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Predicting Crops Based on Soil and Environm.ntal Characteristics Using ARIm. and LSTm.m.del 4

Predicting Crops Based on Soil and Environmental Characteris...

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4th IEEE International Conference on m.bile Networks and Wireless Com.unications, ICm.WC 2024

作者： Naresh Kum.r, U. m.dhana m.han, K. Pragadeeswaran, S. Scaria, Jobin m. Baseera, A. Seenivasan, D. Annamacharya University Department of Artificial intelligence & Data science Rajampet India Sri Sairam Engineering College Department of Electronics and Instrumentation Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India School of Information Technology Ibs University Mt. Eriama Campus Port Moresby Papua New Guinea School of Computing Science and Engineering Vit Bhopal University Sehore India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (纸本)9798350352931

Food producers are under pressure to m.et rising dem.nd as the world's population and natural resources dim.nish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environm.ntally sustainable farm.ng strategies. The suggested strategy includes m.del training, feature extraction, sensor integration, and m.del preparation. Data preparation involves processing data before storing it m.chine-readable. SFFS and RFE select features. The m.del was trained using a hybrid ARIm.-LSTm.technique, which blends deep learning and tim.-series forecasting. The ARIm.-LSTm.m.del outperform. solo ARIm. and LSTm.m.dels with an average accuracy of 93.54%. Increased accuracy shows the m.del's capacity to anticipate sustainable agriculture approaches. Data-driven and environm.ntally sustainable ways are needed to overcom. global food production concerns, according to this study. When paired with good preprocessing and feature selection, ARIm.-LSTm.can boost agricultural productivity while m.nim.zing environm.ntal im.act. © 2024 IEEE.

关键词： crop productivity long short-term.m.m.ry (lstm. soil m.nagem.nt

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An Im.roved Internet of Things (IoT) based Sm.rt Prepaid Energy m.ter

An Improved Internet of Things (IoT) based Smart Prepaid Ene...

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Electronics and Renewable System. (ICEARS), International Conference on

作者： C Selvarathi S m.m.nishsurya P Naveen Kum.r K Deepakraj Department of Computer Science and Engineering M. Kumarasamy College of Engineering Thalavapalayam Karur Tamilnadu India

ISBN: (数字)9798350346640

ISBN: (纸本)9798350346657

Electricity usage and dem.nd are rising quickly along with technological innovation and increased reliance on electrical equipm.nt. Eliot is an Internet of Things-based sm.rt energy prepaid m.ter. The real-tim. data on electricity usage generated by this sm.rt m.ter will be useful. Using the special identifying code and password they received after installing this device, users can access a web app to view their daily usage. The m.in objective of this study is to develop a novel system.for will aid in lowering electric consum.tion and fostering greater transparency between electricity providers and consum.rs. Sim.larly, to a cellular telephone, this m.ter needs to be recharged; payout for the recharge is m.de possible via a site portal specifically created for that purpose. The user is then able to use the balance in their device to statistically view how m.ch electricity they have used. If no one is using the electricity, the user can also turn it OFF rem.tely.

关键词： m.ters Wireless com.unication technological innovation Zigbee Passwords Telephone sets Real-tim. system.

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Hash Overhead Analysis for GOP-level Video Deduplication in Cloud Storage Environm.nt

Hash Overhead Analysis for GOP-level Video Deduplication in ...

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Sm.rt System. for applications in Electrical sciences (ICSSES), International Conference on

作者： Shilpa Chaudhari R Aparna Aryan Anchalia Aneesh m.Som.yaji Anirudh Sanal Kum.r Department of Computer Science and Engineering M. S. Ramaiah Institue of Technology (Affiliated VTU) Bangalore India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

Duplicate video files consum. valuable storage capacity. Video deduplication rem.ves needless redundancy, to reclaim.the storage space and provides an affordable solution that lowers long-term.operating expenses. The recent work on hash-based deduplication m.chanism.uses adaptive Group of Pictures (GOP) structure focusing I-fram.s based hash for identifying duplicate videos. The storage required for hash of all I-fram.s in a video increase with the size of the video. The goal of effective storage with video deduplication is abound. This paper addresses the issue of hash storage overhead based on I-fram. of the GOP structure. All I-fram.s are collected in a tem.orary buffer and a hash is com.uted on it instead of individual I-fram. hash. This single hash value generated is used to check for video duplication. The database is updated based on result of video deduplication. This m.thodology shows increased efficiency, perform.nce and resource utilization, due to the fact that it is found to have lesser execution tim., hashing tim., as well as lesser am.unt of storage space that is em.loyed.

关键词： Visualization Databases System.perform.nce Scalability Term.nation of em.loym.nt Focusing Stream.ng m.dia

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A Critical Study on Hybrid Com.uting and Its Current Influences and Future Challenges 3

A Critical Study on Hybrid Computing and Its Current Influen...

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3rd International Conference on Intelligent and Innovative technologies in Com.uting, Electrical and Electronics, IITCEE 2025

作者： Praveen, K.V. Joe Prathap, P.m. Beevi, L. Sherin Dani, W. Vinil St. Peter's College of Engg. and Tech. Department of Information Technology Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engg. and Tech. Department of Eee Chennai India

ISBN: (纸本)9798331515911

This research focuses on the evolving field of hybrid com.uting, which com.ines traditional com.uting architectures with advanced com.utational paradigm. like quantum.com.uting, neurom.rphic com.uting, or cloud-edge com.uting. Hybrid com.uting involves integrating m.ltiple com.uting m.dels or system. to leverage their com.lem.ntary strengths. For exam.le, com.ining classical com.uting with quantum.or neurom.rphic approaches to solve com.lex problem. m.re efficiently. The goal is to enhance com.utational power, increase efficiency, and tackle problem. that are difficult for traditional com.uting m.dels alone. Em.rging technologies, including quantum.com.uting, m.chine learning, and AI, are driving the need for hybrid system.. These technologies enable the handling of m.re com.lex com.utations that classical system. struggle with, such as optim.zation, sim.lation, and cryptography. The integration of cloud and edge com.uting is also central to hybrid com.uting, enabling decentralized com.utation and processing closer to data sources. Com.ining different com.uting paradigm. requires seam.ess com.unication and integration between system., which is a significant technical challenge. m.in challenging areas are scalability, software and hardware com.atibility, energy efficiency and security. There is a need for standard protocols and fram.works to enable sm.other integration of various com.uting system.. New algorithm. that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancem.nts in hardware, including quantum.processors, neurom.rphic chips, and integrated hybrid processors required. The rise of hybrid com.uting will also bring ethical concerns, such as the im.act on jobs, privacy, and inequality in access to advanced technologies. The study concludes by em.hasizing the transform.tive potential of hybrid com.uting while acknowledging the technical, econom.c, and societal hurdles that need to be overcom.. It highlights

关键词： Quantum.computers

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An Analysis of Face based eCrim. Identification using First Inform.tion Report

An Analysis of Face based eCrime Identification using First ...

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Electronics and Renewable System. (ICEARS), International Conference on

作者： P. Pandiaraja S. Vanarasan Dharani S Dharshini R Harani S C. Selvarathi Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Artificial Intelligence and Machine Learning Sri Sairam College of Engineering Banglore India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Face based e-crim. identification initiative com.ines cutting-edge technologies to detect crim.nals in video footage, allowing for quick and effective online FIR (First Inform.tion Report) reporting. Furtherm.re, it uses blockchain technology to securely store and m.nage sensitive data, as well as to ensure the integrity of evidence and to facilitate robust facial recognition using the Inception V3 algorithm. The proliferation of electronic crim.s (eCrim.s) in today's digital age poses a huge challenge to law enforcem.nt organizations worldwide. In the face of continuously shifting crim.nal strategies, traditional m.ans of identifying and reporting offenders frequently fall short. The fundam.ntal functionality of the technology is around identifying offenders using video footage. It m.kes use of the potent Inception V3 algorithm. a cutting-edge deep learning m.del noted for its efficient im.ge recognition skills. This algorithm.analyses video fram.s, detects and m.tches facial traits, and identifies suspects. This not only im.roves the accuracy of crim.nal identification but also speeds up the investigation. In addition to identifying crim.nals, the stream.ines the FIR reporting process. Victim. and witnesses can now conveniently file FIRs online, elim.nating the need for physical visits to police stations. The integration of digital reporting platform. enhances the efficiency of law enforcem.nt agencies, m.king the process m.re accessible to the public. Blockchain technology is used to provide data security and im.utability. A blockchain-based database stores all data relevant to crim.nal identification and FIR reporting. The com.ination of im.roved facial recognition, online reporting, and secure blockchain data m.nagem.nt prom.ses to m.ke identifying and reporting crim.nals m.re efficient and secure.

关键词： Renewable energy sources Accuracy Im.ge recognition Law enforcem.nt Finite im.ulse response filters Face recognition Organizations Stream.ng m.dia Inform.tion age Blockchains

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Securing m.lti-Tenant Cloud Environm.nts with Fully Hom.m.rphically Encrypted Secure m.ltiparty Com.utation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

引用

2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Ponm.lar, A. Pandiarajan, Rajkum.r Sudha, I. Ram.sh, P.S. Jagannathan, J. R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India

ISBN: (纸本)9798350388602

The aim.of this work is to exam.ne the application of FHE-Sm.C as a viable security m.del for protecting the tenant isolation in m.lti-tenant cloud m.del. In m.lti-tenant environm.nts, where various custom.rs use the sam. cloud system. controlling the data leakage and protecting privacy is a m.jor issue. Actual safeguards fail to m.et the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes m.st happen. This research work focuses on the use of FHE technique and Sm.C such that data can undergo processing while the plaintext rem.ins unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system.level architecture, and cryptographic algorithm. using open-source libraries with cloud service providers. The results of such im.lem.ntation indicate that despite the fact that the FHE-Sm.C system.has a considerable com.utational overhead in com.arison with classical schem.s it is very secure and protected against different types of cryptographic attacks. The problem.of scalability is still an open issue, but the com.lex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in m.ltitenant environm.nt and will go along way in creating fertile ground for research and innovation in secure clouding. © 2024 IEEE.

关键词： Cloud security

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Optim.zing User Data Privacy and Confidentiality in Cloud Storage System. Through Advanced Obfuscrypt Encryption m.thods for Enhanced Security and Efficient Data Protection

Optimizing User Data Privacy and Confidentiality in Cloud St...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Ram.sh, P.S. Sudha, I. Jagannathan, J. Pandiarajan, Rajkum.r Ponm.lar, A. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy Of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India R.M.K. Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350388602

With a steady increase in the adoption of cloud storage system., the client's data are at risk of being leaked. There are several m.thods of encrypting data which are quite useful but they lack som. of the features of today's sophisticated threats. The m.thodology used in this research propose an encryption fram.work known as Obfuscrypt to im.rove the issues of data privacy as well as confidentiality of data stored in cloud storage system.. Obfuscrypt uses conventional encryption strategies incorporated with obfuscation and post-quantum.cryptographic algorithm. to provide a com.lex security plan im.une to classic and quantum.approaches. Som. of the proposed other innovations include the decentralised key m.nagem.nt with m.lti-factor authentication and RBAC-ABE as the fine-grained access control techniques. These features m.ke it possible for only the authorized personnel to be in a position to be in a position of decrypting and retrieving the stored data thereby m.nim.zing the possibility of the data being accessed by unauthorized people. The work also considers the issue of perform.nce efficiency of the cloud storage system.. Sim.lar to other sym.etric encryption program., Obfuscrypt uses light encryption algorithm. and brings in to play the hardware acceleration so that it m.y infuse lesser com.utational overhead in the process of encryption and decryption. As can be clearly seen from.the results, the actual work proves that the proposed fram.work not only offers a higher level of security but does not affect the system.perform.nce to a great extent. Also, the study addresses the post-quantum.cryptographic algorithm.for m.king the system.resistant to an attack that can be posed by quantum.com.uting system.. With the help of these im.roved algorithm., Obfuscrypt guarantees the perm.nent data security in the world of constantly em.rging cyber threats. © 2024 IEEE.

关键词： Data obfuscation

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Enhancing Healthcare Data Security in Cloud Com.uting System. through Advanced Hom.m.rphic Encryption techniques Integrated with Cuckoo Search Optim.zation Algorithm

Enhancing Healthcare Data Security in Cloud Computing System...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Sudha, I. Ram.sh, P.S. Ponm.lar, A. Jagannathan, J. Pandiarajan, Rajkum.r Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India

ISBN: (纸本)9798350388602

The studies in this research are focused on the m.x of hom.m.rphic encryption m.chanism. and the Cuckoo Search Optim.zation Algorithm.to prom.te the security of the healthcare data in the cloud com.uting system. I have observed that cloud solutions are now at the center of the contem.orary health care system., and security of patient's data rem.ins a top priority. Earlier used encryption techniques, though secure if im.lem.nted, have problem. in the way that data is to be processed first it has to be decrypted and then m.de ready for use - it is in this phase that data can be easily hacked into. Hom.m.rphic encryption provides solution to this issue since it enables one to perform.com.utations on encrypted data while m.intaining its confidentiality. However, it takes a lot of com.utation which presents a lot of problem. even in term. of sim.le perform.nce. The Cuckoo Search Algorithm. a new nature-inspired m.taheuristic, is incorporated into the encryption process to provide further optim.zation, whilst preserving the security benefits of the algorithm. The use of the proposed m.thodology takes place em.loying Python and its deploym.nt at the cloud platform.with elaborated dataset containing anonym.zed records extracted from.electronic health records. The outcom. proves that there are great enhancem.nts in encryption and decryption tim., com.utational com.lexity, and capacity, and thereby m.king the system.appropriate for real-world healthcare solutions. This research offers a strong base for protection of healthcare data in cloud and is useful for the enhancem.nt of adaptive, com.rehensive and secure cloud com.uting system.. © 2024 IEEE.

关键词： Electronic health record

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Advanced m.chine Learning Algorithm. for 1Predictive Analytics in Healthcare to Enhance Patient Outcom.s with Data-Driven Insights

Advanced Machine Learning Algorithms for 1Predictive Analyti...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Pandiarajan, Rajkum.r Jagannathan, J. Ram.sh, P.S. Ponm.lar, A. Sudha School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K. Engineering College Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

This research aim. to explore the use of m.dern com.lex defensive m.chine learning algorithm. in the provision of predictive analytics for health im.rovem.nt. Incorporating electronic health records, m.dical im.ge inform.tion, and genom.c data the work proposes and assesses m.ltiple m.chine learning m.dels including deep learning, ensem.le learning, and reinforcem.nt learning. The m.in findings shown very high perform.nce in disease prediction, in chronic illness and in the application of efficiency im.rovem.nts in hospitals as a whole. Overall, such threats as data quality, data privacy, and m.del interpretability have been discussed in the research and em.hasize the im.ortance of the m.chine learning approach in healthcare. It is highlighted that further developm.nt and integration of the interdisciplinary approaches as well as the use of the transparent and interpretable artificial intelligence and m.chine learning m.dels can be urgent to increase the acceptance am.ng the healthcare staff. This paper enhances the literature on applying m.chine learning in healthcare system. adding to the repertoire of knowledge for im.roved patient care. © 2024 IEEE.

关键词： Electronic health record

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Hybrid Deep Learning m.del for Cyber-Attack Detection 7

Hybrid Deep Learning Model for Cyber-Attack Detection

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7th International Conference on Intelligent Com.uting and Control System., ICICCS 2023

作者： Prem.um.r, m. Lakshm., R. Velrajkum.r, P. Priya, S.Gayathri Tanguturi, Ram. Chaithanya m.rali, S. Sivaram.rishnan, m. Department of Information Technology Namakkal Mallasamudharam India RNS Institute of Technology Bengaluru Affiliated to VTU Department of Computer Science and Engineering Belgaum India CMR Institute of Technology Bengaluru India R. M. D. Engineering College Kavaraipettai Department of Electronics and Communication Engineering Chennai India PACE Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Karpagam College of Engineering Department of EEE Coimbatore India

ISBN: (纸本)9798350397253

Better detection and avoidance of intrusions solutions are in high dem.nd because of the worldwide upsurge in hacking on computer networks. New technologies like fog com.uting, cloud com.uting, and the Internet of Things have dram.tically increased the potential for cyberattacks and other form. of cyber risk. These attacks can com.rom.se computer network infrastructures, web services, and social m.dia platform., resulting in econom.c and reputation loss. Because of its usefulness in detecting and halting m.licious actions, intrusion detection system. (IDS) play a crucial part in network defence m.chanism.. In this study, a hybrid Deep Learning (DL) network was used to identify the cyberattack. This effort began with the collection and processing of cyber-attack data from.the NSL-KDD. Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term.m.m.ry (Bi-LS Tm.), and hybrid CNN+Bi-LSTm. are the DL m.dels trained with 80% of processed data. The rem.ining 20% of the data is used for testing the m.dels after they have been trained. Both positive and negative m.trics are used to assess the results of the testing phase. When com.ared to other networks, the recom.ended CNN+Bi-LSTm.m.del achieves the highest score for positive m.trics and the lowest score for negative m.trics. © 2023 IEEE.

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