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Investigations of electricity expansion environmental planning using LEAP model for sustainability

International Journal of Energy technology and Policy 2021年第4期17卷 384-397页

作者： Karunanithi, K. Ramesh, S. Raja, S.P. Rayaguru, N.K. Saravanan, S. Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India Department of Electrical and Electronics Engineering B V Raju Institute of Technology Telangana Narsapur India

Electricity expansion planning is finding an optimal solution for the power system planning problem in which installation of new generation units satisfies both technical and financial constraints. Nowadays, renewable power generation plays an important role in power generation since it is pollution free and has zero fuel cost. In this paper, an attempt is made to design a future power system for Andhra Pradesh, an Indian state in which the electricity is generated mainly from non-renewable energy sources which increase greenhouse gases (GHGs) emission and hence global warming. Keeping in mind about pollution impacts, renewable energy sources like wind, solar, biomass are considered for Andhra Pradesh using long-range energy alternative planning system (LEAP) software. The various scenarios have been developed with LEAP and in all scenarios, the system installed capacity, 100-year global warming potential, social cost, production cost and total cost are investigated quantitatively. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Renewable energy resources

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“Challenges and Solution for Security & Living in High Altitude Area Siachen”

“Challenges and Solution for Security & Living in High Alti...

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Sustainable Emerging Innovations in engineering and technology (ICSEIET), International Conference on

作者： Anirudh Pratap Singh Anushree Rajput Anil Kumar Dhyanendra Jain Pranshi Verma B.S (Data Science & Application) IIT Madras Chennai B.Tech (CSE-AIML) ABES EC Ghaziabad Department of Mathematics & Statistics Gurukula Kangari University Haridwar India Department of Mathematics & Statistics CGC-MJPRU MJP Rohilkhand University Bareilly India Department of CSE-AIML ABES ENGINEERING COLLEGE Ghaziabad India

This abstract discusses the challenges faced by soldiers at high-altitude border fronts and proposes a solution to address these issues. The soldiers endure extreme weather conditions, including strong winds of 300 KM/Hour and bone-chilling temperatures of -60 degrees Celsius, along with blizzards and avalanches. Food scarcity is common, necessitating snow boiling for drinking water, and limited time for eating due to freezing conditions. Communication with loved ones is challenging due to the lack of mobile networks in such remote locations. To tackle these problems, the author introduces a Multipurpose cable system that provides electrical power, telecommunication lines for base camp connectivity, and optical fiber for data sharing. The cable is designed to withstand extreme weather conditions and is spread within the snow using specific alloys to ensure stability at temperatures ranging from -75°C to -100°C. The cable includes self-security features to prevent tampering and provides GPS navigation capabilities. The proposed solution enables the supply of frozen food to soldiers, which can be cooked on electric heaters, making the process environmentally friendly. Additionally, an Indestructible Secure Communication Network is suggested, utilizing optical and telecommunication cables covered with metal repair paste for added security. This network establishes an unhackable serverless system at critical locations, facilitating secure communication for defense forces, research labs, and ministries. The proposed system utilizes two specially designed cables for different terrains and risk levels, effectively connecting high-altitude posts to bases without disturbing the environment. by implementing this solution, the aim is to improve the living conditions and security of armed forces stationed at challenging high-altitude border fronts.

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An Ensemble Model to Predict Network Traffic Prediction Model Using Rb Multicast Protocol

An Ensemble Model to Predict Network Traffic Prediction Mode...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： S Deepa P Sathishkumar Vivek Duraivelu R Suguna M S Arunkumar M b Myleshan Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai -62 Tamilnadu Kongu Engineering College Perundurai Erode Tamilnadu

A Vehicular Ad Hoc Network (VANET) is an effective method for addressing group-oriented services. It is possible to support such services through multicast routing. As a result, packet delivery ratios must be improved, delays must be reduced, and overheads must be reduced. Consequently, multicast routing protocols for VANETs must be stable and reliable. A Receiver based Multicast protocol is proposed in our paper that determines how multicast traffic should be forwarded. In the packet header, Rb Multicast stores a list of destinations. Multicast members to whom this packet is addressed are listed in this destination list. Consequently, there is no need to store tree state at intermediate nodes and no multicast tree is required. According to this study, the objective of the study was to predict network traffic by using the V2R dataset, and to use the receiving packets sent by vehicles to the RSUs as a measure of network activity. Our analysis of network traffic flow resulted in the prediction of network flow. Using ensemble classification metrics, we evaluated the data. Simulated results indicate that the proposed model has a superior performance in terms of execution time and prediction accuracy compared to other algorithms.

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Face Spoof Detection Using Gray Level Co-Occurrence Matrix and Discrete Wavelet Transform Feature Extractor

Face Spoof Detection Using Gray Level Co-Occurrence Matrix a...

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International Conference on Computing sciences (ICCS)

作者： Amal Mathew Kaushik Daiv Polkumpally Rohan Goud Piyush Talreja Sai Sanjana Reddy Vatte B.tech Computer Science Ms Ramaiah University of Applied Sciences Bangalore Karnataka India B.E. Computer Science MIT College of Engineering Pune Maharashtra India B.E Computer Science Chaitanya Bharathi Institute of Technology Hyderabad India B.E Computer Science Maharashtra Institute of Technology Pune Maharashtra India

ISBN: (纸本)9781665494465

Face identification using ML (machine learning) is well-known. Attendance structures may benefit from this method. Using this method, you may achieve the desired area, as well as beneficial attributes and a dataset, by preparing two sets of data again for test and training phases. To distinguish between a testing set and a test sets, a photograph is used as a testing set. An ensemble classification method is used to sort the test images into categories like “identified” and “unidentified.” This model can't provide reliable findings since it simply divides data into two categories. The development of GLCM was motivated by the need to use texture properties to identify faces. The existence of the query picture is noted once face detection has taken place. In simulation findings, the new model outperforms the baseline models in terms of accuracy. Keywords—Ensemble classifier, GLCM, Face Spoof, SVM, DWT

关键词： Training Image segmentation Face recognition Training data Machine learning Transforms Feature extraction

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Image Forgery Localization and Detection using Multiple Deep Learning Algorithm with ELA

Image Forgery Localization and Detection using Multiple Deep...

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Fourth Industrial Revolution based technology and Practices (ICFIRTP), International Conference on

作者： Anshul Kumar Singh Chandani Sharma brajesh Kumar Singh Department of Computer Science & Engineering Quantum University Rorkee India Department of Computer Science & Engineering R. B. S. Engg. Tech. Campus Agra India

In today’s technical world, social media may help an individual grow significantly. On the other side, we must not overlook the reality that it is also a large platform for criticism. With recent advancements, approaches for creating and manipulating multimedia information may now deliver a highly sophisticated level of realism. There has been a blurring of the line between real media and fake media in recent years. Creative arts, film production, advertising, and video gaming are among the industries that could benefit from this technology. However, it poses significant security risks. Software tools freely accessible on the internet enable anybody, with no particular abilities, to generate extremely convincing phony images and films. A dataset composed of real photographs and false images is used in this article to identify images of modifications using Deep Learning algorithms with Error Level Analysis on each image. Our experiment yielded the accuracies of 93.5%, 89.1 and 92.4% in ResNet50, Vgg16 and CNN respectively for 50 epochs.

关键词： Deep learning Training Social networking (online) Software algorithms computer architecture Media Streaming media

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An Innovative Method for Mobile Network Payment Security System based on hybrid LSTM-Ensemble Learning-based Model

An Innovative Method for Mobile Network Payment Security Sys...

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Advancement in technology (ICONAT), International Conference for

作者： N Sujatha S Pramod S Aravindh balagopala Ramesh babu S. Rukmani Devi M. Ananthi Department of Mathematics B. M. S. College of Engineering Bengaluru Karnataka India Department of Mathematics Nitte Meenakshi Institute of Technology Yelahanka Bangalore Karnataka India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering SRKR Engineering College Bhimavaram Andhra Pradesh India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamilnadu India Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Tamilnadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

It is common practice in many industries to use cash since it accounts for more than 85% of payments in almost all developing countries. A cell phone is become an everyday item. Mobile phones have practically become people’s constant companions due to the plethora of other uses they offer beyond just communicating. Since they are both cheap and multipurpose, soon everyone will be utilizing them. In an ideal world, everyone would use their cell phone for everything. Verification, preprocessing, and training the model are the three parts that make up the method. In order to confirm the payment, the customer’s biometric template is checked during the verification procedure. The three stages of fingerprint preparation include matching fingerprint templates, extracting fingerprint minutiae, and fingerprint preprocessing. The training of the model was carried out using a Hybrid LSTM-Ensemble Learning. The average accuracy of the suggested method is 92.76%, which is higher than LSTM and Ensemble Learning.

关键词： Training Accuracy Cellular phones biological system modeling Fingerprint recognition Vectors Mobile handsets Security Ensemble learning Long short term memory

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An IoT and Random Forest-DT-based Early Enhanced Forest Fire Detection in Large and Small Targets

An IoT and Random Forest-DT-Based Early Enhanced Forest Fire...

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Innovation in technology (INOCON), IEEE International Conference for

作者： Manjula Prabakaran Omesh Wadhwani V. Pandiaraj Kaneez Zainab Neerav Nishant Justin Z Department of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Information Technology Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer Science & Engineering B.N. College of Engineering and Technology Lucknow India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India Department of Science and Humanities (English) RMK College of Engineering and Technology Thiruvalluvar India

The world is confronted with more complex and severe environmental concerns than in the past. Extreme weather events have become more common and destructive in the last several decades, endangering forests and urban forests around the globe. Massive forest fires are among the most catastrophic natural catastrophes that affect Earth’s climate and the diversity of life on it. Thus, in order to minimize harmful impacts on ecosystems and people, it is vital to have well-planned and coordinated plans for early warning, prevention, and reaction. Image preprocessing, feature extraction, and training the model are the initial steps in the suggested methodology. Histogram matching and picture smoothing are part of the image preparation procedure. The gray histogram is a way to see how many pixels fall into each grayscale level, and image smoothing is a way to enhance a large region of a picture using image processing. The GLCM and LbP histograms are employed in the feature extraction process. Training RF-Multivariate ARS models requires feature extraction as a prerequisite. The system improved accuracy to 97.29 percent after applying the approach.

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Jellyfish Search Optimizer with Deep Learning Driven Cervical Cancer Recognition and Classification Model on biomedical Images

Jellyfish Search Optimizer with Deep Learning Driven Cervica...

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International Conference on Advances in Information technology (ICAIT)

作者： M. Suresh Anand S. Venkatramulu Sesha Vidhya S W T Chembian Yashaswini b M Magesh Kumar b Department of Computing Technologies School of Computing SRM Institute of Science & Technology Kattankulathur Department of CSE kakatiya Institute of Technology and science Warangal Department of ECE RMK College of Engineering and Technology Department of CSE Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Morai Avadi Electronics & Communication Engineering JSS Academy of Technical Education JSSATE-B Campus Bengaluru Karnataka Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The emergence of Cervical Cancer (CC) Recognition and Classification Model for biomedical Images is a main advancement in fields of medical imaging and diagnostics. Leveraging state-of-the-art image processing and machine learning algorithms, this model can accurately detect and classify CC-related abnormalities in medical images, namely colposcopy or Pap smear images. by providing automated and precise assessments, the model empowers medical experts to make timely and informed decisions, which leads to earlier interference and potential life-saving treatment. With its potential to improve diagnostic accuracy and reduce human error, the model holds great promise in the fight against CC, especially in resource-constrained healthcare settings where medical practitioners may be limited. This study presents a Jellyfish Search Optimizer with Deep Learning Driven CC Recognition and Classification (JSODL-CCRC) model on biomedical Images. The JSODL-CCRC technique is mainly developed to investigate the medical images for detection and classification of CC. The JSODL-CCRC approach comprises median filtering (MF) for noise reduction, followed by feature extraction utilizing Inceptionv3 to derive complex image patterns. besides, the JSO based hyperparameter selection process is performed for the Inception v3 model. At last, Extreme Learning Machine (ELM) algorithm is applied to categorize the images which validates efficacy in handling complex biomedical image data. The experimental results stated that the JSODL-CCRC model enables effective detection results over other models.

关键词： Deep learning Image recognition Jellyfish biological system modeling Noise reduction Medical services Feature extraction Medical diagnostic imaging Cervical cancer Tuning

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Data-driven visual analytics of Human Mobility data and green cover using Image Processing for Smart Cities

Data-driven visual analytics of Human Mobility data and gree...

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Intelligent technologies (CONIT), International Conference on

作者： Sivasubramanian Ramanathan Kulsoom Syed Tejaswini Chavan SVKMs NMIMS Navi Mumbai B. Tech Computer Science & Business Systems Mumbai India SVKMs NMIMS Navi Mumbai School of Technology Management & Engineering Mumbai India

This research paper presents an approach to data-driven visual analytics of human mobility data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods and intuitive visualizations using the ARIMA model. A specific geographic area is chosen for the demonstration of the scope of the proposed system. The system is a web application developed using Streamlit, an open-source python framework. To effectively implement the smart city concept, it is crucial to integrate diverse IoT systems, data sources, data streams, and analytical tools into a unified and seamless platform that facilitates the collection, analysis and presentation of information related to urban systems and subsystems. We propose taking into consideration several attributes when analyzing human mobility patterns, such as vehicle/traffic density, modes of transport, transport data, demographics, latitude, and longitude. Additionally, the system utilizes image processing as an efficient method for calculating urban green cover using morphological operations that are computationally cheaper and easy to use as compared to traditional surveys that are time and resource intensive. This information is used to develop smart plans to sustain or increase green cover in select areas, leading to the creation of sustainable and green smart cities.

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Empowering Safety: Designing of an IoT based Women Protective System using Artificial Intelligence enabled Smart Sensors

Empowering Safety: Designing of an IoT based Women Protectiv...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： R. Sonia T Rubesh Kumar G. Naresh S. Durga Devi N Juliet Department of Computer Applications B S Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai Department of Biomedical Engineering Rajalakshmi Engineering College Chennai Department of ECE Mohan Babu University (Erstwhile SreeVidyanikethan Engineering College) Tirupati Andhra Pradesh Department of CSE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Department of Master of Computer Applications S.A. Engineering College Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Worldwide, women endure a great deal of unethical forms of domestic violence. The lack of an efficient tracking mechanism is speeding this up. An AI-based system that provides protection to vulnerable women was the subject of this research. In critical situations, this framework can respond appropriately automatically and manually. No place is secured for women, but they are more at risk when venturing out on their own into remote areas and unfamiliar roads. After recognizing danger, current women’s handheld safety gadgets require human involvement to activate (e.g., pushing a button or shaking the device). We provide an approach that aims to provide women with false proof safety while also attempting to address the shortcomings of current systems. This system showcases an Arduino-powered wearable safety gadget designed specifically for women. This gadget is designed to protect ladies from harm in case they encounter any risk. The gadget is able to converse and deliver alerts using a wireless sensor network. The user’s precise whereabouts can be sent to designated authorities and stored contacts through the use of GPS and GSM. In the event of an emergency, the device’s switch can be used as a panic button to get a shock; when pressed, the buzzer and laser diode will both activate. This research presents a novel approach to women’s safety, the IoT Powered Women Protection (IoTPWP), and evaluates its efficacy by cross-validating it with the usual model, the bluetooth enabled Women Safety System (bWSS). We also believe that shows with built-in cameras that record electric shock and broadcasting real-time footage can have an effect on women’s lives through the medium of digital technology.

关键词： Wireless sensor networks technological innovation Electric shock Pressing Switches Watches Real-time systems

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