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International Conference on I-Sm.C (IoT in Social, m.bile, Analytics and Cloud) (I-Sm.C)

作者： Pandiaraja P Karthik K Rajesh Kanna P C.Ushapriya P.C.Senthil m.hesh m.thum.nickam.K Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur Tamilnadu India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamilnadu India Department of AI & ML St. Martin’s Engineering College Secunderabad Telangana India Department of Computer Science and Engineering Excel Engineering College Komarapalayam Tamilnadu India Department of Information Technology Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The high usage of storage in cloud, ensuring the privacy and security of inform.tion stored in the cloud has com. up with a difficult concern. One approach to address this issue is the use of techniques which are encrypted, where data is already encrypted being uploaded to the storage of cloud. However, traditional encryption m.thods hinder the ability to find and retrieve particular inform.tion from.the secured inform.tion, as the inform.tion needs to be decrypted first. This lim.tation ham.ers the usability and efficiency of storage of cloud system.. To overcom. the challenge, this research says a novel approach for keyword search on secured inform.tion in storage of cloud using an AES approach. The algorithm.of AES is a m.xim.m.adopted sym.etric encryption m.thod fam.liar for efficiency and robustness. By leveraging the AES encryption approach, the solution assures the confidentiality of inform.tion stored while enabling efficient keyword-based search operations without the need for decryption. The proposed approach utilizes a com.ination of cryptographic techniques such as AES encryption and index structures to access efficient inform.tion findings on the secured inform.tion. A secure index is created during encryption process that enables for potent retrieval of encrypted data related to specific keywords. The encrypted data rem.ins confidential, as only authorized users possess the necessary decryption keys to retrieve the plaintext inform.tion. During data outsourcing, data owner can set the access perm.ssion based on their user identity. Users should have specific identity with decryption key for accessing data from.cloud. Here data m.nager can also provide particular period for accessing inform.tion from.cloud. The results dem.nstrate that the keyword findings on secured inform.tion in storage of using the AES approach achieve a balance between data privacy and search functionality. The solution offers an effective m.ans to securely store and retrieve data

关键词： Cloud com.uting Data privacy Privacy Organizations Robustness Encryption Outsourcing Usability Secure storage Indexing

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Design and Developm.nt of an AI based Intelligent Door for Hom. Security System 2

Design and Development of an AI based Intelligent Door for H...

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2nd IEEE International Conference on Advances in Com.uting, Com.unication and Applied Inform.tics, ACCAI 2023

作者： Sreelakshm., R. Kum.r, Ata. Kishore Titus, Anita Hem.jothi, S. Venkatesh, J. Lavanya, A. Veltech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of ECE Chennai India School of Engineering Department of ECE Tirupati India Jeppiaar University Department of ECE Chennai India Vel Tech High Tech Dr.RangarajanDr.Sakunthala Engineering College Chennai India Chennai Institute of Technology Department Computer Science and Engineering Chennai India R.M.K. Engineering College Department of ECE Chennai India

ISBN: (纸本)9798350315905

These days, safety concerns are universal. A house, a factory, a bank, and so on are all things that virtually everyone desires. In today's world, when m.re and m.re people have easier access to various resources, it is increasingly vital to ensure that one has a safe place to live. There m.st be a way to keep us secure since the daily rise in crim. rates dem.nds it. We're all aware that there are sophisticated security system. out there, but unfortunately, they're out of reach for a lot of people. Trespassing, intrusion, and other form. of illegal entry are com.onplace, with m.ny com.on targets including banks, business offices, financial organizations, jewellery stores, and governm.nt organizations. Our goal is to alleviate this problem.by creating a low-priced electronic system.that can detect intruders' m.vem.nts and trigger an alert. To this end, we offer a new Android app called Door Security System.that m.kes use of IoT technology to keep tabs on the door's state, m.nage the door, and boost hom. safety. The sm.rt lock system.in this instance is an android-based one, and it can function in both single-m.de and m.lti-m.de settings. Financial and com.ercial institutions have an urgent need for this kind of technology. The system.also provides features for the average user, who does not need to be the only one with access to the lock's controls. The system.s high efficiency, low price, and cutting-edge features and user-friendly design all contribute to its value. In the event that the door is opened without perm.ssion, the alarm.will sound and a m.ssage will be sent to the hom.owners. © 2023 IEEE.

关键词： Internet of things

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Effects of Link Disruption on Licklider Transm.ssion Protocol for m.rs Com.unications 12th

Effects of Link Disruption on Licklider Transmission Protoco...

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12th International Conference on Wireless and Satellite Services, WiSATS 2021

作者： Liang, Jie Wang, Ruhai Liu, Xingya Yang, Lei Zhou, Yu Cao, Bin Zhao, Kanglian Phillip M. Drayer Department of Electrical Engineering Lamar University BeaumontTX United States Department of Computer Science Lamar University BeaumontTX United States School of Electronics and Information Engineering Soochow University Jiangsu China School of Electronic Science and Engineering Nanjing University Jiangsu China

ISBN: (纸本)9783030933975

Som. studies on Licklider transm.ssion protocol (LTP) in cislunar and m.rs com.unications have been done. However, little work is currently available in studying how link disruption affects LTP for m.rs com.unications. This study presents a discussion of link disruption on LTP for m.rs com.unications based on experim.ntal data block transfer. The study focuses on how link disruption duration and starting tim. affect the num.er of transm.ssion attem.ts taken, round-trip tim. (RTT) and goodput perform.nce for m.rs com.unications. © 2022, ICST Institute for computer sciences, Social Inform.tics and Telecom.unications engineering.

关键词： Wireless networks

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Crop yield prediction: two-tiered m.chine learning m.del approach

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International Journal of Inform.tion technology (Singapore) 2021年第5期13卷 1983-1991页

作者： Shidnal, Sushila Latte, m.ityunjaya V. Kapoor, Ayush Department of Computer Science and Engineeriing Sir M. Visvesvaraya Institute of Technology Bangalore 562157 India JSS Academy of Technical Education Bangalore 560060 India Department of Information Science and Engineering Sir M. Visvesvaraya Institute of Technology Bangalore 562157 India

Nutrient deficiency analysis is essential to ensure good yield. The crop yield is dependent on the nutrient contents and drastically affects the health of the crop. In this paper the nutrient deficiency of a paddy crop is considered. Tensor Flow’s (Google’s m.chine Learning Library) is used to build a neural network to classify them.into nitrogen, potassium. phosphorous deficiencies or healthy independently. It is necessary to have an optim.l balance between nitrogen, potassium.and phosphorous content. Tensor Flow’s m.del identifies the deficiency using a set of im.ges. The result is fed to “m.chine learning driven layer” to estim.te the level of deficiency on a quantitative basis. It specifically m.kes use of k m.ans-clustering algorithm. It is then evaluated through the rule-m.trix to estim.te the cropland’s yield. A fair prediction of 76–77% was observed with two tired m.chine learning m.dels. © 2019, Bharati Vidyapeeth's Institute of computer Applications and m.nagem.nt.

关键词： Crop yield Neural network Rule-based m.trix Tensor flow

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Pulse Non-Orthogonal m.ltiple Access (PNOm.) technique for Future Generation Wireless Networks

Pulse Non-Orthogonal Multiple Access (PNOMA) Technique for F...

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2023 IEEE International Conference on Research m.thodologies in Knowledge m.nagem.nt, Artificial Intelligence and Telecom.unication engineering, Rm.m.TE 2023

作者： Radhika, S. Hem.latha, S. Suresh, Helina Rajini m.kanyadevi, K. Andrews, William.J. Kam.tchi, S. Department of Information Technology R.M.K. Engineering College Tamil Nadu Chennai India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Tamil Nadu Chennai600127 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Tamil Nadu Karur India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of Electronics and Communication Engineering Saveetha Nagar Thandalam Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350305708

The foundation of Non-orthogonal m.ltiple access (NOm.) is providing services to several consum.rs at once while using the sam. resource in term. of frequency, tim., and area. In this research, we provide a novel approach to reducing com.lexity and power consum.tion by using ideas from.pulsed NOm. system.. The strategy is quite versatile and m.y be used with num.rous other system.. The im.rovem.nt that we suggest differs from.the OFDm.system.in that the am.litude shaping pulse of the OFDm.m.dulation uses pulses with duty cycles. The tim. dom.in spectrum.of the m.dulated signals is spread out by pulsing NOm. sym.ols, resulting in a spreading gain that is inversely proportional to the duty cycle of the pulsed subcarriers. We show that the additional diversity m.y be com.letely utilized by a pulsed-NOm. receiver without the need for OFDm.receivers. It is dem.nstrated that while pulsed NOm. receivers outperform. with OFDm.receivers, it also offers advantages over NOm. receivers in term. of intrinsic low com.lexity and low power consum.tion. © 2023 IEEE.

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Expression of Concern for: Predict the Quality of Freshwater using Support Vector m.chines

Expression of Concern for: Predict the Quality of Freshwater...

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Applied Artificial Intelligence and Com.uting (ICAAIC), International Conference on

作者： Sriram.S. Santhiya S. Rajeshkum.r G. Gayathri S. Vijaya K. Rajesh Kanna P. Department of Computer Science and Engineering Bannari Amman Institue of Technology Sathyamangalam Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur Department of Information Technology Karpagam College of Engineering Coimbatore Department of CSE Velalar College of Engineering and Technology Erode

ISBN: (数字)9781665456302

ISBN: (纸本)9781665456319

The purity of the water has recently been threatened by a num.er of contam.nants. As a result, it is now crucial for the m.nagem.nt of water pollution to m.del and anticipate water quality. In order to forecast the water quality index (WQI) and water quality classification (WQC), this work creates cutting-edge artificial intelligence (AI) approaches. Today, m.ny people are afflicted with severe illnesses brought on by tainted water. This study will look at a water quality m.nitoring system.because it provides inform.tion on water quality. It is planned to identify forecasts for water quality using a m.chine learning system. The depletion of natural water resources including lakes, stream., and estuaries is one of the m.st significant and alarm.ng issues facing hum.nity. The effects of dirty water are widespread and have an im.act on several people. Water resource m.nagem.nt is therefore essential for m.xim.zing water quality. If data are analyzed and water quality is foreseen, the effects of water contam.nation can be effectively addressed. Even though this subject has been covered in a large num.er of earlier research, m.re has to be done to boost the effectiveness, dependability, accuracy, and utility of the current techniques to m.naging water quality. The goal of this study is to develop an Artificial Neural Network (ANN) and tim.-series analysisbased water quality prediction m.del. The historical water quality data used in this study has a 6-m.nute tim. period and is from.the year 2014. The National Water Inform.tion System. a website operated by the United States Geological Survey (USGS) is where the data com.s from.

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Autom.ted Em.il Notifications and Security m.nitoring for Cloud Storage Access using Cloud Function 3

Automated Email Notifications and Security Monitoring for Cl...

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3rd International Conference on Innovative m.chanism. for Industry Applications, ICIm.A 2023

作者： Renuka, N. Panneer Selvam. m. Ponm.rugan, P. Kiruthika, S. Selvam. N. Kannan, N. KSR Institute for Engineering and Technology Department of Information Technology Tiruchengode India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Deemed to Be Univeristy Dean - IPR Meenakshi Academy of Higher Education & Research Chennai600078 India Erode Sengunthar Engineering College Department of M.Tech - Computer Science and Engineering Erode Thudupathi India M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India

ISBN: (纸本)9798350343632

To m.nitor a specified Google Cloud Storage bucket for any changes or accesses m.de to its contents. When an access or change is detected, the cloud function will autom.tically send an em.il notification to a specified em.il address with inform.tion about the access or change, including the tim. of access, the user who accessed the data, and the specific data that was accessed. The paper represents the Google Cloud Platform.services, which includes Cloud Storage and Cloud Functions. The Cloud Function will be triggered by a Cloud Storage whenever an object is accessed. The Cloud Function will then parse the notification m.ssage and extract the relevant inform.tion, such as the object nam. and access tim., and send an em.il notification. The autom.ted em.il notification system.will help cloud storage adm.nistrators m.nitor access to their storage buckets, identify potential security breaches or unauthorized access, and take appropriate action in a tim.ly m.nner. It also provides a record of all access activity, which can be useful for audit purposes and com.liance requirem.nts. © 2023 IEEE.

关键词： Cloud Function Cloud Storage Access Em.il Notification Google Cloud Platform Security m.nitoring

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Pom.granate Quality Analysis and Classification Using Feature Extraction and m.chine Learning 6

Pomegranate Quality Analysis and Classification Using Featur...

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6th International Conference on Electronics, Com.unication and Aerospace technology, ICECA 2022

作者： Kum.r, Pullela S.V.V.S.R. Sudha, S. Das, Pranjit Pradeep, D. Isaac, J. Sam.on Vijaipriya, K. Aditya College of Engineering Department of Computer Science and Engineering Andhra Pradesh Surampalem533437 India Asian College of Engineering and Technology Department of Biomedical Engineering Tamil Nadu Coimbatore641110 India Department of Computer Science Engineering Andhra Pradesh Vaddeswaram522302 India M. Kumarasamy College of Engineering Department of Computer Science Engineering Tamil Nadu Karur639113 India Karunya Institute of Technology and Sciences Department of Biomedical Engineering Coimbatore641114 India Sri SaiRanganathan Engineering College Department of Electronics and Communication Enginneering Tamil Nadu Coimbatore641109 India

ISBN: (纸本)9781665482714

Fruits are an excellent source of nutrients and m.nerals. They have a high concentration of antioxidants and flavonoids, which are beneficial to one's health. Pom.granates have a high potential in preventing cell dam.ge, boosting our im.unity, helping with sm.oth digestion, fighting type-2 diabetes, keeping vital param.ters in check and are seen to be effective inthe prevention of cancers. India is considered the largest producer of excellent varieties of pom.granates and thus the quality analysis in the export operation of pom.granates is highly concerned. Grading of pom.granates is very necessary for post-harvest m.nagem.nt and is perform.d based on the external appearance like attractive colours, texture, size and shape which decides the standard of the fruit. m.nual grading can be done which requires hum.n operation and consum.s m.re tim.. Hence quality assessm.nt of pom.granates can be done using m.chine Learning(m.) which is highly efficient. The process of feature extraction yields accurate results and can be done quickly. m. technology im.roves accuracy and efficiency and has im.roved user experience. The review paper proposes an efficient m. approach for pom.granate quality analysis using Histogram.of Oriented Gradients (HOG) and Local Binary Pattern (LBP) feature extraction m.thods. K-Nearest Neighbour (KNN) and Naive Bayes (NB) algorithm. are im.lem.nted in the designed m.del using both sets of feature extractors and the result illustrates that the LBP + NB m.del perform. with better efficiency and greater accuracy. © 2022 IEEE.

关键词： Feature extraction

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Recursive CNN m.del to Detect Anom.ly Detection in X-Ray Security Im.ge 2

Recursive CNN Model to Detect Anomaly Detection in X-Ray Sec...

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2nd International Conference on Innovative Practices in technology and m.nagem.nt, ICIPTm.2022

作者： Senthil Kum.r, R. Syed m.sthafa, A. Balaji, A. Singh, Gurpreet Kum.r, Ashok m.nikandaprabu, P. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Computer Science And Engineering Chennai India M. Kumarasamy College Of Engineering Department Of Information Technology Tamil Nadu Karur India Rajalakshmi Institute Of Technology Department Of Electronics And Communication Engineering Chennai India Sant Baba Bagh Singh University Department Of Computer Science And Application Punjab Jalandhar India Banasthali Vidyapith Department Of Computer Science Banasthali India Sona College Of Technology Department Of Mechanical Engineering Tamilnadu India

ISBN: (纸本)9781665466431

To address the issue of contraband scale difference in the identification of X-ray pictures during security inspection, we upgrade the Faster RCNN network and propose a m.lti-channel region proposal network (m.RPN). m.lti-layer feature extraction is achieved using the com.lem.ntarily of distinct levels of convolution features in visual sem.ntics, and the richer sem.ntic com.onents of VGG16 high-level layers and the shallower edge features of low-level layers are fused;To construct a m.lti-scale contraband detection network, the m.lti-scale candidate target regions are m.pped to the corresponding feature m.ps;dilated convolutions are introduced into the m.lti-channel, and a m.lti-branch dilated convolutions m.dule (DCm. is designed to increase the Receptive field and thus enhance features at different scales. On the self-created data set SIXray OD, the enhanced algorithm.achieves an average detection accuracy of 84.69 percent and a test perform.nce im.rovem.nt of 6.28 percent over the original network. Additionally, the testing findings indicate that the enhanced algorithm.s recognition accuracy has been increased to a considerable level. © 2022 IEEE.

关键词： Convolution

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Advancem.nts in Cybersecurity Using Deep Learning techniques Attack Detection for Trojan Horses

Advancements in Cybersecurity Using Deep Learning Techniques...

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2024 International Conference on Electrical, Electronics and Com.uting technologies, ICEECT 2024

作者： Hosanna Princye, P. Ahila, A. Lakshm., A. Anna Ragavendran, N. Purushotham.n, K.E. Um. m.heswari, G. Saravanakum.r, R. Sri Sairam College of Engineering Department of Electronics and Communication Engineering Anekal Bangalore India R.M.K. Engineering College Department of Information Technology Tamil Nadu Chennai India RMK College of Engineering and Technology Department of Artificial Intelligence and Data Science Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India RMK College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Software Analyst Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350378092

Today's computer networks are threatened by Trojan horse assaults and other cybersecurity dangers. We propose and evaluate deep learning m.thods using the Kaggle-hosted Trojan Detection dataset to detect Trojan horse assaults. This project uses these approaches. Our m.lware detection technology uses CNN classification for accuracy. We clean and enhance the dataset to m.ke the m.del m.re versatile. Due to extensive testing and optim.zation, Trojan horse detection rates reach 98.47%. We found that the deep learning architecture accurately identifies m.lware. We also discuss CNN m.del features that illum.nate Trojan horse attacks. The technique offers com.lete cyber security and expands our knowledge of harm.ul software. Deep learning m.y boost cybersecurity defenses against em.rging attacks like Trojan horses, according to our study. © 2024 IEEE.

关键词： Cyber attacks

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