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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Cholla, Ravindra Raman Rao, K Nagendra Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

The internet of things technology has developed almost all the sectors including energy management. In traditional energy management system meters are used to recording the number of units and electricity used but they are unable to provide the real time information or updates or any theft of electricity. By focusing this problem, we have developed a system which is able to overcome all the problems in traditional electric management system. It monitors the energy usage and real time theft detection of electricity as it cost lot of money. In our system we have used a current sensor which continuously checks the electricity flow. The microcontroller measures the actual energy usage as well as expected values. It compares both the values. If a big difference found, then it could mean the theft of electricity. So, the GSM module is used to send the alert to the user hence they can take required action. The Wi-Fi module sends the data over the cloud for data analytics. Also, user can see the real time data from the cloud platform. Hence user can track the energy usage and get timely alerts so that user can be aware of energy consumption or energy theft. The system is developed by setting up hardware parts as well as integration with software. The system can be operated or monitored from remote place, as Wi-Fi module connects the system with the internet. System can detect the unusual activities like theft of electricity by analyzing the data. Using the IoT technology for energy management, it can become cost effective as well as smart solution of energy management to avoid any theft. Hence it can overcome the loss. © 2024 IEEE.

关键词： Energy utilization

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Image Captioning System for Movie Subtitling Using Neural Networks and LSTM 3rd

Image Captioning System for Movie Subtitling Using Neural Ne...

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3rd International Conference on Computing and communication Networks, ICCCN 2023

作者： Vijay, K. Manohar, Eashaan Saiganesh, B. Sanjai, S. Deepak, S.R. Department of Computer Science and Engineering Rajalakshmi Engineering College Tamilnadu Chennai India Department of Electronics and Communication Engineering Rajalakshmi Engineering College Tamilnadu Chennai India

ISBN: (纸本)9789819708918

With the advent of the Internet, the multimedia business has experienced explosive growth and is now available to consumers worldwide. Particularly important to this growth has been the dominance of over-the-top (OTT) platforms during the COVID-19 epidemic. One important component in this growth has been the implementation of state-of-the-art Machine Learning (ML) methods. These algorithms can generate captions from video frames mechanically, increasing the platform's accessibility. However, it can be difficult to meet the needs of users speaking different languages, as there are many films being streamed in languages that may be inaccessible to consumers in other parts of the world. There is an issue with English being the most spoken language in the world. Therefore, individuals who aren't fluent in English may have trouble finding videos in their native language. By automatically creating English subtitles for any movie, regardless of the language spoken on the original audio track, machine learning plays a significant role in removing this barrier. Neural Networks, used for processing each frame of the video, and LSTM models, used for caption synthesis, are the key models employed here from the realm of machine learning. After the models have been trained, they can be incorporated into the user interface (UI) using a programming language like Python. In the user interface, the created caption can be shown alongside the uploaded image. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： User interfaces

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Topology, Size, and Shape Optimization in Civil engineering Structures: A Review

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computer Modeling in engineering & sciences 2025年第2期142卷 933-971页

作者： Ahmed Manguri Hogr Hassan Najmadeen Saeed Robert Jankowski Faculty of Civil and Environmental Engineering Gdansk University of TechnologyGdansk80-223Poland Department of Computer Science ModelingElectronics and Systems EngineeringUniversity of CalabriaCosenza87036Italy Civil Engineering Department University of RaparinRania46012Iraq Civil Engineering Department Tishk International UniversityErbil44001Iraq

The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering *** optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural *** design approaches aim to reduce the built environment’s energy use and carbon *** comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these *** trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were *** review also discusses emerging technologies,such as machine learning applications with different optimization *** of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and *** techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure *** and non-linear structures,including geometric and material nonlinearity,are *** role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.

关键词： Structural optimization topology optimization size optimization shape optimization multi-objective optimization

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New improved model for joint segmentation and registration of multi-modality images: application to medical images

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8755-8770页

作者： Badshah, Noor Begum, Nasra Rada, Lavdie Ashfaq, Muniba Atta, Hadia Department of Basic Sciences and Islamiat University of Engineering and Technology Peshawar Pakistan Faculty of Engineering and Natural Science Bahcesehir University Istanbul Turkey Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan Department of Mathematics Islamia College Peshawar Pakistan

Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively. © 2024 - IOS Press. All rights reserved.

关键词： Image segmentation

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Energy-Efficient Underwater communication Using Bio-Inspired Algorithms in Metaheuristic Optimization 8

Energy-Efficient Underwater Communication Using Bio-Inspired...

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8th International Conference on Electronics, communication and Aerospace Technology, ICECA 2024

作者： Kannappan, A. Kolagani, Nagesh Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The condition of the ocean is monitored through a network of acoustic sensor nodes. After the transmitter and receiver nodes are formed, they are placed in a shallow sea environment. Underwater communication takes more time, so optimization of it is a top priority. Cat swarm optimization method and sea lion optimization algorithms are two examples of currently used bio-inspired algorithms. By analyzing the output parameters of both algorithms, we can see which one is more optimized. When comparing cat swarm optimization techniques to, the sea lion algorithm provides superior energy efficiency by lowering the amount of power required to process acoustic sensor node data. Existing cat swarm optimization Algorithms compared with sea lion optimization algorithm. The amount of energy difference that occurs in the two algorithms is nearly up to 18 % more than energy efficiency. Sea lions and cat swarm based upon condition need the individual or group performance, these recreate the bio inspire algorithm nodes. © 2024 IEEE.

关键词： Underwater acoustic communication

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Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

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Intelligent Automation & Soft Computing 2023年第1期35卷 839-851页

作者： B.Murugeshwari D.Selvaraj K.Sudharson S.Radhika Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Information Technology Velammal Insitutue of TechnologyChennai601204India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600124India

Protecting the privacy of data in the multi-cloud is a crucial *** mining is a technique that protects the privacy of individual data while mining those *** most significant task entails obtaining data from numerous remote *** algorithms can obtain sensitive information once the data is in the data *** traditional algorithms/techniques promise to provide safe data transfer,storing,and retrieving over the cloud *** strategies are primarily concerned with protecting the privacy of user *** study aims to present data mining with privacy protection(DMPP)using precise elliptic curve cryptography(PECC),which builds upon that algebraic elliptic curve *** approach enables safe data exchange by utilizing a reliable data consolidation approach entirely reliant on rewritable data concealing ***,it outperforms data mining in terms of solid privacy procedures while maintaining the quality of the *** approximation error,computational cost,anonymizing time,and data loss are considered performance *** suggested approach is practical and applicable in real-world situations according to the experimentalfindings.

关键词： Data mining cryptography privacy preserving elliptic curve information security

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Detection of Distributed Denial of Service in The Application Layer of IoT Using Machine Learning 15

Detection of Distributed Denial of Service in The Applicatio...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Nanthiya, D. Gopal, S.B. Manjula Devi, R. Praveenkumar, R. Vijayakumar, S.D. Raja, T. Dharanish Sriram, L. Kongu Engineering college Department of Computer Technology -UG Perundurai India Kongu Engineering College Department of Electronics and Communication Engineering Perundurai India Kpr institute of Engineering and Technology Department of Computer science and Engineering Coimbatore India Builders Engineering College Department of Electronics and Communication Engineering Kangeyam India Vivekanandha college of Technology for women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798350370249

Cybersecurity prevents harmful assaults on computers, networks, and mobile devices. Included in the category of cyber-attacks are threats like as malware, distributed denial of service assaults, phishing, spoofing, code injection, and supply chain attacks. Massive denial-of-service assaults are destructive. As part of a Distributed Denial-of-Service (DDoS) assault, hackers bypass botnets and go straight for the server. In order to temporarily block or suspend a website, these botnets send a flood of requests to the server. The server typically takes requests and returns responses. There will be a brief outage if the server gets more requests than it can handle. While this attack makes the service inaccessible, it prevents data theft. A machine-learning approach is used to detect server-side DDoS assaults. The term 'machine learning' refers to software that can learn new tasks and solve old ones without any input from a human. Learning Vector Quantization, Support Vector Machine, Naive Bayes, K-Nearest Neighbour, Random Forest, and Decision Tree are the six ML classification models used since these ML procedures enhance accuracy. The main goal of this research is to use the CIDAD dataset to find ways to protect the application layer of the Internet of Things (IoT) against Distributed Denial of Service (DDoS) attacks. The lower layers of the IoT network architecture are disregarded by this dataset, which comprises information at the COAP level. © 2024 IEEE.

关键词： CIDAD Cyber security Cyber-attacks DDoS attacks

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Intrusion Detection in the IoT-Fog Adopting the GRU and CNN: A Deep Learning-Based Approach 6th

Intrusion Detection in the IoT-Fog Adopting the GRU and CNN:...

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6th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2022

作者： Al-Khuzaie, Zahraa Majeed Albermany, Salah A. K. AbdlNibe, Mohammed Ahmed Computer Science Department University of Kufa Najaf Iraq Department of Electronics and Communication Engineering Faculty of Engineering University of Kufa Kufa Iraq

ISBN: (纸本)9789811995118

The main objective of this research is to utilize the technologies of cloud computing in the applications of the IoT. The IoT network is divided into three layers: the cloud computing layer, the edge layer, and the fog layer. More than twenty-five million devices are connected to the fog layer leading to an increase in data traffic in the network in general. As a result, there will be an increase in the number of attacks. Therefore, protecting the data of the IoT and detecting attacks are indispensable. Since new types of attacks frequently emerge, intrusion detecting systems (IDSs) have to be developed to counteract the new types of attacks. However, built upon the principles of deep learning and machine learning, most of the current IDSs are based on predicting the probability of an attack. The most commonly used IDSs are the KNN and the SVN. Such IDSs suffer from low accuracy rates in attacks detection, time consumption, and a large number of false alarms. Thus, this paper suggests a new model of deep learning that is built upon combining two algorithms: the convolution neural network (CNN) and the gated recurrent unit network (GRU). Additionally, two types of datasets are utilized: the NSL-KDD and UNSW-NB15. These datasets are categorized in accordance with multiclass classification. The suggested model provides a better detection rate in comparison with the other models. It achieves a detection rate that is 1.5 more accurate than the other IDSs. Also, it exhibits fewer false alarms, and it provides less time for detection rate. The achieved results of the suggested model are due to utilizing the technologies of deep learning and machine learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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Enhancing Railway Safety with an IoT Based System for Real Time Fault Detection and Crack Monitoring 9

Enhancing Railway Safety with an IoT Based System for Real T...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Nalluri, Madhusudana Rao Chukka, Santhaiah Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

Safety of railway is the major problem worldwide. It has problems like cracks or any fault in the railway tracks. These problems can cause severe accidents if it is not detected regularly and early. In traditional fault finding and crack detecting method, the manual method is involved which is very slow and consist of many errors. By considering all these challenges and issues in track detection method, we have implemented an advanced IoT based railway crack and fault-finding system. It uses advanced sensors, microcontroller, GPS and GSM module which can detect and report faults of the railway track. The system uses real time mechanism. The system is implemented and tested on a prototype railway track to detect crack or any fault. The internet of things technology added modern and accurate fault-finding feature in the system. The IoT plays an important role in implementing advance technologies, as it uses sensors and advance microcontroller. The accuracy rate of fault and crack detection is around 97%, which is for 2 mm crack. Along with this GPS module accurately track fault location. GSM module successfully send the alert message quickly after detecting the fault in the track. Hence the response time is very less and accurate. This system is implemented in order to ensure safety for the railway track and prevent the accidents. Hence the system is useful for improving overall transport safety. © 2024 IEEE.

关键词： Microcontrollers

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