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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Gondi Konda Reddy Jayakiran Reddy Esanakula Prajna K B Kamlesh Chandra Purohit S. Kaliappan Ramya Maranan Department of Mechanical Engineering Sreenidhi Institute of Science and Technology Hyderabad Telangana India Department of Electronics and Communication NITTE Meenakshi Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

Urban areas frequently experience snatch stealing, a severe threat to public safety. It entails forcibly seizing a victim's personal items, including purses or cell phones, before scurrying away from the scene. Due to how quickly they happen, it is difficult to identify snatch theft instances in real-time. The systems now in use to identify the snatching occurrences mainly rely on human participation, which may result in lengthy delays and mistakes. Consequently, there is a need for an automated method that can quickly and effectively identify these situations. Preprocessing, segmentation, feature extraction, and model training are all used in the suggested method. Gaussian Model is utilized for preprocessing. Canny edge is utilized for segmentation. LSTM-AE is used to train the model. Two further models, LSTM and AE, are contrasted with the suggested method. When compared to two existing approaches, the proposed strategy performs exceptionally well.

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Escaping Strategy with Golden Jackal Optimization Based Clustering and Routing in Mobile Ad Hoc Network

Escaping Strategy with Golden Jackal Optimization Based Clus...

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Data science and Network Security (ICDSNS), International Conference on

作者： Ahmad Alkhayyat Komuravelly Sudheer Kumar Murigendrayya M Hiremath Abhijeet Das Madhura G K Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Department of Medical Electronics Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Civil Engineering C.V. Raman Global University (CGU) Bhubaneswar Odisha India Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

The Mobile Ad Hoc Network (MANET) is organized with various nodes which communicates wirelessly. Therefore, the communication in MANET suffered from different problems like low stability, high energy consumption and link failure. Additionally, the path failure happens in network because of unrestricted movement of node, that maximized energy usage, delay and minimized nodes lifetime. In this research, the Escaping Strategy with Golden Jackal Optimization (ES-GJO) algorithm is developed for cluster head selection and routing in the MANET network. The appropriate Cluster Head (CH) and routes are chosen using ES-GJO algorithm that boosts network lifetime and predicts link failure with less energy consumption. The ES-GJO algorithm is estimated with metrics of Packet Delivery Ratio (PDR), Delay and Energy consumption for 100 to 500 nodes. The proposed ES-GJO algorithm attained highest PDR of 99.87%, 99.83%, 99.65%, 98.76% and 97.38% for nodes 100, 200, 300, 400 and 500 which is efficient than Eagle Based Density Clustering (EBDC) and Optimal Route Selection Mobile Ad hoc Networks (ORSMAN) algorithms.

关键词： Measurement Energy consumption Clustering algorithms Network security Data science Prediction algorithms Routing Delays Optimization Mobile ad hoc networks

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A Powerful Algorithm for e-Commerce Credit Risk Analysis

A Powerful Algorithm for e-Commerce Credit Risk Analysis

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Data science and Network Security (ICDSNS), International Conference on

作者： Dayanand Lal N Sandhya Guruswamy Vuyyuru Lalitha Mohan Awasthy Piyush Kumar Pareek Dept. of CSE GST GITAM University Bengaluru Campus Bengaluru India School of computing and information Technology Reva University India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of Engineering and Technology Bharati Vidyapeeth Peeth Deemed to be University Navi Mumbai India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

It is critical to build a system and model for analysing the credit risk associated with e-commerce to decrease the chance of credit defaults occurring during online transactions. This approach examines a client's income, assets, negative transaction rate, reputation, and third-party credit evaluation findings to determine creditworthiness. The risk assessment index-based credit risk analysis model is built using the RB-XGBoost approach. The credit risk analysis algorithm's training process employs a data balancing technique to increase the system's capacity to forecast accurately. This method is designed to solve the problem of class imbalance in machine learning datasets. The training set is separated into groups at this point, and each group is compared to a specified threshold. The dataset is then made more representative by combining oversampling and undersampling. The system's capacity to analyse the credit risk presented by online shops is improved by balancing data subsets. Online retailers might utilise the proposed model and indexing approach to better assess their clients' creditworthiness and thereby reduce the number of consumers whose payments are late. By suggesting a unique way to address class imbalance in machine learning datasets, this study adds to the expanding body of work on credit risk assessment in online transactions.

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Malware Detection and Classification for Internet of Things Using Self-Attention Based Long Short-Term Memory

Malware Detection and Classification for Internet of Things ...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sudhakar K Saif O. Husain D. Raja Babu M. Ashok Kumar R. Dineshkumar Dept. of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Department of Computer Science Software Engineering and Cyber Security Skyline University Nigeria Kano State Nigeria Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The Internet of Things (IoT) connects numerous intelligent devices providing security features that interact with default settings accessed through applications. Additionally, Deep Learning (DL)-based mechanisms was involved for malware detection to identify and mitigate attacks. The Adaptive Synthetic Sampling (ADASYN) technique addresses class imbalances, especially when splitter class instances are distributed data in complex patterns. The proposed Self-Attention based Long Short-Term Memory (LSTM) classification method facilitates the handling of long-range dependencies in transferred data sequences offering high flexibility and adaptability to varying input lengths. For feature selection Particle Swarm Optimization (PSO) is utilized iteratively optimizes a solution in a high-dimensional space although it may have a low convergence rate due to its iterative process. The obtained results demonstrate the proposed Self-Attention based LSTM model achieves better accuracy reaching 98.05% on the NSL-KDD dataset and 99.01 % on the UNSW-NB15 dataset. These results ensure accurate classification compared to other existing methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN).

关键词： Adaptation models Recurrent neural networks Training data Malware computer networks Internet of Things Convolutional neural networks

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DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries

arXiv

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arXiv 2023年

作者： Mehra, Ashman Saha, Snehanshu Raychoudhury, Vaskar Mathur, Archana Dept. of CSIS BITS Pilani K K Birla Goa Campus Goa India Dept. of CSIS and APPCAIR BITS Pilani Goa India HappyMonk AI Bangalore India Department of Computer Science and Software Engineering Miami University Miami United States Dept. of Information Science and Engineering Nitte Meenakshi Institute of Technology India

Delivery of items from the producer to the consumer has experienced significant growth over the past decade and has been greatly fueled by the recent pandemic. Amazon Fresh, Shopify, UberEats, InstaCart, and DoorDash are rapidly growing and are sharing the same business model of consumer items or food delivery. Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path. We observe a significant scope for reducing the costs associated with completing deliveries under the current model. We model our food delivery problem as a multi-objective optimization, where consumer satisfaction and delivery costs, both, need to be optimized. Taking inspiration from the success of ride-sharing in the taxi industry, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Unlike previous attempts for path-sharing, DeliverAI can provide real-time, time-efficient decision-making using a Reinforcement learning-enabled agent system. Our novel agent interaction scheme leverages path-sharing among deliveries to reduce the total distance traveled while keeping the delivery completion time under check. We generate and test our methodology vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 12%, the distance traveled by 13%, and achieve 50% higher fleet utilization compared to the baselines. © 2023, CC BY.

关键词： Reinforcement learning

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Traffic Congestion Prediction in Smart Cities using Multilevel-Gated Recurrent Unit

Traffic Congestion Prediction in Smart Cities using Multilev...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Busani Sravani Shreyas A V Haider Mohammed Abbas Yerrolla Chanti S. Punitha Malla Reddy Engineering College for Women Hyderabad India Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Department of Mathematics Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation (Deemed to be University) Salem India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Traffic Congestion (TC) is a main issue in smart cities because of increase in urbanization, as a result in financial losses. However, existing prediction algorithms failed to predict TC effectively due to dynamic changes of road network. To overcome this problem, a Multilevel-Gated Recurrent Unit (MGRU) is proposed for accurate prediction of traffic flow and congestion avoidance in smart cities. The main advantage of proposed M-GRU model is that it reduces errors while prediction by multilevel layers in softmax that passes information to adapt dynamic changes of road network. The data acquired based on weather conditions, number of vehicles improved the prediction process which utilized a few factors for prediction than previous prediction methods. Then the traffic density is estimated to predict TC on the road. Finally, the TC is predicted accurately by the proposed M-GRU model. The experimental results of the proposed method attained better results accuracy of 0.887, Mean Absolute Error (MAE) is 82.34 which is less than compared to existing method such as Convolutional Neural Network and Bidirectional-Long Short-Term Memory (Conv-Bi-LSTM).

关键词： Adaptation models Accuracy Smart cities Roads Heuristic algorithms Predictive models Prediction algorithms Vehicle dynamics Traffic congestion Meteorology

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A Management of Food Supply Chain in Sustainable Smart Cities using Fire Hawk Optimization

A Management of Food Supply Chain in Sustainable Smart Citie...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： B Girirajan B. Raveendra Naick Hassan M. Al-Jawahry Revanasiddappa Rana Veer Samara Sihman Bharattej R Department of ECE School of Engineering SR University Warangal Telangana India Dept. of CSE(AI&ML) School of Computing Mohan Babu University Tirupati India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science Saint Leo University Florida United States

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Nowadays, the urban cities are facing increasing strain due to the swift increase in population inside metropolitan areas. It is anticipated that the centered strategy for smart cities will address both the ecological environment and urban life. The food business is one of major IoT application areas in smart cities. In smart cities, IoT technology aid in the real-time monitoring, analysis, and management of the food business. In this study, an Internet of Things (IoT) based Dynamical Food Chain of Supply for Smart Cities using Dynamic Vehicle Routing (IFSCDVR) using Fire Hawk Optimization (FHO) technique is suggested, which guarantees food quality while also offering intelligent vehicle routing and the ability to identify the reasons for contamination of food. This strategy would increase the effectiveness and precision of the supply chain network with the smallest possible dataset size. The findings demonstrate that the suggested system works better than the current methodologies. The proposed IFSCDVR-FHO achieved overall performance of 91.34% of accuracy, 89.56% of precision, 89.68% of recall and 90.21% of f1. score.

关键词： Accuracy Smart cities Computational modeling Supply chains Routing Data models Sensors

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Smart Modular Few-Shot Learning Framework Changes Classification of Signal Modulation

Smart Modular Few-Shot Learning Framework Changes Classifica...

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Data science and Network Security (ICDSNS), International Conference on

作者： Spoorthi T Husna Tabassum S.V. Devika K. B. V. Brahma Rao Ramachandra A C Dept. of CSE GST GITAM University Bengaluru Campus Bengaluru India Dept. of CSE HKBK College of Engineering Bengaluru India Department of ECE Hyderabad Institute of Technology and Management Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The purpose of this research is to categorise signal modulation using few-shot learning. The first approach is an intelligent, modular, few-shot learning framework. This system's two components are IQF and 1D-SFP, and it also has a task-driven network design. The 1D-SFP module is responsible for obtaining high-level characteristics from the raw signal, while the IQF module is in charge of retrieving low-level information. The number of 1D-SFP modules required for processing a large amount of data is calculated by studying the correlation in the hidden layer. We evaluate the suggested method using a publicly available dataset and show that it beats standard strategies in terms of accuracy. It can also learn from a small number of instances and is adaptable enough to handle innovative modulation schemes. This enables it to be used in wireless networks for data transmission and reception. The second approach uses a graph convolutional network (GCN) to calculate how many 1D-SFP modules should be integrated into a neural network architecture for a particular job. On benchmark datasets, the presented frameworks outperformed a range of state-of-the-art, conventional methodologies. Both approaches perform preprocessing, representation learning, and classification using their own distinct algorithmic frameworks. The presented strategies have the potential to increase signal modulation classification precision and efficiency in cognitive radios and other kinds of wireless communication. These gains are possible because of the potential of these techniques.

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Block chain based medical reports monitoring system 4

Block chain based medical reports monitoring system

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4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020

作者： Thippeswamy, M N. Sai Kiran, B. Mohan Tanksali, Pawan R Hegde, Monica Naik, Pooja Rani Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Yelahanka Bangalore560064 India UI Mobile Developer One Direct Bangalore560096 India

ISBN: (纸本)9781728154640

These days the healthcare frameworks are attempting to provide central healthcare general administrations. Frequently the frameworks are consistently on the verge of breakdown which was inferable from the high costs, enormous scope measurements and rare assets. In the current scenario, a centralized cloud-based system is used. When a centralized system is used, the stored data is at risk as security is the main concern. If there's a single breach in the centralized system, the entire data becomes easily accessible. A Blockchain, being a distributed network for storage of data can overcome the mentioned problem. The data blocks in the Block chain are immutable and secure. In this research a system is introduced where the patient is given complete control over his medical records. It mainly comprises three roles namely Doctor, Patient and Management. The patient has monopoly over his medical records which provide him the authority to grant access over his medical records. The Medical records are securely stored using the Inter Planetary File System. Thus, one can completely rule out the part of a mediator and the patients have complete command over their medical information. © 2020 IEEE.

关键词： Blockchain

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Transformer Encoder and Decoder Method for Forest Estimation and Change Detection

Transformer Encoder and Decoder Method for Forest Estimation...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Kiran Kashinath Kotin Sharan Kumar Haider Alabdeli Gotte Ranjith Kumar Ramachandra A C Department of ECE Smt. Kamala and Sri Venkappa M. Agadi College of Engineering and Technology Lakshmeshwar Karnataka India Department of ECE H.K.E. Society's S.M.V. College of Engineering Raichur Karnataka India The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Warangal India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Forest Change Detection (FCD) is difficult component of natural assess monitoring and conservation strategy, enabled informing decision-making. Different techniques using strength of Artificial Intelligence (AI) has implemented to detect and classify modifications in forest cover by Remote Sensing (RS) data. In this research, proposed a Transformer Encoder and Decoder (TED) method for forest estimation and change detection. The Multi Feature Fusion (MFF) is used for effective combine features of various scales. Next, Multilevel decoding named as Multi Scale and Multi Decoder (MSMD) is used at fine the scales much excessively. At last, loss is function is used for minimizing loss of MSMD. The performance of proposed method is analysed with performance measures of Intersection over Union (IoU) and Accuracy. The proposed method attained IoU of 86.23% and 87.45% in forest and background Attained mIoU of 85.21% and 88.92% in forest and background respectively. The proposed method attained accuracy of 92.89% and 92.33% in forest and background. Attained mAcc of 90.54% and 91.48% in forest and background respectively. The proposed method performed effective than previous methods like Multiscale Channelwise Cross Attention Network (MCCANet), UNet and Generative Adversarial Networks (GAN).

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