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Smart Devices (ICSD), International Conference on

作者： Navin Kumar Agrawal Viboo Sharma Pranita Singh Savya Sachi Arpit Jain Md Mohtab Alam Department of Computer Engineering and Applications GLA University Mathura U.P. India Department of IT Hindustan College of Science and Technology Mathura U.P. India Department of Computer Science and Engineering Roorkee Institute of Technology Roorkee U.K. India Department of Information Technology L N Mishra College of Business Management Muzaffarpur Bihar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science & Engineering Eklavya University Damoh M.P. India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

Issues like low vision and the distortion of spectral and spatial information in photographs can be brought on by unfavorable atmospheric circumstances including smog, rain, overcast sky, fog, and smoke. This piece looks at images taken in a hazy environment. While de-smogging requires veil and transmission map data, optical imaging systems are limited to capturing smoggy images. To restore blurry photographs, effective optical information prediction is necessary. Research has shown that optical information anticipated by the dark channel prior technique frequently produces subpar images in photographs with areas of higher brightness, noticeable pollution gradients, or textured information. In order to estimate optical information, this study makes use of Deep Transfer Learning (DTL) and Optimized Gradient Prior Prediction (OGPP).We gathered benchmark images of both smoggy and smog-free environments in order to train the DTL model. The OGPP technique uses the smog gradient, which the DTL model is taught to anticipate, to recover pictures free of pollution. A comparison analysis shows that when it comes to recovering hazy photos, the OGPP restoration model performs better than other approaches.

关键词： Integrated optics Performance evaluation Pollution Rain Transfer learning Optical distortion Predictive models Optical imaging Image restoration Smart devices

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Efficient Task Scheduling in Cloud Environment Based On Dynamic Priority and Optimized Technique

Efficient Task Scheduling in Cloud Environment Based On Dyna...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Karthik, G.M. Gupta, Ankur Rajeshgupta, Sh. Jha, Amaresh Sivasangari, A. Mishra, B.P. Vellore Institute of Technology School of Computer Science and Engineering Vellore India RIMT University Department of Cse Godindgarh Mandi India Sanskriti University Department of Management Uttar Pradesh Mathura India University of Petroleum and Energy Studies School of Modern Media India Ut Sathyabama Institute of Science and Technology India Electronics Jss Academy of Technical Education Noida India

ISBN: (纸本)9798350322309

Cloud computing emerging filed process large task to handle the resources. From an application perspective, the study of task scheduling mechanisms for data transfer in large cloud computing environments is performed *** scheduling leads to traffic load, loss of energy and hardware controls failure. In addition, resident's devices are not considered to reduce power consumption delay. So, the Internet of things (IoT) rules the current trends of the Internet. The huge number of things (objects), which are associated with the Internet, produces a large amount of information that needs a ton of exertion and tasks preparation to make it valuable. To resolve this problem, we propose a dynamic multi-level task scheduling (DMLTS) based in cascade shrink priority (CSP) to allocate task to optimize the scheduling. With intent a TacticalLoad Balancer (TLB) and The Preemptive Flow Manager (PFM) is responsible for the application of load balancing strategy based on the mixed load balancing algorithm improves the task allocation better to balance load to improve the response time. Experimental results have been demonstrated with respect to better load balancing, lower power rate, and time consumption rate in both phase and random uniform propagation. Simulated results performance of this process can reduce data processing time and achieve load neutralization. © 2023 IEEE.

关键词： Cloud computing

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Identification and Characterization of Spammers from Lists of Recipients 2

Identification and Characterization of Spammers from Lists o...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Shinde, Sharmila Chitre, Vidya Patil, Seema Jitendra Meganathan, R. Kaliappan, Seeniappan Dhyani, Bijesh Jayawantrao Sawant College of Engineering Department of Computer Engineering Maharashtra Pune411038 India Vidyalankar Institute of Technology Department of Information Technology Maharashtra Mumbai400037 India Dr. Vishwanath Karad Mit World Peace University School of Computer Engineering and Technology Maharashtra Pune411038 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Velammal Institute of Technology Department of Mechanical Engineering Tamil Nadu Chennai601204 India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350315905

For millions of users and their friends, online social networks (OSNs) are popular platforms for communication and cooperation. Regrettably, they may be efficient instruments for running spam campaigns and dispersing malware when in the wrong hands. Social spam is more effective than traditional email because users are logically more inclined to reply to messages from friends on Facebook than from complete strangers. In fact, evidence already in existence indicates that hostile actors are seeking to steal OSN account credentials in order to fund these "high-return"spam campaigns. All the more definitively, we see spam crusades that are coordinated and begun on KETTO, a sizable publicly supporting stage situated in India, and we follow the group laborers to their spamming exercises on Quora, the country's greatest local area based Q & A (QA) stage. We can recognize the total biological system that upholds publicly supported spam attacks by associating the spam crusades, laborers, spammer accounts, and spamming ways of behaving. We give a careful and scientific assessment of the biological system from a few points, including the size and degree of spam assaults, Sybil records and spammers' conniving strategies, work costs and monetary motivations, quality looks at conveyed by spam crusades, and so forth. © 2023 IEEE.

关键词： Biological systems

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An enhanced Active Reinforcement Learning for Autonomous Robotics in Industrial automation

An enhanced Active Reinforcement Learning for Autonomous Rob...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： V. Aravinda Rajan T. Marimuthu Rajat Bhardwaj Rishi Prakash Shukla Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar Tamil Nadu INDIA School of Computer Science and Engineering RV University Bengaluru India AIT Management Chandigarh University Mohali Punjab

An enhanced active reinforcement learning technique has been proposed to enable autonomous robots to operate and execute tasks in industrial automation. This approach combine hierarchical reinforcement learning and Bayesian optimization, to acquire knowledge from complex real-world environments and acquire optimal policies which can enable autonomous robots to perform collaborative tasks efficiently. The main advantage of this enhanced active reinforcement learning approach is the capability of the autonomous robot to autonomously adapt its movements and decision-making strategies when new tasks are required. It allows for the robot to explore its environment and learn how to complete tasks optimally while reducing the burden of manual intervention. Moreover, the proposed approach can generalize its knowledge to establish rewarding collaborative behaviors between robots and humans, thus allowing for collaborative human-robot interactions. This will be beneficial in performing industrial automation with robot cooperative tasks and optimize the efficiency of the industrial automation system..

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Recognition and Integration of AI with IoT for Innovative Decision Making Techniques in Cyber Physical Systems

Recognition and Integration of AI with IoT for Innovative De...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Shivakumar Nethani Lingala Sivaranjani Marrapu Aswini Kumar Bechoo Lal Mohit Tiwari Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad Telangana India School of Computing Mohan Babu University Rangampet Tirupati Andhra Pradesh India Department of Computer Science and Engineering Centurion University of Technology and Management Vizianagaram Andhra Pradesh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation (KLEF) KL University Guntur Andhra Pradesh India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

With the integration of Internet of Things (IoT) devices and the demand for effective decision-making methods, cyber-physical systems (CPS) are getting more complicated. In CPS, machine learning (ML) has become a potent technique for processing and analyzing massive volumes of data. The problem remains in optimizing ML algorithms to accomplish quick and accurate decision-making. To overcome this difficulty, we provide a novel strategy in this paper that integrates ML with Particle Swarm Optimisation (PSO) and IoT. The suggested framework optimizes ML algorithms for CPS using PSO, an optimization technique inspired by nature. To find the optimum solution in a multidimensional space, PSO imitates the behavior of a swarm of particles. Our goal is to improve the precision and effectiveness of decision-making processes in CPS by combining PSO with ML techniques. Additionally, by integrating IoT devices into CPS, a wealth of real-time data is made available, enabling the collection of diverse and dynamic data from numerous sources. Our method uses this data to analyze patterns, trends, and anomalies using ML algorithms, enabling wise decision-making in real time. The advantages of our suggested strategy are dual. First, PSO optimization increases the efficiency of ML algorithms and lessens the computational complexity related to large-scale datasets. Second, the framework's inclusion of IoT devices makes it possible to employ real-time data, leading to more precise and rapid decision-making.

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Customer Churn Prediction Using Tunicate Swarm Optimization Based Hybrid Machine Learning Algorithms 2

Customer Churn Prediction Using Tunicate Swarm Optimization ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Haroon P S, Abdul Lateef Kala, S. Padma Bindu, G. Husseen, Ahmad Naresh, V. Ballari Institute of Technology and Management Department of Ece Karnataka Ballari India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of Cse Tamilnadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India The Islamic University Najaf Iraq Malla Reddy Engineering College for Women Dept of Eee Telangana India

ISBN: (纸本)9798350382952

The customer churn is referred as number of previous customers who leads to leave the service provider through particular time. The main goal is to predict churn customers as early stage and find the cause for its churn. This paper proposed a Tunicate Swarm Algorithm-Support Vector Machine-Improved Random Forest (TSA-SVM-IRF) for predicting customer churn. The IBM Telco dataset is used in this paper and it is preprocessed by one hot encoding. At this time, the data is converted into numerical form. The TSA is used for feature selection which overcomes dimensionality detection and vanishing gradient issues. Then, the SVM-IRF is used for predicting churn customers at early stage. The f1-score, recall, precision, and accuracy are used to estimate the model performance. TSA-SVM-IRF attains 97.59% f1-score, 98.86% recall, 96.64% precision and 96.82% accuracy when compared to existing techniques like Grey Wolf Optimization and Ensemble Neural Network (GWO-ENN), and Swish Recurrent Neural Network (S-RNN). © 2024 IEEE.

关键词： Support vector machines

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Convolutional Neural Network Model With Hybrid Feature Learning For Sematic Text Mining 2

Convolutional Neural Network Model With Hybrid Feature Learn...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Patil, Shrinivas Kolekar, V.K. Singh, K. Ranjith Sharma, Pratibha Hariharasudhan, S. Goyal, Shubhi School of Management - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India

ISBN: (纸本)9798350388916

In this article, we present a generic inference hybrid structure for Convolutional Recurrent Neural Network (conv-RNN) text semantic modelling, combining the strengths of convolutional and recurrent neural network structures in extracting distinct linguistic features. In addition to the conv-RNN-based sentence matching as well as classification models, we also offer a new sentence classification model as well as an attention-based response selection model with improved performance. Two difficult answer selection (AS) tasks & five standard datasets for sentence classification (SC) are used to verify the suggested models. As far as we are aware, these are the most comprehensive comparison findings in AS and SC to date. We demonstrate the superiority of conv-RNN over other state-of-the-art methods experimentally on a variety of hard tasks and benchmark datasets. Deep training-based architectures outperform ensemble learning methods and supervised training approaches for the sentiment analysis issue in educational data mining. Overall classification accuracy was 95.80% for long-term short-term memory networks using representations built upon the GloVe word-embedding method, making them the most efficient prediction arrangement. © 2024 IEEE.

关键词： Convolutional neural networks

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Intrusion Detection and Prevention System for DDoS Attacks using Hybrid CNN Architecture 2

Intrusion Detection and Prevention System for DDoS Attacks u...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Rathika, S. Aggarwal, Diksha Xavier, V.K. Rege, P.R. Thangarasu, N. Kaur, Prabhjot Prince Shri Venkateshwara Padmavathy Engineering College Department of Business Administration Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India School of Management - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798350388916

Machine-based intrusion detection systems are crucial for network protection and data security in this age of widespread Internet connectivity and consistent growth in data quantities. However, current methods of both shallow and deep learning fall short when trying to capture the intricate incursion patterns associated with data transmission. To combat this, we offer a large data-based hierarchy deep learning system to further improve the effectiveness of IDS-based artificial intelligence. Both network traffic as well as content information may be captured by the proposed system thanks to its behavioral and content-functional features. Each framework deep learning model is dedicated to understanding the data structure of a specific cluster. For this reason, we advocated the use of a convolutional Neural Network (CNN) using a Min-Max optimization approach. When compared to earlier single-learning models, the suggested method increases the categorization rate of disruptive attacks. Incorporating a larger number of devices also speeds up the system's model-building process. © 2024 IEEE.

关键词： Network intrusion

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Hate Speech Detection Using the GPT-2 and Natural Language Processing

Hate Speech Detection Using the GPT-2 and Natural Language P...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Savya Sachi Ajay Kumar Singh Arpit Jain Suman Devi Yogesh Kumar Sharma Senthil Athithan Department of Information Technology Lalit Narayan Mishra College of Business Management Muzaffarpur Bihar India Allenhouse Institute of Technology Kanpur Uttar Pradesh India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Guntur A.P. India Department of Computer Science and Engineering Chaudhary Devi Lal University Sirsa Haryana India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Guntur AP Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. India

Hate speech on social media has increased as people use it to communicate and express their thoughts. Automatic hate speech detection has been studied in several languages. This study investigates the conclusion of the performance evaluation of the machine learning model GPT-2 (Generative Pre-trained Transformation 2) for natural language processing. The proposed model is developed and evaluated using a hate speech dataset. We attained an accuracy of about 97.12 percent, higher than the existing models called biLSTM, CNN, CNN-LSTM, and BERT.

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Automating Thinning Theorem: Synthesizing Efficient Dynamic Programming Algorithms

arXiv

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arXiv 2022年

作者： Ruyi, J.I. Tianran, Z.H.U. Xiong, Yingfei Zhenjiang, H.U. Key Lab of High Confidence Software Technologies Ministry of Education Department of Computer Science and Technology School of Computer Science Peking University Beijing China

Dynamic programming is an important optimization technique, but designing efficient dynamic programming algorithms can be difficult for even professional programmers. Thinning, a technique developed for systematically deriving efficient dynamic programming algorithms, has received much attention in studies because of its effectiveness for a large class of problems. Despite the success of thinning in theory, its practical usage is still limited because (1) applying thinning requires mathematical and algorithmic background, and (2) applying thinning solely may not be enough to generate algorithms as efficient as proposed by human experts. In this paper, we propose two approaches, MetHyl and MetHyl+, to resolve both problems. First, MetHyl automates the application of thinning via program synthesis, and thus eliminates the burden to the user for applying thinning. Second, MetHyl+ integrates three rules into MetHyl that optimizes three important factors on the time complexity of dynamic programming algorithms that are ignored by thinning, and thus make it able to automatically generate expert-level dynamic programming algorithms on many tasks. We evaluate our approaches on 37 tasks related to 16 optimization problems collected from Introduction to Algorithm, a popular textbook for algorithm courses. The results show that MetHyl+ achieves exponential speed-ups on 97.3% tasks with an average time cost of less than one minute. Moreover, MetHyl+ generates algorithms that are as efficient as the reference programs provided by human experts on 70.3% tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

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