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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Gadegaonkar, Sakshi Lakhwani, Darsh Marwaha, Sahil Salunke, Prof. Abhijeet Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9781665462167

Millions of students graduate from college every year and start looking for jobs, but hunting for a suitable job that pays according to the applicant's skills is not an easy process. On the other hand, recruiters and companies look for graduates to join them according to their requirements. Unfortunately, there is often a gap between the applicants (job seekers) and recruiters (job providers). To bridge this gap and solve this problem, this study has come up with a job recommendation system that will recommend jobs in the IT sector that match the applicants' skills and expectations. This study has built an Android application to recommend jobs to the users based on their interests, databases, frameworks, platforms, and languages comfortable with. The recommendation is done by using a Machine Learning (ML) model (written in Python), which makes use of the content-based filtering algorithm. This study has built the application using Kotlin, Jetpack Compose, Ktor, and the UI has been designed by using Material 3 Design Principles. © 2023 IEEE.

关键词： Recommender systems

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Deep Learning Techniques for Malware Detection: A Comprehensive Survey

Deep Learning Techniques for Malware Detection: A Comprehens...

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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Ajvad Haneef, K. Madhu Kumar, S.D. National Institute of Technology Department of Computer Science & Engineering Calicut India

ISBN: (纸本)9798350338003

Malware detection and familial classification approaches based on deep learning (DL) techniques have emerged as a promising solution in recent years. The ability of deep neural networks (DNNs) to learn complex patterns and features from malware samples has resulted in high detection rates and has attracted the attention of researchers in the field of cyber-security. The use of deep learning algorithms in malware detection and familial classification can help to improve the accuracy and efficiency of existing methods and can also be used to detect new and unknown malware variants. DL-based malware detection models are proven to outperform traditional, state-of-the-art machine learning models. Therefore, there exists a greater significance in reviewing existing DL-based malware detection techniques alone. This survey presents a comprehensive overview of state-of-the-art DL techniques used for malware detection. Although there exist few works that review machine learning and deep learning-based malware detection techniques, and to the best of our knowledge, only a limited number of surveys focused on deep learning-based malware detection techniques alone. Overall, this survey provides a comprehensive overview of the recent works in the field that are intended to be a valuable resource for researchers, practitioners, and anyone interested in deep learning-based malware detection. We also present the open issues and challenges in this area which require research attention. © 2023 IEEE.

关键词： Learning algorithms

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Enhancing Healthcare Record Privacy Through the Integration of Visual Cryptography and Diverse Image Encryption Techniques 9

Enhancing Healthcare Record Privacy Through the Integration ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Sandeep, Dayina Ganesh, Addala Siva Krishna, Angalakurthi Jaya Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

The methodology of this research focuses on analyzing real-world breaches, such as the Star Health Insurance leak, and demonstrating how multi-layered encryption techniques could have mitigated the impact. By splitting sensitive healthcare information into multiple shares using visual cryptography, unauthorized access is prevented unless all shares are combined, adding an additional layer of security. Furthermore, image-based encryption adds another line of defense, ensuring that even if hackers gain access to data, it remains unusable without the correct decryption methods. Through the implementation of these techniques, we demonstrate a significant enhancement in the security of healthcare records. By combining diverse encryption methods, our approach not only strengthens the protection of personal health information but also prevents unauthorized data dissemination, as seen in the Star Health breach. The results indicate that our model is resilient to various cyber-attacks, reducing the likelihood of large-scale data leaks and improving the integrity of healthcare systems. © 2024 IEEE.

关键词： Electronic health record

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Keyframe Extraction assisted Crime Detection 7

Keyframe Extraction assisted Crime Detection

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7th International Conference on computer Applications in Electrical engineering-Recent Advances, CERA 2023

作者： Jangam, Ameya G. Mohite, Atharva P. Nayak, Deep U. Nimkar, Anant V. Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350305005

This paper investigates the impact of keyframe extraction algorithms on the accuracy of the action detection model. The work aims to improve the efficiency of crime detection systems by enabling real-time inference and accurate predictions. The performance of various state-of-the-art models with different keyframe extraction algorithms is evaluated. The results highlight the significant effect of the algorithm on model accuracy, underscoring the importance of selecting an appropriate keyframe extraction algorithm for effective action detection. Additionally, a parameter-efficient fine-tuning approach is proposed that can reduce the required training data while improving the model's performance, which could further enhance the system's efficiency. The models are evaluated on a publicly available dataset to ensure comparability, and the study thoroughly evaluates the proposed approach. © 2023 IEEE.

关键词： Efficiency

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Lattice thermal conductivity of material and its correlation with Debye temperature using regression models

Lattice thermal conductivity of material and its correlation...

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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Dandapat, Sayantan Sahu, Nepal Azad, Chandrashekhar Kumar, Uday National Institute of Technology Jamshedpur Department of Physics Jamshedpur India National Institute of Technology Jamshedpur Department of Computer Science and Engineering Jamshedpur India

ISBN: (纸本)9798350363289

Lattice thermal conductivity (K) of the material plays a crucial role in its variety of applications such as thermoelectric, thermal barrier coating and so on. The role of the Debye temperature (Td) is a significant factor in determining the lattice thermal conductivity of any materials. However, the relation between the lattice thermal conductivity and Debye temperature is very less explored using Machine Learning (ML). In this study, we first predicted lattice thermal conductivity using four machine learning regression models: Neural Network (NN), Gaussian Process Regression (GPR), Boosted Tree Regression (BTR) and Fine Tree Regression (FTR). NN and GPR is found to be the best performing model among all the used models with R2 equal to 99.868 % and 97.333 % respectively. Further, for these two best performed models, we predict the relation between the K and Td in both training and testing data. The relation between training and testing exhibits similar nature in the two models NN and GPR. This work guides a pathway for searching of materials for wide applications. © 2024 IEEE.

关键词： Regression analysis

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Mental Stress Prediction Using Wrist Wearable Through Machine Learning Approaches

Mental Stress Prediction Using Wrist Wearable Through Machin...

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2023 International Conference on Sustainable Emerging Innovations in engineering and technology, ICSEIET 2023

作者： Verma, Poonam Singh, Rahul Kanpur Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350329186

Stress is one of the mental health issues that people face today. In reality stress is the primary cause of many conditions, such as heart disease, migraines, infertility, obesity, insomnia, and a host of others. In this scenario, stress prediction is crucial at an early stage so that we can protect ourselves against future issues. For identifying the stress, method of connecting sensors to multiple data gathering units using traditional technologies typically involved electrodes, wires, and sensors, was uncomfortable. Smart watches, on the other hand, are designed to be worn on the wrist and have sensors integrated into them that can measure heart rate and activity levels. Here, we report on our smart watch's highest level of stress prediction accuracy. We have used random forests, decision trees, support vector machines, naive bayes, logistic regression, and k-nearest neighbor models with k-fold cross-validation and voting ensemble learning to predict stress using data from smart watches (Fit bit). The support vector machine model had a binary class prediction of stress accuracy of 94°, which was the highest. Our findings suggest that the support vector machine and voting ensemble combination may be more effective at identifying stress using smart watches. Our study closes a variety of methodological shortcomings in previous studies on the automatic stress prediction using smart watches. It has also been noted that the model performs better when ensemble learning techniques are used. © 2023 IEEE.

关键词： Watches

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Automatic Weed Detection using CCOA based YOLO Network in Soybean Field 2

Automatic Weed Detection using CCOA based YOLO Network in So...

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

作者： Kavitha, S. Gangambika, G. Padmini, K. Supriya, H.S. Rallapalli, Sreekanth Sowmya, K. Nitte Meenakshi Institute Of Technology Department Of Mca Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350381665

Due mostly to competition with crops, weeds now account for 45% of crop failures in the gardening sector. This ratio can be decreased via an actual wild plant detecting technique. Because of the large variety of weed species that are mixed together and the complicated distribution status of soybean, traditional approaches have a poor weed detection rate. To successfully apply selective herbicide spraying, weeds in crop fields must be accurately identified. The opaque allocation of wildflowers and cuts prepares it difficult to identify weeds because border segmentation at the overlap is imprecise, making it impossible to accurately classify pixels. This paper suggests a perfect for weed finding in soybean fields based on an enhanced YOLOv5 network architecture to address this issue. A new weed identification model is produced as a consequence of fusing feature maps of various sizes in the decoding step in order to improve the model's utilisation of global information linkages. The CrissCross Optimisation (CCO) algorithm chooses the best kernel for convolution. The average ratio reached 98% when this model was used to identify a dataset comprising many widely dispersed weedy soybean seedlings, and the accuracy increased by 3.94% in comparison to other traditional semantic segmentation models. The findings provided guidance for the additional use of modifiers in wildflower credit by demonstrating that the model suggested in this paper can positively speech the subjects of improper border delineation credit when wildflowers are thickly dispersed with harvests and improper organisation when credit boards overlay. © 2024 IEEE.

关键词： Herbicides

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Mandala Symmetrization through Curvature Map and Geometric Graph* 38

Mandala Symmetrization through Curvature Map and Geometric G...

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38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Sarkar, Tusita Chatterjee, Preetam Chayan Bhowmick, Partha Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798350370515

Mandalas are esteemed as an intriguing form of abstract art and gaining popularity day by day in the arena of digital art. Although their structures are perfectly circular and symmetric, their man-made renditions are usually not perfect - whether sketched on a piece of paper or prepared by a graphics tablet. Hence, for efficient storage and computation, their digital images demand symmetrization and simplification. In this paper, we focus on this specific problem and show how a novel technique based on geometric graph can be used to give us an efficient solution. The geometric graph is computed from the mandala image and referred to as mandala graph. The proposed technique aims to compute its symmetric embedding after executing the steps of primitive grouping, primitive vectorization, and primitive realignment. Certain discrete-geometric concepts such as multi-scalar curvature map and optimum isothetic covers are used in these steps. To adjudge its merit we provide some experimental results, and we conclude with its multi-faceted scope as a form of digital art. © 2023 IEEE.

关键词： Geometry

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HETEROGENEOUS NEURONAL AND SYNAPTIC DYNAMICS FOR SPIKE-EFFICIENT UNSUPERVISED LEARNING: THEORY AND DESIGN PRINCIPLES 11

HETEROGENEOUS NEURONAL AND SYNAPTIC DYNAMICS FOR SPIKE-EFFIC...

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11th International Conference on Learning Representations, ICLR 2023

作者： Chakraborty, Biswadeep Mukhopadhyay, Saibal Department of Electrical and Computer Engineering Georgia Institute of Technology United States

This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction performance, enabling spike-efficient (unsupervised) learning. We analytically show that the diversity in neurons' integration/relaxation dynamics improves an RSNN's ability to learn more distinct input patterns (higher memory capacity), leading to improved classification and prediction performance. We further prove that heterogeneous Spike-Timing-Dependent-Plasticity (STDP) dynamics of synapses reduce spiking activity but preserve memory capacity. The analytical results motivate Heterogeneous RSNN design using Bayesian optimization to determine heterogeneity in neurons and synapses to improve E, defined as the ratio of spiking activity and memory capacity. The empirical results on time series classification and prediction tasks show that optimized HRSNN increases performance and reduces spiking activity compared to a homogeneous RSNN. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Neurons

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Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER

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Intelligent Automation & Soft Computing 2023年第3期35卷 3545-3555页

作者： C.P.Thamil Selvi P.Muneeshwari K.Selvasheela D.Prasanna Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and TechnologyCoimbatore641009TamilnaduIndia Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Chennai602105Tamil NaduIndia Department of Computer Science and Engineering Sreesakthi Engineering CollegeKaramadai641104Tamil NaduIndia Department of Computer Science and Engineering Mahendra Engineering CollegeNamakkal637503Tamil NaduIndia

The term sentiment analysis deals with sentiment classiﬁcation based on the review made by the user in a social *** sentiment classiﬁcation accuracy is evaluated using various selection methods,especially those that deal with algorithm *** this work,every sentiment received through user expressions is ranked in order to categorise sentiments as informative and *** order to do so,the work focus on Query Expansion Ranking(QER)algorithm that takes user text as input and process for sentiment analysis andﬁnally produces the results as informative or *** challenge is to convert non-informative into informative using the concepts of classiﬁers like Bayes multinomial,entropy modelling along with the traditional sentimental analysis algorithm like Support Vector Machine(SVM)and decision *** work also addresses simulated annealing along with QER to classify data based on sentiment *** the input volume is very fast,the work also addresses the concept of big data for information retrieval and *** result com-parison shows that the QER algorithm proved to be versatile when compared with the result of *** work uses Twitter user comments for evaluating senti-ment analysis.

关键词： Sentiments QER SVM bayes multinomial big data analysis simulated annealing

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