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Machine Learning for Forecasting Depression and Anxiety in University students 1

8th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Biswas, Tamal Bhattacharya, Diptendu Rudrapal, Dwijen Roy, Srijan National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India M Tech Student National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

The timely identification of mental health issues enables experts to more effectively provide treatment and enhance the well-being of patients. Mental health pertains to an individual’s emotional, mental, and interpersonal state, influencing their thoughts, emotions, and behaviors. It remains crucial across all life phases, spanning childhood, adolescence, and adulthood. Historically, categorizing mental health problems among college students demanded significant effort and time from psychologists. This research engaged five machine learning methods to classify such issues swiftly. The effectiveness of these methods was evaluated based on different standards. The five methods included logistic regression, KNN classification, decision tree classification, random forest, and stacking. A comparison and implementation of these methods revealed that stacking yielded the highest accuracy, predicting 82.20. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： students

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Marine Robot for Underwater Surveillance

Marine Robot for Underwater Surveillance

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2025 IEEE International students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Vishnu Gowda, H.P. Reddy, Deepa N Gowda, Tejas B Shrikala, R. Yogitha, H.N. Bms Institute of Technology and Management Student of Department of Electronics and Communication Engineering Bengaluru560064 India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Bengaluru560064 India Bms Institute of Technology and Management Student of Department of Computer Science Engineering Bengaluru560064 India

ISBN: (纸本)9798331529833

Underwater surveillance has become a crucial area of research, particularly for marine exploration and environmental monitoring, with marine robots playing a vital role. These robots, equipped with sensors and cameras, capture real-time data and images from underwater environments, reducing the need for human intervention in hazardous areas. However, a major challenge in this field is developing reliable communication networks, as underwater environments face limitations in bandwidth, range, and signal attenuation, hindering real-time data transmission. This paper presents a simple marine robot prototype based on the Raspberry Pi Model 4B, utilizing Electronic Speed Controllers (ESCs) and brushless motors for efficient movement. The robot's body is manually designed using 3D printing technology, with PETG (polyethylene terephthalate glycol) material chosen for its water resistance, durability, and buoyancy. The design ensures the robot's stability and functionality underwater, offering a cost-effective solution for marine robotics while underscoring the need for improved underwater communication technologies. © 2025 IEEE.

关键词： Marine communication

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Comparative Analysis of Traditional and Machine Learning Based Face Recognition Models 2

Comparative Analysis of Traditional and Machine Learning Bas...

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2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Shreya, R. Mulgund, Akanksha P Hiremath, Shravani Sri Harshitha, A. Koundinya, Anjan K PES University Department of Computer Science Bengaluru India PES University Department of Computer Science and Engineering Bengaluru India BMS Institute of Technology and Management Department of Information Science and Engineering IEEE Student Branch Counsellor Bengaluru India

ISBN: (纸本)9798350314168

Face recognition technology uses methodologies to process images, register them, and compare and verify it with a set of faces it has already registered and labelled. Recent development in visual recognition showcases the immense power of utilising machine learning techniques like deep neural networks, convolutional neural networks to achieve face recognition. Based on a comprehensive literature review it was found that some of the algorithms that provide great performance metrics in a variety of conditions are MTCNN, FaceNet model, Haar cascade, LBPH, hence it was chosen to be the subject of comparison under various conditions in the paper. A machine learning model was constructed which included MTCNN for face detection and FaceNet for face *** Performance of KNN and SVM classifiers has been studied. The second model uses a combination of Haar cascade for face detection and LBPH for face recognition, a traditional face recognition technique. The results highlight that MTCNN, FaceNet and KNN perform exceptionally well. The performance of the other models are also discussed in the results and discussion section. The research not only guides researchers and practitioners in choosing suitable face recognition techniques but also offers ground for future improvements in accuracy and reliability in the field of algorithm developments. © 2023 IEEE.

关键词： Face recognition

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COVID-19 vaccine sentiment analysis using public opinions on Twitter

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Materials Today: Proceedings 2022年 64卷 448-451页

作者： Chinnasamy, P. Suresh, V. Ramprathap, K. Jebamani, B. Jency A. Srinivas Rao, K. Shiva Kranthi, M. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India Department of Management Studies M.Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India UG Student Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs. © 2022

关键词： Vaccines

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Bi-Directional Conversion Algorithm of Database Based on Artificial Intelligence 2

Bi-Directional Conversion Algorithm of Database Based on Art...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Tao, Ning Geng, Lin Xu, Chong Shenyang Jianzhu University School Of Computer Science And Engineering Liaoning Shenyang China Shenyang Jianzhu University Department Of Student Affairs Liaoning Shenyang China

ISBN: (纸本)9798350381665

The traditional database conversion algorithm is difficult to implement, expensive and difficult to popularize. The lack of conversion algorithm directly affects the result of data integration. The core problem of the mutual transformation between XML(eXtensible Markup Language) and relational data is how to construct the mapping between relational database and XML document, that is, the mapping between objects and relationships. In this paper, the element tree is used to transform the probabilistic relational data model and the probabilistic XML data model in heterogeneous databases. First, the corresponding relationship between the element tree and the unit nodes and various data models is established. Then, on this basis, the bidirectional transformation of the data model is realized through the mapping relationship between the element tree and the data. When the transaction sending frequency is 400 transactions /s, the write throughput is 89TPS and the query throughput is 185TPS. The query and write performance of the database bidirectional conversion algorithm based on artificial intelligence is better. © 2024 IEEE.

关键词： Mapping

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Machine learning based cardiovascular disease prediction

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Materials Today: Proceedings 2022年 64卷 459-463页

作者： Chinnasamy, P. Arun Kumar, S. Navya, V. Lakshmi Priya, K. Sruthi Boddu, Siva Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Bethesda Institute of Technology and Science Gwalior India Department of Electronics and Communication Engineering East Point College of Engineering & Technology Bangalore India Department of Electronics and Communication Engineering PERI Institute of Technology Chennai India UG Student Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

In past few years, one of the really tough situations in medicine is already making predictions cardiac dysfunction. Almost any moment, roughly one patient dies through myocardial infarction with in present era. Due to the difficulty of forecasting cardiac sickness, it is vital to systematize the procedure in place to minimize the risk associated with and to educate the patient well enough in preparation. The risk of cardiovascular disease in the World is tremendous. The accurate and thorough assessment of a physician's cardiovascular risk is critical for reducing the incidence and severity attack and stroke and improving cardiovascular protection. In order to solve this issue, we are proposing method to discover these heart abnormalities as soon as reasonably practicable to avert fatal consequences. The next step is to determine whether or not the user is at risk of developing cardiovascular disease. It also answers difficult problems concerning heart detection of diseases, allowing clinicians to make better clinical decisions. Medical specialists have accumulated a large number of medical data that can be analysed and important fact retrieved. Machine learning approaches can improve in the prognosis and early identification of heart illness after hypothesis testing. © 2022

关键词： Machine learning

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Necessity of Self-explainability in AI: Insights from the Existing Social Projects

Necessity of Self-explainability in AI: Insights from the Ex...

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International Conference on Next Generation Systems and Networks, BITS EEE CON 2022

作者： Jadhav, Mayur K. Laddha, Shilpa S. Kabra, Sanchit M. Ph.D. Student Department of Computer Science Engineering Government College of Engineering Maharashtra Chh. Sambhajinagar India Head of Department Information Technology Government College of Engineering Maharashtra Chh. Sambhajinagar India Computer Science Engineering BITs Pilani India

ISBN: (纸本)9789819904822

Self-explanatory artificial intelligence has lately gained prominence as researchers and practitioners strive to make their methodologies more understandable. It is not contentious to assert that researching how individuals explain to one another may be a useful beginning point for explanation in artificial intelligence, given that the majority of this research has focused on publicly explaining decisions or actions to a human observer. However, it is reasonable to say that the vast bulk of self-explanatory artificial intelligence research is based solely on the researchers’ subjective opinions of what constitutes a "good" explanation. Extensive and essential study in science and technology has been conducted on the definition, creation, selection, assessment, and presentation of explanations by humans. This study contends that people use certain cognitive biases and social expectations in the explanation process. This paper covers relevant studies from several fields and makes the case that self-explainable artificial intelligence should expand on some current research. It highlights several key results and explores how they may be combined with research on self-explainable artificial intelligence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Explainability Explanation Interpretability Self-éxplainable AI Transparency

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UAV weaknesses against deauthentication based hijacking attacks 22

UAV weaknesses against deauthentication based hijacking atta...

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics, SAMI 2024

作者： Krasnyanszki, Bruno Brassai, Sandor Tihamer Nemeth, Andras Óbuda University Under Graduate Student in Computer Science Engineering Budapest Hungary Sapientia Hungarian University of Transylvania Department of Electrical Engineering Corunca Romania National University of Public Service Department of Electronic Warfare Budapest Hungary

ISBN: (纸本)9798350317206

Unmanned Aerial Vehicles (UAVs) and Unmanned Aerial Systems (UAS) are more important than ever before and we're using it daily for agriculture, photo making, film industry, Search & Rescue, long range cable quality control, Geo Informatics, cartography, and state use (including border protection, disaster recovery and the military). Most of the UAV/UAS devices are using digital communication protocols nowadays, especially the IEEE 802.11 Wireless Local Area of Network for the cheaper and middle price UAV/UAS. Unfortunately, the protocols specified by the previously mentioned IEEE standard are vulnerable against some attacks. This research paper proposes a method that can interrupt the normal operation of the UAV/UAS that are using the IEEE 802.11 protocol and hijack the interrupted UAV/UAS permanently. For this method, deauthentication attack has been performed against the UAV/UASs. The attack vector is capable of denying the communication between the UAV and the control station. With sufficient prior knowledge it is possible to hijack the UAV that communicates on a 802.11 based protocol. Deauthentication based attacks are much more sophisticated than for example traditional electronic jamming, because the target can be precisely chosen and the attack will not interrupt other devices using the same communication method. The research has unfortunately some limitations in equipment, measurements conducted only in three different types of UAVs from three price categories, but in the future the authors would like to extend the range of UAVs. From this three UAVs two are hijackable by this method and the third should also be hijackable with some modifications. This research could be a base later for UAV/UAS counter measure systems for the safety of all of us. © 2024 IEEE.

关键词： Drones

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A Comprehensive Study of Plant Disease Detection Using Deep Learning Methods 1st

A Comprehensive Study of Plant Disease Detection Using Deep ...

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1st International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022

作者： Haider, Kashan Prasandeep Ahmed, Maaz Pal, Ankit Rawat, Sur Singh Gupta, Vimal Shaw, Rabindra Nath Ghosh, Ankush Student Department of Computer Science and Engineering JSS Academy of Technical Education Noida201301 India Faculty Department of Computer Science and Engineering JSS Academy of Technical Education Noida201301 India University Center for Research and Development Chandigarh University Punjab India

ISBN: (纸本)9783031250873

Growing crops for human consumption and financial benefit is known as agriculture. It is the capability of using cutting-edge methods to cultivate crops and raise animals. As a result, the quality of the crop production is crucial to the development of the economy. In the agriculture industry, plant diseases and pests pose the biggest threat to crop production. Thus, it is essential to develop a method for accurately recognizing these illnesses so that the appropriate steps can be taken to treat them, increasing crop quality and quantity. A reliable disease detection system can be created by combining tools for classifying and extracting information with computer vision or image processing techniques employing deep learning. Different researchers have offered different approaches for identifying plant diseases. This study analyses these techniques. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cultivation

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Asteroid Hazard Prediction Using Machine Learning: A Comparative Analysis of Different Algorithms 3

Asteroid Hazard Prediction Using Machine Learning: A Compara...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Sharma, Tushar Sharma, Shamneesh Sharma, Ajay Kumar, Aman Malik, Arun Sharma, Abhimanyu Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India UpGrad Education Private Limited Delivery and Student Success upGrad Campus India

ISBN: (纸本)9798350330724

The prediction of asteroid risk is a crucial topic in the realm of space technology. The utilization of machine learning techniques has enabled the identification of asteroids in photographic and video data obtained from telescopes and satellites, prediction of their trajectory, simulation of their potential impact on Earth, assessment of the probability and magnitude of such an impact, and provision of support to decision-makers in the event of an impact. The implementation of these techniques can enhance our comprehension and preparedness for prospective asteroid collisions, along with our capacity to effectively rank initiatives and distribute resources targeted at mitigating the gravity of such an impact. Furthermore, the utilization of these techniques can enhance our readiness for prospective collisions with asteroids. The present study introduces a novel model for predicting asteroid hazards, which incorporates multiple variables such as asteroid dimensions, speed, path, and atmospheric conditions. The employed methodology involves the utilization of machine learning algorithms to scrutinize past records of asteroid collisions and replicate plausible impact scenarios. The training process involves utilizing 70% of the available data for training the model, while the remaining 30% is reserved for testing purposes. By implementing these methods in current research, researchers have achieved a noteworthy accuracy of 99.99% in the task of classifying asteroids. The model is subjected to comparative analysis with alternative methods, and it exhibits superior precision in comparison to the latter. © 2023 IEEE.

关键词： Forecasting

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