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Enhancing Lighter Neural Network Performance with Layer-wise knowledge Distillation and Selective Pixel Attention

Enhancing Lighter Neural Network Performance with Layer-wise...

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International eConference on Computer and knowledge engineering (ICCKE)

作者： Siavash Zaravashan Sajjad Torabi Hesam Zaravashan Dept. Computer Engineering Iran University of Science and Technology Tehran Iran Data Scientist Part Research Center of AI Tehran Iran

Deep neural networks have made a revolution in different areas of Artificial Intelligence such as image classifi-cation and object detection. However, most of these models are heavy and time-consuming at the inference stage. If we desire to use real-time applications, it’s necessary to lighten them up. However, this could negatively affect the performance of models. One suggested approach for enhancing the capabilities of smaller models involves a technique called knowledge distillation. In this method, a less advanced model, referred to as the student model, learns from and emulates the more advanced teacher model to enhance its performance. This method has been proven as an effective technique to compress the model and increase its accuracy. Previous methods mostly focus on proposing feature transformation and loss functions between the same level’s features to improve the effectiveness. In this paper, we investigate the effect of connections between different layers of teacher and student networks and reveal their great importance. We have employed three different loss functions—attention-driven distillation, hierarchical context loss, and non-local loss—to enhance the classifier’s overall performance. Extensive experiments on the CIFAR-100 dataset show that the proposed method has a great performance improvement. For the benefit of the research community, we will make the code for this study available on GitHub.

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Development of an Intelligent System for Predictive Analysis of Student Academic Performance Using Machine Learning Techniques

Development of an Intelligent System for Predictive Analysis...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anand Tripathi KTV Reddy Dept. of Artificial Intelligence and Data Science Datta Meghe Institute Of Higher Education and Research Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

This research proposes an intelligent system utilizing machine learning techniques to predict student academic performance. A Random Forest classification model was developed based on a dataset consisting of key attributes such as attendance, previous grades, hours spent weekly on studying, medical emergencies, and assignment scores. The model classifies students into three categories: High-Performing, Average, and At-Risk, achieving an impressive accuracy of 99 %. Attendance and previous grades emerged as the key determinants of academic success. To facilitate real-world application, this project also includes an interactive Streamlit-based web application, enabling educators to make instant data-driven predictions by inputting students' academic details. This tool empowers educators with actionable insights, allowing them to implement timely interventions, optimize resource allocation, and adapt learning experiences for improved student outcomes. The study underscores the rapid advancements achieved through predictive analytics in education. By integrating artificial intelligence into pedagogic practice, institutions can proactively address learning difficulties, enhance student engagement, and foster academic excellence.

关键词： Education data visualization Learning (artificial intelligence) Alarm systems Real-time systems Resource management data mining Predictive analytics Intelligent systems Random forests

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Hyperparameter Tuning of SVM Using Metaheuristics: An Empirical Analysis

Hyperparameter Tuning of SVM Using Metaheuristics: An Empiri...

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Wireless Africa Conference (WAC)

作者： Sunday O. Oladejo Lateef A. Akinyemi Stephen O. Ekwe Taiwo G. Omomule Oluwaseyi P. Babalola School for Data Science and Computational Thinking Stellenbosch University South Africa Centre for Augmented Intelligence and Data Science University of South Africa South Africa Dept. of Electrical Electronic and Comp. Engineering Cape Peninsula University of Technology South Africa Dept of Mathematics and Statistics University of Guelph Ontario Canada

ISBN: (数字)9798331517588

ISBN: (纸本)9798331517595

The Support Vector Machine (SVM) performance is highly dependent on the selection of optimal hyperparameterse Conventional optimisation methods, including grid search, gradient descent, randomized search, and various experimental approaches, have been extensively utilized in the literature to identify optimal hyperparameters. This paper provides an empirical analysis of SVM's hyperparameter tuning by use of metaheuristics. We compare 11 widely known metaheuristics, such as the Genetic Algorithm (GA), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), and Differential Evolution (DE), and employ three common datasets, namely Iris, Breast Cancer Wisconsin, and MNIST, due to their unique characteristics. Empirical analysis takes into account factors including cross-validation, number of agents, noise, and mis-labelled data insertion, with emphasis on performance metrics such as accuracy, number of support vectors, and computational time. Our findings reveal that the PSO outperforms other metaheuristics, followed by the DE and ACO. In addition, we conclude that the ACO has the least computational time.

关键词： Support vector machines Wireless communication Accuracy Reviews Metaheuristics Optimization methods Vectors Particle swarm optimization Tuning Genetic algorithms

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Machine Learning Model for Prediction of Smartphone Addiction

Machine Learning Model for Prediction of Smartphone Addictio...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： S Likhith Chengamma Chitteti M Dharani V Nivedhitha N Guna Geethika V Godwin Dept. of Information Technology Mohan Babu University(Erstwhile Sree Vidyanikethan Engineering College) A.Rangampet Tirupati India Dept. of Data Science Mohan Babu University(Erstwhile Sree Vidyanikethan Engineering College) A.Rangampet Tirupati India Dept. of ECE Mohan Babu Universuy(Erstwhile Sree Vidyanikethan Engineering College) A.Rangampet Tirupati India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Smartphone addiction has become a growing concern in recent years, with increasing numbers of people exhibiting symptoms such as excessive phone use, loss of productivity, and even physical and psychological health problems. As a result, there is a need to develop effective tools for predicting smartphone addiction and identifying those at risk. In this study, we built a ML model used for predicting smartphone addiction using data collected from a survey of smartphone users. Demographics, phone usage habits, and a range of psychological issues like stress, anxiety, and depression were all covered in the poll. A popular and effective machine learning method, to build our model. In this study, the data is preprocessed by encoding categorical variables and normalizing numerical variables to ensure that the model could learn effectively. Further, the model is trained on some percentage of the data and evaluated its accuracy and its performance on the remaining percentage of the data using several metrics such as accuracy. The results showed that the model has achieved a high accuracy in predicting smartphone addiction. The most important features for predicting addiction were mobile phone usage patterns such as the frequency of checking notifications, the number of hours spent on the phone each day, and the types of apps used most frequently. Other important factors included age, gender, and stress levels. The model developed has several potential applications. It could be used by healthcare professionals to identify individuals who are at risk of developing smartphone addiction and provide appropriate interventions. It could also be used by app developers to design apps those are less addictive and promote healthier phone usage habits. In a brief, this study illustrates both the practicality and efficacy of using machine learning models for predicting smartphone addiction. Further research is needed to validate our findings on larger and more diverse datasets and to ex

关键词： Surveys Adaptation models Accuracy Addiction Psychology Machine learning Human factors

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A Novel Machine Learning Approach to Detect Application Layer DDoS Attacks

A Novel Machine Learning Approach to Detect Application Laye...

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Emerging Technology (INCET), International Conference for

作者： V. Jyothsna Sarabu Naga Swetha P. Lalan Kumar Vadde Uday Kumar Putta Kalyan K. Srilakshmi Dept. of Data Science MOHAN BABU UNIVERSITY (Erstwhile Sree Vidyanikethan Engineering College (Autonomous)) Tirupati India Dept. of Information Technology Sree Vidyanikethan Engineering College Tirupati India Sri Padmavathi Women s Degree & PG College TTD Tirupati India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Attackers use application-layer DDoS (App-DDoS) attacks, which aim to bring down the victim's server by sending fake packets in its direction. Because malicious packets mimic the behavior of legitimate users and contain real source IP addresses, neither attackers nor IDS can distinguish packets from legitimate users. Regardless of the kind, the primary objective of DDoS attackers is to make the victim servers react so slowly that they become unusable or shut down entirely. They overburden the victim server's bottleneck resources in order to do this. For application-layer DDoS attacks, CPU cycles, RAM, I/O bandwidth, disk bandwidth or database bandwidth and TCP/IP stacks are the intended bottleneck resources. Through the use of valid requests, the attacker overwhelms the bottleneck resources in App-DDoS attacks. An attacker overwhelms limited resources with real requests in App-DDoS attacks. Any bot that wants to participate in an attack must first establish a TCP connection to the victim's server, which requires a real IP address to perform such an attack. An App DDoS security technique's primary goal is to identify and separate malicious traffic from legal traffic. Because attackers intentionally generate App-DDoS traffic to mimic real traffic, this problem is challenging. However, experienced hackers are always evolving their toolkits and creating increasingly complex App DDoS attacks, making it harder to identify attack activity. On the other hand, the victim server can stop all traffic coming from the attack sources once the attacked traffic is identified. Over the past decade, a number of traditional, heuristic, and more recently, machine learning (ML)-based methods have been developed to identify and differentiate App-DDoS activity from real traffic. Previously used traditional strategies, such are heuristic and classic approaches, use traffic engineer-programmed rules to identify App DDoS activity. Due to the constantly changing and evolving nature of App-DDo

关键词： Training Accuracy Stacking Bandwidth Denial-of-service attack Feature extraction Boosting

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A Cloud-based Auto-Scaling System for Virtual Resources to Back Ubiquitous, Mobile, Real-Time Healthcare Applications

A Cloud-based Auto-Scaling System for Virtual Resources to B...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Sk Khader Basha NagaMalleswara Rao Purimetla D Roja Nagagopiraju Vullam Lavanya Dalavai Sai Srinivas Vellela Dept. of CSE-Data Science Chalapathi Institute of Technology Guntur AP India Dept. of CSE Chalapathi Institute of Technology Guntur AP India Dept. of CSE Chalapathi Institute of Engineering and Technology Guntur AP India

The utilisation of virtualization technologies in conjunction with cloud computing has emerged as a significant and influential trend within the Information Technology (IT) sector. Cloud computing has significantly transformed the development landscape of mobile ubiquitous services by introducing notable attributes such as reliability and cost-effectiveness. Consequently, it has successfully penetrated the market, bringing about substantial changes in the industry. While it is possible to easily convert numerous types of best-effort mobile ubiquitous applications to cloud infrastructures, it is crucial to thoroughly analyse essential elements in order to enhance the effectiveness of cloud computing infrastructures for real-time healthcare applications. When programme clients observe an anomaly, there is a substantial increase in the data transfer rate. Nevertheless, there are inherent issues in the current allocation of computational resources by server-side processes to effectively manage the fluctuating and time-varying data quantities. This article introduces a novel auto-scaling technique for servers, which aims to dynamically allocate virtual resources. The suggested method’s efficacy in scaling virtual resources up and down in response to the number of real-time tasks is evaluated through testing conducted on an Amazon EC2 platform. The obtained findings demonstrate the efficient performance of the mechanism.

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AI-Driven Energy Optimization: Household Power Consumption Prediction With LSTM Networks and PyTorch-Ray Tune in Smart IoT Systems

AI-Driven Energy Optimization: Household Power Consumption P...

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International Conference on Microelectronics, ICM

作者： Vinoth Kumar Kolluru Yagnesh Challagundla Advaitha Naidu Chintakunta Bappadittya Roy Amine Bermak Renuka Devi S M Department of Data Science Stevens Institute of Technology Hoboken NJ USA Engineering Education Dept. University of Florida USA Department of Computer science and Engineering University of North Carolina Charlotte USA School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh India College of Science and Engineering Hamad bin Khalifa Universityy Ar-Rayyan Qatar Dept. of ECE G Narayanamma Institute of Technology & Science (for women) Hyderabad

ISBN: (数字)9798350379396

ISBN: (纸本)9798350379402

Energy optimization is critical in smart home systems and IoT networks, necessitating innovative models that reduce energy use. This research presents two Long Short-Term Memory (LSTM) network models for estimating home power consumption using time-series data. The first model employs a standard LSTM technique with multivariate characteristics including global active power, voltage, and sub-metering variables. This technique strikes a balance between prediction accuracy and computing economy, making it ideal for resource-constrained applications. The second model provides an optimized version of the LSTM, which uses PyTorch and Ray Tune for hyperparameter optimization. The optimization focuses on tweaking learning rates, batch sizes, and LSTM layers to improve model accuracy and convergence speed. Using hyperparameter tuning, the Mean Squared Error (MSE) for global active power forecasts is decreased to 0.0018, proving its appropriateness for low-resource IoT systems. Both models are tested on a resampled real-world household electric power consumption dataset for effective training. The study emphasizes the advantages of multivariate time-series analysis and hyperparameter optimization, demonstrating that the optimized LSTM model can accurately predict energy consumption, enhance energy management in smart homes, and lower computing costs. Future work will look into combining more IoT data streams and real-world deployment for further improvement. The findings add to the expanding body of knowledge about energy optimization in IoT environments, addressing the crucial demand for effective, real-time energy management systems.

关键词： Power demand Accuracy Computational modeling Biological system modeling Smart homes Predictive models Hyperparameter optimization Real-time systems Long short term memory Optimization

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Comparative Study of Word2Vec, FastText, and Glove Embeddings for Synonym Identification in Bugis Language

Comparative Study of Word2Vec, FastText, and Glove Embedding...

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Wahyu Ramadhan Arianto Yuyun Ahmad Abrar Najirah Umar Nasrullah Nurfaedah Gusnawaty Asril Jarin Dept. of informatics engineering Handayani University of Makassar Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesian Dept. of informatics management Handayani University of Makassar Makassar Indonesia Dept. of Regional Literature Hasanuddin University Makassar Indonesia

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This research aims to analyze the performance of word vectors generated by three pre-trained word embedding models, namely Word2Vec, Fast'Text, and Glove, to detect synonyms at the word level in the Bugis language. The word vectors from each model were integrated with a Bugis language dataset containing 12,060 monolingual texts, and then trained using the Long Short-Term Memory (LSTM) model. The results show that Word2Vec has the highest accuracy of 0.9412, followed by Glove with 0.9353 and FastText with 0.9262. Additional experiments with cosine similarity revealed that Glove performed best for predicting synonyms across all word occurrence frequency categories, while FastText and Word2Vec had inferior results. These findings indicate that although high training accuracy is achieved, it does not always imply that the model will excel in predicting synonyms.

关键词： Training Measurement Accuracy Semantics Predictive models Transformers Vectors Informatics Long short term memory Context modeling

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Comparative Study of Inductive Graph Neural Network Models for Text Classification

Comparative Study of Inductive Graph Neural Network Models f...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Saran Pandian Uttkarsh Chaurasia Shudhanshu Ranjan Shefali Saxena Data Science DAIICT Gandhinagar India Dept. of Computer Science and Engineering NIT Hamirpur India Dept. of Computer Science and Engineering Presidency University Banglore India Dept. of Electronics and Communication NIT Hamirpur India

Graph neural networks(GNN) are a special variant of neural networks which help in dealing with unstructured data such as graph data. The advent of the GNN has helped in dealing with problems in different domains, especially in the domain of Natural Language Processing(NLP). In NLP, GNNs are used to implement tasks such as text classification which has a wide variety of applications. There are two ways to represent the text data using GNN namely, Inductive and transductive. In this paper, we apply the approach of the inductive model using different variants of GNN. We observed that the GAT variant gave better performance compared to other variants. Moreover, we observed that the complexity of the model and the dataset size influences the entropy of the output.

关键词： Computational modeling Text categorization Natural languages Graph neural networks Entropy Complexity theory Task analysis

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Cybernated Watermarking Approach on Image with Cryptographic Encryption

Cybernated Watermarking Approach on Image with Cryptographic...

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Computer, Communication and Control (IC4), International Conference on

作者： R. Sivasankari T Veeramakali S M Mustafa Nawaz P. Prakash P Umadevi A Alvin Ancy Dept. of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Dept of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Dept of Computer Science and Engineering Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Sri Sai Ram Engineering College Department of S & H KPR Institute of Engineering and Technology Coimbatore Dept of Electronics and Communication Engineering Anna University Chennai

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types; a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text.

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