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IoT, Communication and Automation technology (ICICAT), International Conference on

作者： Anil Kumar Umesh Chandra Jaiswal Information Technology & Computer Application Madan Mohan Malaviya University of Technology Gorakhpur U.P India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting children's societal and cognitive skills, leading to recurring behaviors, limited interests, communiqué challenges, and difficulties in societal interaction. Early detection of ASD can mitigate its sternness and long-term impact. Federated learning (FL) is a cutting-edge technique useful for precise ASD diagnoses in the early stages or preventing prolonged effects. This article applies FL technique uniquely to detect autism. It trains support vector machine (SVM) and local logistic regression (LR) classifiers for ASD classification in both children and adults. Results from classifiers affected by FL are sent to a central server, where a meta-classifier is trained to identify the most accurate approach for detecting ASD in both children and adults. Four distinct datasets related to ASD patients, each comprising over 700 records of affected individuals, were acquired from various repositories for feature extraction. The suggested model accurately predicted ASD in children with a 97% accuracy rate and in adults with an 85% accuracy rate.

关键词： Support vector machines Autism Pediatrics Logistic regression Accuracy Federated learning Psychology Predictive models Medical tests Servers

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Secure Virtual Machine Allocation for Prevention of Side Channel Attacks in Cloud Computing 1

Secure Virtual Machine Allocation for Prevention of Side Cha...

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1st International Conference on Intelligent Controller and Computing for Smart Power, ICICCSP 2022

作者： Rout, Chinmayee Sethi, Srinivas Badajena, J Chandrakanta Sahoo, Ramesh Kumar Computer Science Engineering and Application IGIT Sarang Utkal University Odisha Bhubaneswar India Computer Science Engineering and Application IGIT Sarang Odisha Bhubaneswar India Information Technology Outr Odisha Bhubaneswar India

ISBN: (数字)9781665472586

ISBN: (纸本)9781665472586

Cloud computing allows customers to use a variety of computing resources on-demand and with no maintenance overhead. One of the major issue concerning cloud computing is security. From the end user's perception, migrating to cloud exposes them to additional security risks that are entirely considered to be produced by other occupants who may have some access to shared resources. The co-location or co-residence attack, otherwise called as co-resident assault, is the focus of this research. This is a type of attack in which malevolent individuals construct side channels and steal confidential information from VMs that share the same server. Here we have studied on the co-resident attacks and the mechanisms to detect and prevent the attack. To address this issue, we have focused on the PSSF VM allocation policy as PSSF policy has high security with low energy consumption. © 2022 IEEE.

关键词： Cloud computing

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A Survey of Machine Learning Techniques Related to Understanding Autism Spectrum Disorder

A Survey of Machine Learning Techniques Related to Understan...

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Disruptive Technologies (ICDT), International Conference on

作者： Anil Kumar Umesh Chandra Jaiswal Information Technology & Computer Application Madan Mohan Malaviya University of Technology Gorakhpur (U.P) India

Speech, social skills, non-verbal communication, and repetitive activities are all indicators of autism spectrum disorder (ASD), a disease classified as a spectrum disorder by the DSM-5. Gender plays a role in the prevalence of ASD. If ASD isn't caught early enough, the damage is done. It's been a while since computer-aided ASD diagnosis made a lot of progress, which is talked about in this work. This article explores virtual reality for people with ASD with a focus on training and intervention. Our taxonomy categorizes prior VR training for people with ASD into two categories: immersive and regular (non-immersive) VR systems, which we present and put into practice. To fill up the knowledge gaps, we conducted a comprehensive analysis of the existing literature and offered suggestions for further investigation. It is the goal of this study to categorize and review various machine learning approaches, as well as analyze the characteristics of ASD and performance evaluation using various measures. People with ASD have difficulties with social interaction, communication, and other aspects of their cognitive and behavioural development. According to this review, the Random Forest method outperforms other algorithms.

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Enhancement of Farming Using IoT Based Unmanned Aerial Vehicles

Enhancement of Farming Using IoT Based Unmanned Aerial Vehic...

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Disruptive Technologies (ICDT), International Conference on

作者： Vanshika Tiwari Shiva Prakash Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur Uttar Pradesh India

The agriculture sector faces the pressing challenge of increasing food production while optimizing resource use and minimizing environmental impact. One promising solution to address these challenges is the integration of Internet of Things (IoT) based Unmanned Aerial Vehicles (UAVs) into farming practices. This research paper explores the potential benefits, challenges and implications of using IoT-based UAVs in agriculture. It examines how this technology can enhance farming by providing real-time data, precision agriculture capabilities and improved resource management. There are detecting diseases in agriculture field to reduce background noise using the Median filters, Temporal filters. This helps to remove noise and vestiges caused by the movement of the drone or the target factory.

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Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

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The Symphony of Style: An Extensive Examination of Determination Mechanisms in Deep Learning-Inspired Outfit Suggestions 2

The Symphony of Style: An Extensive Examination of Determina...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Guru Prasad, M.S. Bhosagi, Ravatappa A. Madhusudhana, K. Prithviraj Chandrappa, S. Kumar, Vikash Dehradun India School of Computing and Information Technology Reva University Bengaluru India Dept. of Electronics & Communication Engineering SDM Institute of Technology Ujire India NMAMIT Mangalore India Bengaluru India Department of Computer Application Graphic Era Deemed University Dehradun India

ISBN: (纸本)9798331530389

In the field of the fashion industry, the introduction of deep learning technology has taken an innovative revolution by providing assistance in the construction of intellectual systems that are intelligent to deliver personalized ensemble recommendations. In this research work, an innovative ensemble recommendation structure called Deep Learning VGG16 (Visual Geometry Group 16) recommender is presented. The proposed method allows a deep learning model VGG16 to understand complex patterns and developments resulting from a vast group of fashion images. In order to increase the user experience and greater participation in the well-established fashion industry, the system offers personalized ensemble ideas. The important, core and fundamental component of the system is VGG16, consisting of 13 convolutional layers and 3 fully connected layers, which is used to support the analysis of fashion images. A cutting-edge convolutional neural network (CNN) model is improved over a large and diverse collection of fashion images. Model will extract advanced high-level information and statistics with the capability to distinguish particulars in colour patterns, clothing combinations, and stylistic elements. Using this process of development, the system becomes better at identifying user preferences and fashion trends. © 2025 IEEE.

关键词： Convolutional neural networks

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Classification and Prediction of Medical Dataset Using Ensemble DeepQ Classifier 4th

Classification and Prediction of Medical Dataset Using Ensem...

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Salman, Rahama Gupta, Subodhini Jain, Neelesh Department of Computer Science SAM Global University MP Bhopal India Department of Computer Application School of Information Technology SAM Global University MP Bhopal India Department of Computer Science and Engineering SAM Global University MP Bhopal India

ISBN: (纸本)9789819923212

The clinical dataset consists of a huge amount of disease information and which marks negative effects with respect to features. Feature selection is a major key player removing redundant information. It also increases the decision making result in an effective manner. The important step in feature selections is classification and dimensionality reduction. Medical experts and researchers are using various machine learning techniques to predict and analyze the huge volume of data. The large scale volume of medical dataset is having number attributes and dimensions. Dimensions can affect the analytics and diagnose the results. In this paper we evaluate principle component analysis, support vector machine, factor analysis and ranking methods compared with our proposed CNN-based ensemble feature selection using K-nearest classifier. Our proposed method is followed as standard testing features with parameter optimization. Compared with the existing method our method has 94% of accuracy result by using TensorFlow. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Reinforcement Learning for Predictive Modeling and Management of Rare Genetic Disorders in Pediatric Healthcare 3

Reinforcement Learning for Predictive Modeling and Managemen...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Chaudhary, Pavan Inzimam Hannah Jessie Rani, R. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India Electrical and Electronics Engineering Faculty of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350343274

The difficulties caused by rare genetic abnormalities in pediatric healthcare highlight the need for advances in predictive modeling and treatment choices. An innovative method is discussed in the article 'Reinforcement Learning for Predictive Modeling and Management of Rare Genetic Disorders in Pediatric Healthcare.' Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critical are the three RL algorithms used in the suggested approach. To determine the most effective treatment for children with uncommon genetic illnesses, we used these algorithms to create a prediction model. To improve healthcare quality, we carefully evaluate the suggested approach alongside six alternative methods with overlapping but distinct names. We also take accuracy and precision into account while making our assessments. The suggested strategy, which we refer to as the 'Novel Genetic Predictor (NGP),' excels in every metric we tested. The results show a considerable improvement in the detection of rare genetic disorders in children. NGP also demonstrates an enhanced ability to make precise predictions, which opens the path for treatment alternatives that are both patient-specific and more effective. This research not only shows the promise of RL in pediatric healthcare, but also emphasizes the need for tailored care for children with rare genetic disorders. The results show that NGP is better than other methods, and they provide doctors a tool they may use to better care for their patients. © 2023 IEEE.

关键词： Reinforcement learning

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Utilization of ECC in Smart Grids: A Review

Utilization of ECC in Smart Grids: A Review

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Gopala Krishna, K. Shekhawat, Jyoti Agarwal, Trapty Faculty of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

Within the realm of smart grids, a power service provider plays a crucial function in coordinating predicted power production with actual power demand in order to support power system stability. Insufficient protection means that, even with these advancements, data sent inside smart grids is still vulnerable to a variety of security risks and assaults. To enhance the security of electronic grid networks, a robust and effective authentication procedure must be put in place. The trouble of ensuring the security of communications among substations and electronic machines stays, even in shrewd lattice settings where SCADA (supervisory controls and data acquisition) frameworks assume a part in shielding associations between the administration place and substations. In this present circumstance, predominant security strategies frequently neglect to accomplish an unpretentious harmony among security and execution. With the expansion of an safe part on the smart machine side, this work incorporates security insurance into a moderation verification framework in view of elliptic bend cryptography. This clever convention handles the requirement for a finely tuned balance between the security and execution parts of shrewd lattices. Key arrangement, common verification, and character security are significant parts of the proposed convention. The convention's viability is upheld by a complete assessment utilizing the Gong-Needham-Yahalom rationale. This procedure is a critical improvement in reinforcing the security position of smart grids, particularly taking into account the continually changing climate of cyber threats and weaknesses. © 2023 IEEE.

关键词： Authentication Elliptic Curve Cryptography Privacy Protection SCADA Systems Security Smart Grids

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Research on expressway traffic flow prediction model based on MSTA-GCN

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9317-9328页

作者： Ning, Tao Wang, Jiayu Duan, Xiaodong Institute of Computer Science and Engineering Dalian Minzu University Dalian116000 China Big Data Application Technology Key Laboratory of State Ethnic Affairs Commission Dalian Minzu University Dalian116000 China Institute of Information Dalian Maritime University Dalian116021 China

The traffic flow prediction of expressway is highly nonlinear and complex, so it is technically and operationally difficult to predict it accurately. In order to predict the traffic flow of expressway and alleviate its traffic congestion, a multi-component spatial–temporal graph convolution network model based on attention mechanism (MSTA-GCN) is proposed, which combines GCN, standard CNN and attention mechanism. Firstly, near period, daily period and weekly period are modeled to capture the nonlinear correlation characteristics in multivariable urban spatial–temporal series. Then input the sample data into the integrated model for training and extract the characteristics of traffic flow data. Finally, the model is tested on California Expressway PeMS D7 dataset. The experimental results show that MSTA-GCN is superior to classical shallow learning model, benchmark deep learning model and other traffic flow deep learning models, and has lower prediction error. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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