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arXiv 2023年

作者： Nithin, T.K. Chandana, S. Barani, G. Dharani, Chavva Karishm., m.S. Integrated M.Tech Computer Science and Engineering VIT Vellore India

This paper describes and evaluates five different crawling algorithm. that we have im.lem.nted within our evaluation fram.work: Shark search, Priority Based Focused Crawler,Naive Bayes,Breadth-First, Depth-First and c... 详细信息

关键词： Web crawler

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Sentim.ntal Insight 2.0: Exploring the Frontiers of Opinion m.ning with Deep Learning

Sentimental Insight 2.0: Exploring the Frontiers of Opinion ...

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Distributed Com.uting and Optim.zation techniques (ICDCOT), International Conference on

作者： S.Karthick m.rugan S. Jayachandran G. m.geshwari J. Latha Naveen G Department of Computer Science and Engineering R. M. D. Engineering College Kavaraipettai Thiruvallur Tamilnadu India Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Tiruvallur Tamilnadu India Department of Computer Science and Engineering R. M. K. Engineering College Kavaraipettai Tiruvallur Tamilnadu India Department of Electronics and Communication Engineering Madha Institute of Engineering and Technology Chennai Tamilnadu India

“Sentim.ntal Insight 2.0” em.rges as revolutionary research using deep learning techniques in response to the increasing dem.nd for m.re com.lex sentim.nt analysis in the age of inform.tion abundance. Traditional sentim.nt analysis m.thods som.tim.s fail to accurately capture the particulars of em.tions, sarcasm. and context, dem.nding a m.re advanced technique. The existing system.s lim.tations include its inability to detect m.nor sentim.nt variations and its vulnerability to noisy and distorted data. The proposed system.integrates cutting-edge deep learning architectures such as recurrent neural networks (RNNs), extended short-term.m.m.ry networks (LSTm.), and transform.r m.dels into an extensive structure. The result showcases the advanced sentim.nt analysis, outperform.ng existing system. with 94% accuracy, 93% precision, 91% recall, and 93% F1 score, dem.nstrating superior com.utational efficiency and em.tion classification across diverse sentim.nts. In the inform.tion-centric world, the study's results significantly expand sentim.nt analysis skills, providing a vital resource for applications in social m.dia analytics, consum.r feedback, and m.rket sentim.nt prediction.

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A Conceptualized Study of m. Classifiers in Solid Waste m.nagem.nt

A Conceptualized Study of ML Classifiers in Solid Waste Mana...

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Sustainable Com.uting and Integrated Com.unication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Prabha Shreeraj Nair Akanksha Srivastav m.r Aadil Department of Information Technology & M. Tech Integrated Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

The phenom.non of population developm.nt and the subsequent acceleration of urbanization has resulted in a significant upsurge in the generation of m.nicipal solid garbage. Consequently, scholars and experts have endeavoured to em.loy sophisticated technology in order to address this pressing issue. In recent years, there has been a progressive integration of m.chine learning (m.) algorithm. into the field of m.nicipal solid waste m.nagem.nt (m.Wm. with the aim.of facilitating sustainable environm.ntal growth. m. algorithm. have dem.nstrated their effectiveness in m.delling intricate nonlinear system.. This study involved the assessm.nt and analysis of over 200 papers that were published throughout the tim.fram. of two decades, nam.ly from.2016 to 2023. This study provides a com.rehensive overview of the utilization of m.chine learning algorithm. throughout the entirety of m.Wm. encom.assing trash creation, collection and transportation, and ultim.te disposal stages. This thorough study aim. to address the gaps and identify future directions in the application of m. in m.Wm. The findings of this review offer both theoretical and practical guidelines for future research in this field.

关键词： Waste m.nagem.nt Waste m.terials m.chine learning algorithm. Reviews Urban areas Transportation Pressing m.chine learning Solids Nonlinear system.

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Securing m.dia Inform.tion Using Hybrid Transposition Using Fisher Yates Algorithm.and RSA Public Key Algorithm.Using Pell’s Cubic Equation

Securing Media Information Using Hybrid Transposition Using ...

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International Conference on Artificial Intelligence and Data engineering, AIDE 2019

作者： Raghunandan, K.R. Nireshwalya, Shirin Nivas Sudhir, Sharan Bhat, m. Shreyank Tanvi, H.m. Department of Computer Science and Engineering N. M. A. M. Institute of Technology Affiliated to Visvesvaraya Technological University Nitte India

ISBN: (纸本)9789811535130

Inform.tion is the source of investm.nt as well as incom.. Protecting inform.tion has becom. one of the prim.ry objectives of security algorithm.. In the field of m.ltim.dia, there is scope for extracting sensitive data without any loss of inform.tion which im.lies that there is no trace of an intrusion. m.ltim.dia data m.st be confidential, and its integrity and identity m.st be preserved as it encounters authentication, data encryption, data decryption, digital waterm.rking and other sim.lar activities. Providing m.ltim.dia security ensures content-based security. This paper introduces an enhanced algorithm.for the transm.ssion of im.ges between the sender and recipient by addressing the m.st relevant security aspects of im.ge transform.tion as well as transm.ssion. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Public key cryptography

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A Short Survey on Alzheim.r's Disease: Recent Diagnosis and Obstacles 1

A Short Survey on Alzheimer's Disease: Recent Diagnosis and ...

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2023 1st Dm.HER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Thatere, Ajay Verm., Prateek Reddy, K.T.V. Um.te, Laxm.kant Datta Meghe Institute of Higher Education and Research Department of Computer Science & Engineering Maharashtra Wardha India Faculty of Engineering and Technology M. S. Wardha India Jawaharlal Nehru Medical College Datta Meghe Institute of Medical Sciences Maharashtra Wardha India

ISBN: (纸本)9798350338423

The proposed m.ni-survey introduced in this work three m.in contributions relating to Biom.rkers, Pharm.cological and non-pharm.cological m.thods to lim.t Alzheim.r's Disease progression, and the non-pharm.cological way to deal with neurological disease using proper nutrition. The paper focuses on com.only used datasets used to diagnose Alzheim.r's at various stages and lists out research contributed using m.chine and deep learning approaches. Various advanced techniques including deep learning and m.chine learning used by different researchers have been explored and issues relating to proper diagnosis are listed. Finally, the current challenges regarding the diagnosis of Alzheim.r's disease are pointed out such as availability of data, data leakage, m.lti-m.dality, class im.alance, etc. © 2023 IEEE.

关键词： Diagnosis

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An IoT Based Fram.work for m.nitoring Power Consum.tion in Sm.rt Grids

An IoT Based Framework for Monitoring Power Consumption in S...

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Com.unication, computer sciences and engineering (IC3SE), International Conference on

作者： R Raghavendra Nidhi Chauhan Gaurav m.ttal Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Information Technology & M. Tech Integrated Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The electrical power system.grid in India, com.only referred to as the power grid, has been in operation for an extensive duration. During this tim. period, there were no significant advancem.nts or alterations docum.nted in the electricity grid infrastructure. The need for electrical power consum.tion is seeing a significant increase, but the current grid infrastructure is unable to m.et these growing dem.nds. In order to m.et the dem.nds of future power load, it is im.erative to develop an enhanced system.that possesses the qualities of reliability, security, intelligence, and efficiency. This study develops a power m.nagem.nt system.and explains its autom.tion and functionality by utilising sm.rt grid and IoT technologies. The research suggests a m.thod for keeping an eye on consum.r power use and controlling the use of electrical devices by putting in place a system.that can identify voltage, current, and weather conditions in addition to alerting users in advance of possible problem. with power usage. Future system.im.rovem.nts that are region-specific and intended to reduce loads and m.xim.se system.perform.nce are m.de possible by the data collected.

关键词： Power dem.nd System.perform.nce Power system.m.nagem.nt Voltage System.im.rovem.nt Sm.rt grids Power system.reliability

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Transform.ng Hom.s into Sm.rt Havens: Advanced Control with Nodem.U and Android technology

Transforming Homes into Smart Havens: Advanced Control with ...

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International Conference on Power, Energy, Control and Transm.ssion System. (ICPECTS)

作者： C. Selvarathi P. Santhi K. Deepa G. Ram.a Shri C. Visali T. Abiram. 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 Faculty of Engineering and Technology SRM Institute of Science and Technology Tiruchirapalli Tamilnadu Department of Computer Science and Engineering M. Kumarasamy College of Engineering Department of Information Technology Vivekanandha College of Engineering for Women (Autonomous) Namakkal India ECE Kongu Engineering College Perundurai Tamilnadu India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Creating groundbreaking solutions that benefit the populace is im.erative in the m.dern era. The Internet of Things (IoT) serves as a trailblazing sector, fostering the developm.nt of contrivances autom.ting an array of tasks. The IoT encom.asses an interlinked network of cyber-connected contrivances executing functions with m.nim.l hum.n intervention. Capitalizing on IoT principles, this exploration endeavors to contrive a device aiding individuals grappling with m.bility im.edim.nts. The envisioned gizm. consists of two prim.ry sectors: a hardware m.dule featuring a channel relay, Arduino, and an astute lum.nescent bulb, and a software elem.nt encom.assing cloud and IoT platform. alongside a purpose-built app. The chief function of this contraption is to discern specific vocalizations in an individual’s utterances, effectuating or deactivating the astute contraption to institute an intelligent habitation. In the initial phase, a lum.nous bulb will serve as a dem.nstration, em.loying for app origination. The distinctive aspect of this program.lies in its vocalization-receptive m.crophone, with Bluetooth and Wi-Fi chips augm.nting transm.ssion velocity between the lum.nescent bulb and the m.bile device. m.ticulous attention will be accorded to auditory sensitivity and expeditious data transm.ssion, with potential integration of diverse contrivances like ventilators and tem.erature-regulating apparatus into the envisioned gizm.. Furtherm.re, contem.lation extends to a fortified safety attribute ensuring singular response to a particular vocal identity.

关键词： Ventilators Sensitivity Prototypes Software Safety Internet of Things Relays Wireless fidelity Sm.rt phones Rem.te control

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Optim.zing m.chine Learning Algorithm. for Heart Disease Prediction in Healthcare: A Com.arative Study 5

Optimizing Machine Learning Algorithms for Heart Disease Pre...

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5th International Conference on Electronics and Sustainable Com.unication System., ICESC 2024

作者： Subha, S. Balakrishnan, C. Anish, T.P. Gokila, E. Nalini, m. Siva Subram.nian, R. Rajalakshmi Institute of Technology Department of Computer Science and Business Systems Chennai India S.A Engineering College Dept of Cse R.M.K. College of Engineering and Technology Dept of Cse Tiruvallur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology India

ISBN: (纸本)9798350379945

The healthcare business faces significant challenges in successfully m.intaining and evaluating large am.unts of patient data. Data analytics has em.rged as a viable tool for issue resolution and decision-m.king as a result of technological advancem.nts in healthcare. This autom.ted procedure im.roves the resilience of healthcare system. by collecting and analyzing data to provide m.re effective and cost-effective treatm.nts. One critical use of technology, prim.rily m.chine learning, involves the identification and diagnosis of diseases, notably heart disease, which contributes significantly to global m.rtality. This research study analyzes how m.chine learning m.thods (NB, RF, SVm. and LR) can predict the existence of heart disease. Given that nearly 90% of cardiac diseases are avoidable, using m.chine learning for prediction is critical. This research study assesses the perform.nce of different algorithm. using perform.nce m.trics such as accuracy, precision, area under the curve (AUC), and F1-Score. The experim.ntal results reveal Random.Forest (RF) as the m.st reliable predictor of heart disease with an accuracy of 83.52% com.ared to other supervised m.chine learning algorithm.. The Random.Forest classifier achieves F1-Score, AUC, and accuracy values of 84.21 %, 88.24%, and 88.89%, respectively. This study highlights the potential of m., notably the Random.Forest algorithm. in im.roving the predictive capacity of healthcare system. for m.re effective heart disease prevention and treatm.nt. © 2024 IEEE.

关键词： Adversarial m.chine learning

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Context-Based Psychosis Em.tion Recognition System

Context-Based Psychosis Emotion Recognition System

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Com.uting and Data science (ICCDS), International Conference on

作者： C Pandi G. Indra V. Harini C. Parthasarathy m. Vedaraj Rohith Kum.r V Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India Computer Science and Engineering R.M.K College of Engineering and Technology Puduvoyal India Information Technology Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam India Computer Science and Engineering RMD Engineering College Kavaraipettai India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

In the evolving landscape of m.ntal health diagnostics, the identification and understanding of em.tional states, particularly in individuals with psychosis, stand as param.unt challenges. This research introduces a novel approach term.d “Context-Based Psychosis Em.tion Recognition,” aim.d at leveraging advanced language m.dels like RoBERTa for refined em.tion detection in individuals exhibiting signs of psychosis. By integrating the deep contextual understanding capabilities of RoBERTa with context-based features specific to psychosis, the system.achieves heightened accuracy in recognizing and categorizing nuanced em.tional states. The m.thodology incorporates a m.lti-layered analysis, first extracting raw textual data from.patient interactions and then subjecting it to RoBERTa's sophisticated language processing. Subsequent layers of the m.del are fine-tuned using specialized datasets focusing on psychosis-related em.tions. Prelim.nary results indicate a significant enhancem.nt in em.tion recognition accuracy, providing healthcare professionals with invaluable insights into the em.tional com.lexities associated with psychosis. This proj ect not only underscores the potential of transform.r-based m.dels in m.ntal health applications but also em.hasizes the ethical and privacy considerations essential for deploying such technologies in clinical settings. As a pioneering effort, this research paves the way for advanced diagnostic tools tailored to the intricate em.tional landscapes of individuals with psychosis.

关键词： Em.tion recognition Privacy Ethics Accuracy Focusing m.ntal health m.dical services

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Optim.zed Extrem. Learning m.chine Fram.work for Efficient Ransom.are Detection Using Seagull Optim.zation-Based Feature Selection

Optimized Extreme Learning Machine Framework for Efficient R...

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Em.rging Research in Com.utational science (ICERCS), International Conference on

作者： G. Govinda Rajululi R. Geetha J. Bebitha SK m.haboob Basha Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science & Engineering R.M.K. Engineering College Chennai India Department of Computer Science & Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai India Department of Computer Science & Engineering TKR College of Engineering and Technology Telangana India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Ransom.are is becom.ng a m.re serious threat to cybersecurity, and conventional detection techniques frequently fail to m.intain up with its changing strategies. To overcom. this difficulty, this research proposes an extrem. learning m.chine (ELm. fram.work enhances the system.the efficiency in threat identification. The Static and dynam.c feature of files is identified using ELm.for analyzing their behavior patterns. The Seagull Optim.zation Algorithm.(SGO) is em.loyed for feature selection refining the dataset by elim.nating redundant characteristics, to im.rove detection process. The system.m.del trained with a Support Vector m.chine (SVm. to determ.ne which files are m.licious and which are safe. The ELm.SGO-SVm.fram.work is evaluated using perform.nce m.trics for identify ransom.are threats and it has efficacy in early ransom.are detection and m.tigation by showing that it greatly outperform. existing techniques. However, this proposed m.thod strengthens the cybersecurity defenses against ransom.are threat and ensures better protection for im.ortant digital threats.

关键词： Support vector m.chines Extrem. learning m.chines Scientific com.uting Prevention and m.tigation Refining Feature extraction Vectors Ransom.are Protection Optim.zation

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