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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Espin Shalo, S.P. Jeeshan, R.J. Gunasekar, S. Pius Agbulu, G. Venkat Ramana, N. Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai602105 India Rajalakshmi Engineering College Department of Computer Science and Design Tamilnadu Chennai602105 India Chinhoyi University of Technology Department of Mechatronics Engineering Zimbabwe Indian Institute of Technology Department of Electrical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331510022

Chronic Kidney Disease (CKD) is a global health concern that often progresses silently until advanced stages, making early detection critical. This paper presents a point-of-care system that automates the color comparison between a test strip and a reference strip to detect biomarkers like albumin associated with kidney dysfunction. The system leverages mobile-captured images, applying image processing techniques such as segmentation and color extraction, and compares the test strip's top color with reference colors on the parent strip using the Delta-E metric in the LAB color space. The closest match quantifies the biomarker concentration, providing a reliable diagnostic measure. To enhance diagnostic precision, this research incorporates Convolutional Neural Networks (CNNs) to refine image preprocessing and anomaly detection. The CNN model complements existing methods by mitigating environmental influences like lighting variability and improving feature extraction accuracy. This hybrid approach ensures consistency while adapting to diverse datasets and image conditions. The result is a cost-effective, fast, and accessible diagnostic tool that reduces manual errors and supports early CKD detection in resource-limited settings. Future work will focus on expanding the application of this system to detect other diseases and further optimize the integration of CNNs for enhanced scalability and robustness. © 2024 IEEE.

关键词： Convolutional neural networks

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A Systematic Survey on Deep Neural Networks for Sentiment Analysis 10

A Systematic Survey on Deep Neural Networks for Sentiment An...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Yadav, Pinky Kashyap, Indu Bhati, Bhoopesh Singh Fet Mriirs Department of Computer Science and Engineering Faridabad India Indian Institute of Information Technology Department of Computer Science and Engineering Sonepat India

ISBN: (纸本)9789380544472

Sentiment analysis is a powerful approach for processing, analysing, speculating, and summarising unstructured content to convert it into reliable information. With the increasing demand for sentiment analysis in real-life applications, new discriminate machine learning and natural language processing techniques have been introduced in past years. However, the deep neural network is an emerging approach that learns by multiple layer feature dimensions of data and predicts faultless for large corpora with minimum computational cost. These neural networks provide the automatic feature engineering and advanced word embedding (TF-IDF, One Hot Encoding, Word2Vec, and Glove) for feature vector presentations that are helpful in extracting the meanings from complex linguistic texts. Therefore, a systematic survey is presented to provide detailed knowledge about deep neural networks that are rapidly applied in sentiment analysis with enhanced linguistic capabilities. The prime contribution of the existing researchers is highlighted with the focal point of deep learning-based sentiment analysis. Additionally, several benchmark datasets, feature engineering techniques, and existing deep learning-based research performance are also discussed. The prime objective of this survey is to present the credibility of the deep neural network to handle the sentiment analysis tasks. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Sentiment analysis

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Unmanned Aerial Vehicles and Low-Flying Drones: A Review of Classification 2

Unmanned Aerial Vehicles and Low-Flying Drones: A Review of ...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Pagrut, Shrijeet Jawandhiya, Pradip Shri Sant Gajanan Maharaj College of Engineering Department of Computer Science and Engineering Maharashtra Shegaon India Pankaj Laddhad Institute of Technology and Management Studies Department of Computer Science and Engineering Maharashtra Buldhana India

ISBN: (纸本)9798331528713

Nowadays Research and Development of Novel and Efficient drones becomes the core part of strategic practices of every nation. There is a growing need of using a variety of Unmanned Aerial Vehicles with significant capabilities in Civilian, Military and Commercial Applications. UAVs flight patterns such as Vertical Takeoff Landing and Horizontal Takeoff Landing, its maximum altitude capacity, range are discussed. Designing of Low Flying drones are classified into Small, Micro and Nano-Pico-SmartDust(NPSD) categories. Use and Impact of different materials like Titanium alloy in design is provided. Additionally, current activities in the market related to small drones and future research direction in the field of utility and applications drones are discussed. © 2024 IEEE.

关键词： Drones

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A Simple Algorithm to Secure Data Dissemination in Wireless Sensor Network 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Sonai, Veeramani Bharathi, Indira Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India

ISBN: (数字)9789819981298

ISBN: (纸本)9789819981281

Wireless sensor networks are heavily utilized in numerous applications in both industry and science. As the original data is transferred from the sensor node to the sink node, unauthorized individuals attempt to access it, breaching confidentiality. The first step in enhancing the security of data transmission in a wireless sensor network (WSN) is figuring out the best method for encrypting data as it moves from the sensors to the central node. In order to move encrypted data from different sensor nodes to sink nodes efficiently in wireless sensor networks, this paper introduces a simple data encryption technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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Effective Seizure Recognition from EEG Data by Asymmetrical Back Propagation Neural Network (ABPN) Classification 4

Effective Seizure Recognition from EEG Data by Asymmetrical ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Gupta, Prateek Mishra, Dinesh Kumhar, Bhupchand Shukla, Prashant Kumar Naveen, K.B. Roy, Lipika Shri Ram Institute of Science and Technology Department of Computer Science and Engineering Madhya Pradesh Jabalpur India Mangalyatan University-Jabalpur Department of Computer Science and Engineering MP Jabalpur483001 India Ies University Ies Institute of Technology and Management Department of Computer Science and Engineering Madhya Pradesh Bhopal462044 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur522302 India Adichunchanagiri University Bgs Institute of Technology Department of Electronics and Communication Engineering India Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

ISBN: (纸本)9798331518981

A significant task within neurology and epilepsy management is the differentiation of seizures from the Electroencephalogram (EEG signal). The current work of research focuses on suggesting an idea to enhance the detection of seizures by applying the Asymmetrical Back Propagation Neural Network (ABPN) classification. This dynamic aspect of ABPN is ideal for learning the details of the features and patterns of EEG since seizures are usually unilateral. We also compare our ABPN classifier with the standard neural networks to confirm that our proposed classifier perform better in sensitivity, specificity, and the overall assessment of the classifier in the global environment. According to the experiment, the authors are convinced that the ABPN model can solve the problem of lowering the false-positive rate along with the increase in the speed of detection to make it suitable for real-time seizure observation. This research will be useful in the improvement of better equipment to detect the seizures with a positive implication on patient health. © 2024 IEEE.

关键词： Neural networks

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On Identifying Communities in Online Hate Speech

On Identifying Communities in Online Hate Speech

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Burman, Ayusha Karan, Shaswata Mallik, Shreya Majee, Drishti Bal, Mayukh Anand, Sumit Chakraborty, Amartya University of Engineering & Management Department of Computer Science and Engineering Kolkata India St. Thomas' College of Engineering and Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350317060

Hate Speech is one of the most dangerous problems which disturbs social and religious harmony. Social media which plays a vital role in our lives has now become a responsible element in spreading hate speech. This work presents a comprehensive analysis of hate speech on Twitter, using Tweepy, an open-source Twitter API, to fetch relevant data. Our study sought to investigate the behavioral patterns of users responsible for spreading hateful comments online. After collecting a sample of tweets, standard sentiment analysis techniques have been leveraged to segregate the tweets as per their positive and negative sentiments. Using Affin lexicon and TextBlob libraries, the polarity and subjectivity of the tweets have been identified. The k-means clustering algorithm is utilized to group the data into positive and negative clusters. These clusters have been considered as communities of the users and further analyzed. Standard metrics indicate a strong community formation in our proposed approach. The feasibility of clustering the features of user expressions toward community formation in social networks is explored in this work. Our findings provide valuable insights into the prevalence and nature of hate speech on social media and offer suggestions for addressing this critical issue. © 2023 IEEE.

关键词： Sentiment analysis

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Recent Advances in Analysis and Detection of Tuberculosis System in Chest X-Ray Using Artificial Intelligence (AI) Techniques: A Review

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Current Materials science 2023年第1期16卷 43-51页

作者： Suchitra, Shanmugam Ibrahim, S. Jafar Ali Sathya, Mariappan Sahini, Varsha Chakravarthy, N. Surya Kalyan Kumar, Vaneet Saruchi Department of Computer Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT SCOPE Vellore Institute of Technology Tamilnadu Vellore India AMC Engineering College Karnataka Bengaluru India Department of Computer Science Engineering CT Institute of Engineering Management and Technology Punjab Jalandhar India Data Science QIS College of Engineering and Technology Andhra Pradhesh Ongole India Department of Applied Sciences CT Institute of Engineering Management and Technology Shahpur Campus Punjab Jalandhar India Department of Biotechnology CT Institute of Pharmaceutical Sciences Shahpur Campus Punjab Jalandhar India

Mycobacterium tuberculosis causes tuberculosis (TB), a bacterial illness. Although germs are most typically found in the lungs, they can affect other sections of the body as well. Tuberculosis is one of the primary causes of mortality in both developed and developing nations, necessitating worldwide attention. Even though TB may be prevented in the majority of instances if discovered and treated early, the number of deaths caused by the disease is quite high. There has been a significant increase in interest and research activity in TB detection in recent years. The new advancement in the field of AI technology may be able to assist them in overcoming these development gaps. computer-Aided Detection and Diagnosis (CADD) aids in the diagnosis of diseases by analysing symptoms and X-ray images of patients. Many solutions are currently be-ing developed to improve the effectiveness of TB diagnosis classification using AI and DL ap-proaches. Although a variety of TB detection techniques have been developed, there is no common-ly acknowledged method. The purpose of this study is to give a survey on Tuberculosis Detection. It also emphasises the difficulty and complexity of the Tuberculosis Detection System's design. © 2023 Bentham science Publishers.

关键词： computer vision

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Towards Unbiased Minimal Cluster Analysis of Categorical-and-Numerical Attribute Data 27th

Towards Unbiased Minimal Cluster Analysis of Categorical-an...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Yunfan Luo, Xiaopeng Chen, Qingsheng Zou, Rong Zhang, Yiqun Cheung, Yiu-Ming School of Computer Science and Technology Guangdong University of Technology Guangzhou China School of Computer Engineering Guangzhou Huali College Guangzhou China Department of Computer Science Hong Kong Baptist University Hong Kong

ISBN: (纸本)9783031781650

Categorical and numerical attributes occur frequently in cluster analysis tasks. To bridge the information gap between the heterogeneous categorical and numerical attributes in cluster analysis, the existing approaches usually adopt prior assumptions to distance definition and cluster distribution, which unavoidably introduce bias to the clustering process. To address this issue, we propose to analyze mixed data comprising both categorical and numerical attributes by forming minimal clusters through neighborhood set theory. As the minimal clusters are the smallest cluster units that can be obtained without relying on prior assumptions, unbiased cluster analysis can be facilitated accordingly. To avoid information loss, distance and density metrics that are unified on both numerical and categorical attributes are also proposed and utilized to merge the minimal clusters hierarchically. It turns out that our proposed approach is highly interpretable, and is capable of accurately and robustly clustering data sets composed of any combination of numerical and categorical attributes. Extensive experimental evaluations demonstrate its efficacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Predicting Student Academic Performance using Machine Learning: Analyzing Socio-Economic and Personal Factors from Secondary Education in Portugal 4

Predicting Student Academic Performance using Machine Learni...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Sahu, Santosh Kumar, Ashok Kumar, Rahul Shukla, Anshita Chole, Vikrant Moolchandani, Jhankar Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

This paper aims to predict student academic performance using machine learning algorithms by analyzing socioeconomic and personal factors from secondary education in Portugal. The study utilizes a dataset containing demographics, family background, educational support, extra-curricular activities, and academic records to evaluate their impact on students' success in Mathematics and Portuguese courses. Machine learning models such as Logistic Regression, Decision Trees, and Random Forests were employed to analyze the data. The Random Forest model outperformed others with an accuracy of 85 %, indicating significant potential in predicting student performance using the selected features. © 2024 IEEE.

关键词： Adversarial machine learning

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Novel Hybrid Optimization Algorithm for Improved Search Performance in Real-World Applications

Novel Hybrid Optimization Algorithm for Improved Search Perf...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Shailendra Pratap Srivastava, Prateek Bennett University School of Computer Science Engineering and Technology UP Greater Noida India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382723

The paper proposes a new method that combines the advantages of Deep Learning (DL) with the Differential Evolution (DE) algorithm to create a hybrid framework that can solve optimization problems more effectively. The proposed hybrid algorithm enhances the search and optimization procedures by the learning capabilities of DL and the population-based nature of DE algorithm. First, an initial population of solutions is produced by the DE algorithm and put into a DL model for additional refinement. In order to train the DL model - typically a deep neural network - a mix of supervised and unsupervised learning techniques is used. This enables the model to recognize complex relationships and patterns within the challenge space. The combined DE algorithm with DL, as demonstrated by experimental findings for optimum convergence rate and search for addressing complex real-world optimization problems effectively. © 2024 IEEE.

关键词： Optimization

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