability to detect malicious activities through continuous training. This research will focus on the application of machine learning models in detecting cyber-attacks and malicious activities.

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Intelligent Cyber Attack identification using Machine Learning Techniques

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The rapid expansion of digital technologies has significantly increased the dependency of organizations on computer networks and internet-based services. While these technologies improve efficiency and connectivity, they also introduce serious cybersecurity risks. Cyber attacks such as malware, phishing, ransomware, and denial-of-service attacks continue to evolve in complexity, making traditional rule-based security systems less effective. Conventional intrusion detection systems rely heavily on predefined signatures and manual monitoring, which limits their ability to detect new or unknown threats. Machine Learning (ML) has emerged as a promising solution to enhance cybersecurity systems by enabling automated analysis of network data and identification of abnormal behaviour patterns. ML algorithms can learn from historical data and detect suspicious activities that may indicate potential cyber attacks. This research paper explores the use of machine learning techniques for cybersecurity threat detection. It examines different machine learning algorithms, discusses data preprocessing methods, and analyses the effectiveness of ML models in detecting malicious activities within network traffic. The study highlights the advantages, challenges, and practical applications of machine learning in cybersecurity systems. The proposed approach aims to improve detection accuracy, reduce false alarms, and strengthen the overall security infrastructure of modern digital environments.

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Cybersecurity Threat Detection using Machine Learning

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Pterygium is an ocular surface disease requiring accurate diagnosis and severity assessment for effective clinical decision-making; however, existing methods often lack detailed analysis and interpretability. This paper presents PteriGrade-Net, an explainable multimodal deep learning framework designed for automated pterygium detection and ordinal severity grading. The model integrates anterior-segment image processing, clinical features, and quantitative biomarkers. It employs advanced preprocessing followed by an Attention U-Net for lesion segmentation and biomarker extraction. These features are dynamically fused with visual representations from EfficientNet-B0 and structured clinical data using attention mechanisms to generate a unified embedding. A multi-task learning strategy optimizes three objectives: (1) binary classification (healthy vs. pterygium), (2) lesion segmentation, and (3) ordinal severity grading. Additionally, the framework enhances explainability by highlighting lesion regions and quantifying morphological characteristics, thereby improving clinical interpretability. Experimental results demonstrate superior performance compared to existing approaches in both detection and severity grading. By combining multimodal inputs with lesion-aware analysis, the proposed system aligns well with clinical workflows and offers a reliable, interpretable solution for real-world ophthalmic applications.

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 Keywords

Pterygium Detection; Multimodal Deep Learning; Lesion Segmentation; Ordinal Severity Grading; Explainable AI

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Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of reliable and early diagnostic systems. This paper proposes a hybrid machine learning framework designed to enhance the accuracy and robustness of heart disease risk prediction. The framework integrates multiple machines learning techniques, combining the strengths of both traditional classifiers and advanced ensemble methods to improve predictive performance. Initially, data preprocessing techniques such as normalization, missing value imputation, and feature selection are employed to ensure data quality and relevance. Subsequently, a hybrid model is constructed by integrating algorithms such as Decision Trees, Support Vector Machines, and Gradient Boosting, leveraging their complementary capabilities for improved classification. The system also incorporates feature importance analysis to identify key clinical indicators contributing to heart disease risk. Experimental evaluation on benchmark healthcare datasets demonstrates that the proposed hybrid approach outperforms individual models in terms of accuracy, precision, recall, and F1-score. The results highlight the potential of hybrid machine learning techniques in providing early, accurate, and interpretable predictions, thereby supporting clinicians in effective decision-making and preventive healthcare strategies.

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 Keywords

Heart Disease Prediction, Hybrid Machine Learning, Ensemble Learning, Feature Selection, Clinical Decision Support, Healthcare Analytics, Predictive Modeling, Early Diagnosis

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Creative Commons Attribution 4.0 and The Open Definition