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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Vigneshwaran N A. Vijayaraj V.P. Murugan Jebakumar Jebakumar N. Mageshkumar R. Megavannan Department of IT R.M.K. Engineering College Chennai Department of Mathematics Panimalar Engineering College Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Computer Science and Technology Madanapalle Institute of Technology & Science Andhra Pradesh Faculty of Management SRM Institute of Science and Technology

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

In the modern world, technological developments and inventions have given us the ability to handle practically each aspect of our lives, including keeping track of money, education, job appearances, and security. But this reliance on technology has also made it easier for con artists to defraud people and gain quick money. Fake work notices are a specific approach that has recently become more common. Unsuspecting people submit applications for these fake job openings, handing over their personal information and paying the application fees to the con artists as though they were going to fall for the fraud and waste their hard-earned money. In this paper, the Natural Language Processing (NLP) approach is used to identify bogus job postings. We collected relevant information from job advertisements, after that we applied the random forest classifier to train and evaluate our model. The results we obtained show that our method can recognize fake job advertisements with a 97% accuracy rate.

关键词： Technological innovation Accuracy Jobs listings Employment Feature extraction Natural language processing Fraud Classification algorithms Security Random forests

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Assessing forest cover changes and fragmentation in the Himalayan temperate region: implications for forest conservation and management

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Journal of Forestry Research 2024年第4期35卷 128-141页

作者： Kaleem Mehmood Shoaib Ahmad Anees Akhtar Rehman Aqil Tariq Qijing Liu Sultan Muhammad Fazli Rabbi Shao’an Pan Wesam Atef Hatamleh College of Forestry Beijing Forestry UniversityBeijing 100083People’s Republic of China Institute of Agriculture Sciences and Forestry University of SwatSwatPakistan Department of Forestry The University of AgricultureDera Ismail Khan 29050Pakistan Key Laboratory for Silviculture and Conservation of Ministry of Education Beijing Forestry UniversityBeijing 100083People’s Republic of China Beijing Key Laboratory of Precision Forestry Forestry CollegeBeijing Forestry UniversityBeijing 100083People’s Republic of China School of Environmental Studies China University of GeosciencesWuhan 430074People’s Republic of China Department of Wildlife Fisheries and AquacultureCollege of Forest ResourcesMississippi State UniversityStarkvilleMS 39762‑9690USA School of Management Tianjin University of CommerceTianjin 300134People’s Republic of China Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 5117811543 RiyadhSaudi Arabia

This study comprehensively assessed long-term vegetation changes and forest fragmentation dynamics in the Himalayan temperate region of Pakistan from 1989 to *** satellite images,including Landsat-5 TM and Landsat-8 Operational Land Imager(OLI),were chosen for subsequent assessments in October 1989,2001,2011 and *** classified maps of 1989,2001,2011 and 2019 were created using the maximum likelihood ***-classification comparison showed an overall accuracy of 82.5%and a Kappa coefficient of 0.79 for the 2019 *** revealed a drastic decrease in closed-canopy and open-canopy forests by 117.4 and 271.6 km^(2),respectively,and an increase in agriculture/farm cultivation by 1512.8 km^(2).The two-way ANOVA test showed statistically significant differences in the area of various cover *** fragmentation was evaluated using the Landscape Fragmentation Tool(LFT v2.0)between 1989 and *** large forest core(>2.00 km^(2))decreased from 149.4 to 296.7 km^(2),and a similar pattern was observed in medium forest core(1.00-2.00 km^(2))*** the contrary,the small core(<1.00 km^(2))forest increased from 124.8 to 145.3 km^(2) in *** perforation area increased by 296.9 km^(2),and the edge effect decreased from 458.9 to 431.7 km^(2).The frequency of patches also increased by 119.1 km^(2).The closed and open canopy classes showed a decreasing trend with an annual rate of 0.58%and 1.35%,*** broad implications of these findings can be seen in the studied region as well as other global ecological *** serve as an imperative baseline for afforestation and reforestation operations,highlighting the urgent need for efficient management,conservation,and restoration *** on these findings,sustainable land-use policies may be put into place that support local livelihoods,protect ecosystem services,and conserve biodiversity.

关键词： Natural catastrophes Landsat Change detection Forest fragmentation Landscape fragmentation tool(LFT) Afforestation Reforestation

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Revolutionizing Urban Drainage: A Smart IoT Approach to Stormwater management Using AdaBoosting Algorithm

Revolutionizing Urban Drainage: A Smart IoT Approach to Stor...

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2024 International Conference on Advances in Modern Age technologies for Health and Engineering science, AMATHE 2024

作者： Thanigaivelu, P.S. Sairam, A. Vijayan, P. Kumar, A. Suresh Mohankumar, N. Ganeshbabu, T.R. Srm Institute Of Science And Technology Department Of Computing Technologies Kattankulathur Tamil Nadu Chennai India Saveetha Institute Of Medical And Technical Sciences Department Of Computer Science And Engineering Saveetha School Of Engineering Tamil Nadu Chennai India Saveetha Engineering College Department Of Artificial Intelligence And Data Science Tamil Nadu Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department Of Management Studies Tamil Nadu Chennai India Maharashtra Nagpur India Department Of Electronics And Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350371567

The management of stormwater in urban areas across the globe is becoming an increasingly difficult problem because of the increased risk of floods caused by climate change. Typical drainage methods often turn out to be insufficient, which calls for the development of novel approaches. This study suggests a smart stormwater management system powered by the Internet of Things and aims to boost urban resilience and reduce floods. The system continually monitors critical drainage characteristics, such as water levels, flow rates, and water quality, thanks to a network of Internet of Things sensors. To accurately predict probable flooding occurrences, advanced predictive algorithms examine this data together with incoming meteorological information. The technology performs real-time dynamic management of the drainage network, improving the efficiency of pumps and valves to reduce the likelihood of flooding. When potential flood hazards are identified, those who need to be informed, such as local authorities and citizens, are notified through an early warning system. The findings of this study lead to a more effective allocation of resources, an improvement in water quality, and an increase in the resilience of metropolitan areas to severe weather events. This approach, powered by the Internet of Things, promises a route toward sustainable and flood-resilient communities by transforming urban drainage. © 2024 IEEE.

关键词： Internet of things

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Deep Convolutional Neural Network for Two-Dimensional Medical Image Segmentation Using a Learned Curriculum 2

Deep Convolutional Neural Network for Two-Dimensional Medica...

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

作者： Joyce Jacob, J. Vats, Satvik Sunitha, B.K. Wankhede, D.S. Balaji, N.V. Sharma, Sorabh Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India School of Management - UG 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

Radiologists greatly benefit from medical image segmentation algorithms when determining the severity of diseases, conducting clinical evaluations, and choosing the best course of therapy. Deep learning is quickly becoming the go-to method for medical picture segmentation because to both the proliferation of big data and improvements in medical imaging methods. The medical segmentation structure encounters various obstacles, such as a lack of labelled data, complicated picture representation, low contrast variation, high structural similarity among infection as well as background, and irregularities related to texture and morphology within homogeneous infected areas. For the purpose of training convolutional neural networks to segment deformable paired 3D medical images, we investigate several curriculum learning approaches. We believe our approach to training medical image segmentation algorithms via curriculum learning, which involves beginning with a simple setup and progressively increasing its complexity, is the first of its kind to aim at improving performance. First, we take a look at two methods for learning curricula that are already out there: curriculum dropout along with curriculum through smoothing. But we provide a fresh and easy way to do curriculum: start with intentionally blurry photographs and work your way up to crisper ones throughout training. Curriculum learning can outperform traditional training methods, according to our studies using a state-of-the-art deep learning model. Among the evaluated curriculum learning methodologies, we also demonstrate that curriculum through input blur offers the highest accuracy against time trade-off. © 2024 IEEE.

关键词： Medical imaging

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AI-based Analytics for Human Resource Data Insights 3

AI-based Analytics for Human Resource Data Insights

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahadevan, Padma Sridharan, A. Sakpal, Shreya Sudesh Gujar, Shantanu Sudhir Labhane, Suchitra Kharche, Akash Isbr Business School Department of Management Studies Karnataka Bengaluru560100 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Law Department of Physical Education & Yoga Tamil Nadu Chennai600077 India Golisano College of Computing and Information Sciences Rochester Institute of Technology Department of Computer Science RochesterNY14623 United States Golisano College of Computing and Information Sciences Rochester Institute of Technology Department of Cyber Security RochesterNY14623 United States Datta Meghe Institute of Management Studies Department of Mba Maharashtra Nagpur440020 India

ISBN: (纸本)9798331509675

A rapid digital transformation in enterprises has resulted in an explosion of data pertaining to human resources (HR), which has necessitated the use of advanced analytics in order to discover insights that can be put into action. Using contemporary machine learning techniques, this research investigates the use of artificial intelligence (AI) for HR data analytics. The goal of the study is to solve issues in data quality, feature selection, and predictive modelling. A number of resampling strategies are utilized during preprocessing in order to correct class imbalances, and data transformation is utilized in order to extract useful features. Through the utilization of Principal Component Analysis (PCA) for dimensionality reduction, the process of feature selection is streamlined in order to determine which predictors have the most impact. In order to anticipate important HR outcomes, such as employee attrition and performance categorization, feedforward neural networks (FNNs) are utilized for classification purposes. Datasets from the real world of human resources are used to validate the suggested framework, which demonstrates enhanced accuracy and efficiency in the process of gaining insights. The findings of this study emphasize the potential for AI-driven approaches to change HR analytics. © 2025 IEEE.

关键词： Dimensionality reduction

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MRI-3D Image Fusion Using an Automatic Cone-Centred Breast Ultrasound Scanner

MRI-3D Image Fusion Using an Automatic Cone-Centred Breast U...

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2023 IEEE International Conference on Paradigm Shift in Information technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Mustare, Narendra B Sivaji, U. Das, Debi Prasad Sivasangari, A. Ammisetty, Veeraswamy Harshitha, Doppalapudi Cvr College of Engineering Telangana State Hyderabad India Institute of Aeronautical Engineering Information Technology Hyderabad India Kiit School of Management Kiit University Bhubaneswar India Sathyabama Institute of Science and Technology Department: It India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram India Kkr & Ksr Institute of Technology and Sciences Electronics and Communication Engineering India

ISBN: (纸本)9798350329773

The limits of conventional approaches are criticized and the importance of prompt detection of breast cancer with sophisticated imaging techniques is emphasized in this study report. The research supports the revolutionary potential of MRI-3D image fusion, which goes beyond the limitations of traditional modalities by fusing 3D MRI with computerized cone-centered breast ultrasonography. It explores multimodal image fusion techniques such as spatial registration and feature-based synthesis and shows how they might improve understanding of breast lesions. It is recognized that incorporating intelligent technology (AI) into combined image operations may automate procedures, increase accuracy, and decrease human error. Even with these developments, problems like false positives continue to arise, which is why neural network integration is being proposed. The study also looks at newer imaging modalities such PET and tomosynthesis, highlighting how they work together to provide a complete picture of breast tumors. The use of these innovative technologies must take into account ethical factors such as patient permission, privacy, and equitable access. The study ends by recommending ongoing investigation, commitment to ethical principles, and patient-centered treatment in order to mold a bright future for diagnostics related to breast health. © 2023 IEEE.

关键词： Image fusion

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Cognitive Large Language Model in Social Media with Local Memory

Cognitive Large Language Model in Social Media with Local Me...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Vigneshwaran N A. Vijayaraj V. P. Murugan R. Jebakumar N. Mageshkumar R. Megavannan Department of IT R.M.K. Engineering College Chennai Department of Mathematics Panimalar Engineering College Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Computer Science and Technology Madanapalle Institute of Technology & Science Andhra Pradesh Faculty of Management SRM Institute of Science and Technology

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Enter the era of social media reimagined! This research taps into an interesting combination of advanced Artificial Intelligence, with large language models such as ChatGPT and GEMINI your favorite social platform. Personal information of each user is armed with voice commands and LLM. But this is the first. LLM talk to each other, exchange information. face recognition adds an extra layer in the mass layer the excitement of your Chatbot sharing only the right information Your account security level private, reserved or public. When it comes to security. We got you covered Choose between playing low key or just sharing just the right number of juicy details. Plus, all the classic features It's still fun, like scrolls and scrolls.

关键词： Social networking (online) Large language models Face recognition Oral communication Chatbots Security

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Investigating Cybersecurity Dynamics in SDN Controller Buildings: The Danger Environment and Consequences

Investigating Cybersecurity Dynamics in SDN Controller Build...

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Communications and Information technologies (GCCIT), Global Conference on

作者： Satvik Vats C. Vennila Lakshman K S.A. Tiwaskar K. Ranjith Singh Tarun Kapoor Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Management - UG Management JAIN (Deemed to be University) Bangalore Karnataka Computer Engg Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

A novel way of constructing networks is the software-defined network, or SDN. New network security dangers exist, yet it can better enable dynamic traffic management than the conventional network. It is crucial to use several controllers to promptly identify suspicious SDN devices in order to enhance SDN security and alleviate the burden on a single controller, utilizing analytical modeling of cyber assaults utilizing the concept of topological change in stochastic networks, the paper presents a way to assuring the functionality of Software-Defined Networks (SDN) in cyber attack situations. In contrast to previous popular methods, the suggested technique integrates analytical modeling-based SDN resilience evaluation with neural network-based SDN status monitoring. Analytical expressions may be used to determine the robustness indicators of SDN, which are made feasible by considering the mathematical basis of this evaluation. The suggested major metric for SDN resilience is the right operation coefficient. This study presents an enhanced anomaly detection approach for SDN multi-controller based on the XGBoost mechanism. The system is fault-tolerant, meaning it can withstand aberrant or erroneous instructions and guarantees the right issuance of the flow table. One relatively new machine learning approach that has been gaining a lot of traction is XGBoost. IIt is essential for SDN controllers to have high accuracy in attack detection, low false positive rates, and fast processing speeds. As a detection approach in SDN-based clouds, we use extreme gradient boosting (XGBoost) in this study. Furthermore, we use the POX as the SDN controller, construct the SDN topology using Mininet, and mimic a real-life DDoS assault scenario with the help of the attack tool Hyenae. The XGBoost classifier compares itself to other classifiers using the flow packet data set obtained from TcpDump for DDoS detection. The detection findings show that our technique is faster, more accurate, mo

关键词： Cloud computing Analytical models Accuracy Stochastic processes Process control Robustness Topology Object recognition Software defined networking Resilience

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NM-LUD: A NLOS Mitigation Method with Low-cost UWB Devices for Indoor Positioning 8

NM-LUD: A NLOS Mitigation Method with Low-cost UWB Devices f...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Wu, Tong Li, Han Gao, Bo Xiong, Ke Fan, Pingyi Ben Letaief, Khaled Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China Beijing Jiaotong University National Engineering Research Center of Advanced Network Technologies Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China Hong Kong University of Science and Technology Department of Electrical and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665456128

Ultra-wideband (UWB) is a popular technology for indoor positioning. However, non-line-of-sight (NLOS) propagation caused by human occlusion dramatically affects the accuracy of UWB-based indoor positioning. Most of the existing works focused on mitigating NLOS errors in the scenarios with building structure occlusion, but the NLOS mitigation under human occlusion has not been well addressed. To this end, this paper aims to mitigate the NLOS impact of human occlusion, and then proposes a method of NLOS mitigation to reduce NLOS ranging errors under human occlusion with low-cost UWB devices (i.e., NM-LUD). It takes a set of signal features extracted from low-cost devices as input, calculates their weights and mitigates the ranging error based on the features' weights. Experiment results on the two data sets collected by LinkTrack S and UML1 UWB devices show that the method reduces ranging error from 0.9009 m to 0.1435 m (about by 84.07%) and from 0.7466 m to 0.0792 m (about by 89.39%) respectively, which greatly mitigates the influence of NLOS caused by human occlusion. © 2023 IEEE.

关键词： Costs

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Image steganography in the IoMT and telemedicine: Residual and Enhanced Discriminative Network 2

Image steganography in the IoMT and telemedicine: Residual a...

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

作者： Balaji, N.V. Sharma, Sorabh Sahoo, Ashok Kumar Murali, M.J. Umakanth, S. Patil, Swati B. Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India School of Management - UG Management Karnataka Bangalore India Vishwakarma Institute of Technology Department of Information Technology Pune India

ISBN: (纸本)9798350388916

Through the use of technology such as computers, phones, and medical equipment, telemedicine and the Internet of Medical Things (IoMT) enable off-site interactions between medical professionals, patients, and experts. Patient privacy or consultation outcomes might be jeopardized, leading to potentially catastrophic medical events, if the information sent in IoMT is unlawfully steganographic, altered, or leaked throughout transmission and storage. There is an immediate need to safeguard the privacy and value that is the picture data included in electronic health records (EHRs). A possible option may be image steganography. Consequently, a StegaStamp-based steganographic model for pictures from medical records is created. Medical record photos are document images, not like natural photographs, and hence they are susceptible to image cropping assaults. When it comes to detecting illicitly transmitted medical pictures in IoMT and telemedicine, steganalysis plays a crucial role. To address the challenges of picture steganalysis in IoMT and telemedicine, we provide RGAN-Net, a Residual Generative Adversarial Discriminative Network. A deep residual network, a steganographic information discriminative mechanism, and an augmentation module for steganographic information make up RGAN-Net. In order to improve the illicit steganographic signal in highly detailed medical images with complex textural qualities, the RGAN-Net utilizes a steganographic information improvement module. In order to extract and compress steganographic features, a deep residual network is used. The deep residual network uses a steganographic data discriminative technique to correct the steganographic characteristics and remove high-frequency characteristics that are erroneously classified as steganographic information. The range of RGAN-Net steganalysis error PE was found to be 0.0732-0.0010 and 0.231-0.026, respectively, in experiments carried out on public and private datasets with data concealing payl

关键词： Telemedicine

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