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An Effective Deep Learning based Recommender System with user and item embedding

An Effective Deep Learning based Recommender System with use...

2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Nareshkumar, R. Agalya, K. Arunpandiyan, A. Vijayalakshmi, M. Ranjani, V. Ramya, A. SRM Institute of Science and Technology Department of Networking and Communication Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India A. v C. College of Engineering Department of Computer Science and Engineering Mayiladuthurai India Care College of Engineering Artificial Intelligence and Data Science Trichy India Care College of Engineering Department of Computer Science and Engineering Trichy India Indra Ganesan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350334357

The success of deep learning in a variety of fields has spurred a rush of desire to develop new novel recommender systems. As a result, our research introduces new RecDNNing, a ground-breaking technique that combines integrated people and products with a deep learning model. There are two phases to the suggested suggestion method. We begin by constructing a unique mathematical version for each human and item, which we refer to as user and item embedding. The embeddings of the goods and users then are aggregated, concatenated, and sent into the deep learning model. In the second phase, the spliced people and items implants are utilised as sources, and the ratings scores are forecasted using the forward propagation technique. The suggested new RecDNNing algorithm outperforms state-of-the-art techniques on MovieLens, according to the results. © 2023 IEEE.

关键词： Recommender systems

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Dynamic Topology Management In Ad-Hoc Networks For Improved Performance 2

Dynamic Topology Management In Ad-Hoc Networks For Improved ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anakath, A.S. Kannadasan, R. Margarat, G. Simi Pandian, A. Pasumpon Antony Sibiya Varghese, V. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology VIT University Tamil Nadu Vellore India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering CARE College of Engineering Trichy India Department of Artificial Intelligence and Data Science GRT Institute of Engineering and Technology Tamil Nadu Tiruttani India

ISBN: (纸本)9798350383867

Optimization in ad hoc networks is a highly specialized task because the structure of the network is loosely formed, and each node is independently responsible for its operation. In order to obtain a better result from this solely formed networks this report offer extensive Dynamic Topology Management (DTM) method. Dynamic routing protocols lager lines, dividend balancing, Life-saving administration, guarantee of reliability, cross-layer design, security aspects and the on-time monitoring and discovery of topology changes are the main objectives and steps to be accomplished. Real-time Location monitoring and forest structure discovery are made possible by employing adaptive routing protocols, namely Dynamic Source Routing(DSR) and Ad-Hoc On-Demand Distance Vector(AODV). Also, the algorithms are capable of making advance adjustments to routing routes and resource allocations drawn from the mobility patterns forecasting which is vital. Load balancing methods, which distribute traffic in a network evenly, enable the system to run efficiently and avoid problems caused by overloading. Algorithms that are energy-efficient and make the best use of energy resources having their topology's optimized are introduced to the streamlined energy distribution systems. These algorithms are focused on the node's energy level. Through fault tolerance techniques which recognize and handle failures in nodes and partitioned networks, connectedness activity is a continuous one. Through QoS perception, the network topology might automatically be tailored to conform to the given quality criteria, thus not restricting the choice for differing applications. The proposed could have defenses against adversary nodes and their potential DDoS attacks which will enhance network protection. Yet another advantage of an architectural style that promotes cross-layer collaboration is enhanced topology management which is the goal around which the functions of various levels of the protocol stack revolve

关键词： Decision making

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3Dimensional improvise clustering algorithm for unmanned aerial vehicles: 3DICA

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International Journal of Information Technology (Singapore) 2024年第4期16卷 2563-2576页

作者： Gupta, Vinti Seth, Dambarudhar Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad India

A Flying Ad-hoc Network (FANET) is a group of Unmanned Aerial Vehicles (UAVs) that operate in an ad-hoc fashion, like Mobile Ad-hoc networks (MANET) and Vehicle Ad-hoc networks (VANET). The application of UAVs has been seen broadly used in disaster management, military operations, patrolling, and environmental investigations. Data must be transferred continuously in all scenarios in Flying Ad-hoc Networks, hence connectivity between all UAVs is necessary. Because UAVs are continually in the air, their location can be estimated in 3D space. This manuscript suggests a novel cluster routing protocol 3Dimensional Improvise Clustering Algorithm (3DICA) for handling the node uncertainty problem for cluster head selection. The author calculates the energy consumption through transmission after creating the cluster head and cluster members. The simulation findings demonstrate that the suggested technique delivers more data while taking less time to construct clusters in 3 dimensions. Manuscript compares the results with other clustering routing protocols. The proposed algorithm maintains consistency and security which leads to higher cluster stability and greater energy savings. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： 3-Dimensional Clustering Energy consumption FANET MANET UAVs

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The Multi-Way Path Over the Blockchain: The Development of the Security Algorithm RB-24-1 6

The Multi-Way Path Over the Blockchain: The Development of t...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Rajaprakash, S. Bagath Basha, C. Rathish Babu, T.K.S. Karthik, S. Subapriya, V. Kavitha, S. Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science & Engineering Kommuri Pratap Reddy Institute of Technology Telagana Hyderabad India Department of Computer Science & Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science & Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai India Department of Computer Science & Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

In the current world, a lot of new technologies are becoming famous. The Block Chain is one of these technologies. A very high amount of safety is offered by the technology in question;it is extremely robust. Users are not privy to a great deal of information regarding Block Chain;however, Protecting the data that is sent in multiple ways is what its security features are for. The "SalSa and RBJ25 algorithms" were still being used by this person. We are going to talk about the new "security mechanism" we have named RB-24-1. This method is comprised of these four different techniques. The procedure begins with the "Latin Alphabet code" being assigned to T. This is the first step in the process. To proceed to the next step, use Equation (1) to perform four times the multiplication of the number by itself. "Exchanging the values of the cells" is what the third step does., and it is carried out with the help of encrypted data. Putting the "SalSa" algorithm into action within the matrix constitutes the fourth step throughout the process. Finally, the plain text is converted into the encrypted text format. This has been a long time coming. There is a higher level of security offered by the method that has been suggested in compare and contrast with the method that is being used at the moment. © 2025 IEEE.

关键词： Encryption algorithms

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NR-Router+: Enhanced Non-Regular Electrode Routing With Optimal Pin Selection for Electrowetting-on-Dielectric Chips

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第9期43卷 2606-2619页

作者： Pan, Youlin Liu, Genggeng Huang, Xing Li, Zipeng Huang, Hsin-Chuan Liang, Chi-Chun Wang, Qining Kim, Chang-Jin Ho, Tsung-Yi Northwestern Polytechnical University School of Computer Science Xi'an710072 China Fuzhou University College of Computer and Data Science Fuzhou350116 China Apple Inc. Silicon Engineering Group CupertinoCA95014 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan University of California at Los Angeles Mechanical and Aerospace Engineering Department Los AngelesCA90095 United States The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

With the advances in microfluidics, electrowetting-on-dielectric (EWOD) chips have widely been applied to various biological and chemical laboratory protocols. Glass-based EWOD chips with nonregular electrodes are proposed, which allow more reliable droplet operations and facilitate the integration of optical sensors for many biochemical applications. Furthermore, nonregular electrode designs are utilized in EWOD chips, e.g., interdigitated electrodes for more reliable droplet manipulation, custom shaped electrodes for specific applications like concentric heating, etc. However, due to the technical challenges of fabricating multilayer interconnection on the glass substrate, e.g., unreliable process and high cost, both control electrodes and wires are fabricated with a single-layer configuration, which poses significant challenges to pin selection for nonregular electrodes. In this article, we propose a minimum-cost flow-based routing algorithm called NR-Router+ that features efficient and robust routing for single-layer EWOD chips with nonregular electrodes. To the best of our knowledge, this is the first work that overcomes the aforementioned challenges. We construct a minimum-cost flow algorithm to generate optimal routing paths followed by a light-weight model to handle flow capacity. A grid reduction strategy is proposed to reduce the computational overhead. Additionally, a flow collocation algorithm based on integer linear programming is presented to efficiently prevent wire overlapping. Experimental results show that NR-Router+ achieves 100% routability while minimizing wirelength with shorter run time. Moreover, NR-Router+ can generate mask files feasible for manufacturing via adjustments of design parameters, thus demonstrating its robustness and efficiency. © 1982-2012 IEEE.

关键词： Electrodes

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Supervised deep learning for content-aware image retargeting with Fourier Convolutions

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Multimedia Tools and Applications 2024年第36期83卷 83611-83627页

作者： Givkashi, MohammadHossein Naderi, MohammadReza Karimi, Nader Shirani, Shahram Samavi, Shadrokh Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Electrical and Computer Engineering McMaster University HamiltonONL8S 4L8 Canada Computer Science Department Seattle University SeattleWA98122 United States

Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datasets are unavailable for training deep learning models in the image retargeting tasks. As a result, we present a new supervised approach for training deep learning models. We use the original images as ground truth and create inputs for the model by resizing and cropping the original images. A second challenge is generating different image sizes in inference time. However, normal convolutional neural networks cannot generate images of different sizes than the input image. To address this issue, we introduced a new method for supervised learning. In our approach, a mask is generated to show the desired size and location of the object. Then the mask and the input image are fed to the network. Comparing image retargeting methods and our proposed method demonstrates the model’s ability to produce high-quality retargeted images. Afterward, we compute the image quality assessment score for each output image based on different techniques and illustrate the effectiveness of our approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolution

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Machine Learning-Based Optimization for Identifying Effective Drugs in Breast Cancer on an in Vitro Platform 4

Machine Learning-Based Optimization for Identifying Effectiv...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Saxena, Mahima Rao, P.V.V.S. Eswara Choudhary, Amar Reddy, P. Srinivas Sharma, Vipin Kumar Kumar, Vinish Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Sasi Institute of Technology and Engineering Department of Computer Science Tadepalligudem India Department Electronics and Communication Engineering Bengaluru India Cvr College of Engineering Mechanical Engineering Department Rangareddy India Ims Engineering College Department of Mca India Ghaziabad India

ISBN: (纸本)9798350360165

In this innovative quest for more efficient irrigation, we investigate the combined use of sensor technology and machine intelligence to optimize agricultural water usage. We collect precise data on essential variables, including water flow, temperature, humidity, soil moisture, and water level, every 15 days by promptly deploying sensors in the field to monitor them in real-time. We use the vast amount of data available to us to train machine learning models, including Recurrent Neural Networks (RNN), K-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), specifically for streams that are anticipated to have a certain distribution. CNN has exceptional precision with a decoding accuracy of 94.5% in visual signal interpretation, surpassing ANN, KNN, and RNN which achieve lower accuracies of 91.1%, 88.7%, and 83.6% respectively. Our investigation uncovers a fluid interaction between sensor data and model training, leading to each model exhibiting unique characteristics. These findings not only showcase the accuracy with which machine learning can distribute water, but they also signify a noteworthy progress in resource-efficient and sustainable agriculture. In addition to generating mathematical discoveries, our research establishes the foundation for advanced irrigation systems that smoothly integrate mechanical and natural processes in agricultural fields. © 2024 IEEE.

关键词： Soil moisture

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Exploring Deep Learning and Word Embedding Techniques for Sentiment Analysis on Diverse Textual Data 3rd

Exploring Deep Learning and Word Embedding Techniques for Se...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Lavanya, B.N. Rathnam, K. V. Anitha Kiran, K. Appaji, Abhishek Shenoy, P. Deepa Venugopal, K.R. Department of Computer Science and Engineering University of Visvesvaraya College of Engineering BangaloreUniversity Bengaluru India Department of Medical Electronics BMS College of Engineering Bangalore India

ISBN: (纸本)9789819743582

The rapid expansion of data poses a significant challenge for analyzing sentiments. The importance of user-generated reviews highlights the need to carefully curate and evaluate text data to extract opinions. This research delves into the world of learning for sentiment analysis exploring models such as Bi-LSTM, CNN, and GRU along with various word embeddings like N-grams, Keras embedding, BERT, Roberta, CT-Bert, and Elmo. It effectively categorizes sentiments and detects sarcasm in Twitter and IMDB data making it a vital tool for applying learning to real-world sentiment analysis tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sentiment analysis

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Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 3043-3056页

作者： S.Ashwini S.Magesh Kumar Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiTamilnaduIndia

Increase in the use of internet of things owned devices is one of the *** publicly available network many kinds of phishing attacks are able to enter into the mobile devices and corrupt the existing *** Phishing is the slow and resilient attack stacking techniques probe the *** proposed model is focused on detecting phishing attacks in internet of things enabled devices through a robust algorithm called Novel Watch and Trap Algorithm(NWAT).Though Predictive mapping,Predictive Validation and Predictive analysis mechanism is *** the test purpose Canadian Institute of cyber security(CIC)dataset is used for creating a robust prediction *** attack generates a resilience corruption works that slowly gathers the credential information from the *** proposed Predictive analysis model(PAM)enabled NWAT algorithm is used to predict the phishing probes in the form of suspicious process happening in the IoT *** prediction system considers the peer-to-peer communication window open for the established communication,the suspicious process and its pattern is identified by the new *** proposed model is validated by finding thepredictionaccuracy,Precision,recallsF1score,errorrate,Mathew’sCorre-lationCoefficient(MCC)andBalancedDetectionRate(BDR).Thepresented approach is comparatively analyzed with the state-of-the-art approach of existing system related to various types of Phishing probes.

关键词： Cyber security internet of things phishing attacks fault-tolerant devices smart devices cyber security attacks

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Leveraging Convolutional Neural Networks for Enhanced Educational Question Classification

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SN computer science 2024年第8期5卷 1052页

作者： Deena, G. Ancy Breen, W. Raja, K. Shanmuganathan, C. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Ramapuram Chennai India

Question classification is a critical task in natural language processing, as well as applications in question-answering systems and intelligent personal assistants. This study presents novel machine learning approach for automatic question classification based on Bloom’s taxonomy, a hierarchical structure of cognitive levels. Traditional methods rely on feature engineering and classic machine learning models, but these methods often lack the ability to understand complex patterns and connections in natural language data. This study proposes a novel approach using a convolutional neural network (CNN) model, which can automatically learn relevant features from input data without extensive feature engineering. The model classifies questions into pre-established categories or labels using an embedding layer, convolutional and pooling layers, and a fully linked layer for classification. We assess the model’s efficacy on a publicly accessible dataset generated by the subject expert and compare it to conventional machine learning methods like logistic regression and support vector machines. The CNN-based model outperforms conventional techniques, achieving greater recall, accuracy, precision, and F1-score. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bloom’s Taxonomy Convolutional Neural Network Machine Learning Word embedding

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