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International Conference on Emerging Research in Electronics, computer science and Technology (ICERECT)

作者： Abhishek Dubey Vijay Kumar Priyank Singhal Savyasachi Atowar ul Islam R Kuppuchamy Department of CSE Chandigarh Engineering College Jhanjeri India Department of CSE JAIN University Bangalore India Department of Computing Sciences & IT Mahaveer University Moradabad India Department of information technology Lalit Narayan Mishra College of Business Management Muzaffarpur Bihar Dept. of Computer Science and Electronics University of Science and Technology Techno City Meghalaya Department of Computer Applications PSNA College of Engineering and Technology Dindigul India

Scientists in academia and business have thoroughly studied Encryption Scheme (SE). Whereas many scientific SE methods provide verifiable confidentiality, in order to attain high performance, they typically reveal certain “trans (such as search and access patterns). Yet, a number of infer methods have taken advantage of this leaking, for instance, a query recovering attack that uses foreknowledge to convert transparent question passageways to their matching keywords. But at the other hand, several more pro - posed SE schemes require substantial modification applications, that also tends to make them less convenient, weak in user experience, and complicated to deploy. In this research, we offer IDCrypt, a safe and useful Is the Enclosure (SSE) scheme with demonstrable security power for azure. IDCrypt uses cryptographic techniques to increase SE's security level and search efficiency. We also highlight the key difficulties in safely sharing decrypted data across different individuals and search across several indexes. We suggest a coin search strategy to maintain the search features across several indexes or a key sharing system that combines Personality Crypto (IBE) with Community Encrypted data to solve the aforementioned problems (PKE). Our empirical findings demonstrate that the number of key agreement's expense is rather modest.

关键词： computer science Protocols Art Benchmark testing Search problems User experience Encryption

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Adaptive DNA Cryptography With Intelligent Machine Learning for Cloud Data Defense

Engineering Reports

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Engineering Reports 2025年第6期7卷

作者： Animesh Kairi Tapas Bhadra Saroj Kumar Pandey Anurag Sinha Anindya Nag Institute of Engineering and Management Kolkata India Aliah University Kolkata India Department of Computer Engineering & Applications GLA University Mathura India Tech School Computer Science Department ICFAI University Ranchi India Department of Computer Science and Engineering Northern University of Business and Technology Khulna Bangladesh

With the exponential growth of sensitive data generated and stored in cloud environments, traditional cryptographic techniques are increasingly strained in addressing emerging security threats. While DNA cryptography offers promising security potential due to its complexity and biological uniqueness, its standalone application lacks adaptive intelligence for real-time threat mitigation. This research addresses the gap in integrating biologically inspired encryption with artificial intelligence by proposing a hybrid framework that combines DNA-based cryptographic encoding with machine learning for robust cloud data protection. The proposed methodology introduces an adaptive model where DNA cryptographic operations—such as DNA encoding, XOR operations, and complementary rules—are optimized using supervised machine learning algorithms. These algorithms dynamically enhance encryption-decryption processes based on anomaly detection and performance metrics. Experimental results on benchmark cloud datasets demonstrate significant improvements in encryption strength, key management, and resistance to common attacks, without compromising processing efficiency. The implications suggest a novel direction for future-proofing cloud security infrastructures through the convergence of bio-computing and intelligent systems.

关键词： cloud security cryptosystem DNA cryptography genetics cryptography

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A Novel Transfer Learning Technique for Rice Leaf Disease Detection

A Novel Transfer Learning Technique for Rice Leaf Disease De...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Naveen Kumar Nikshepa T Piyush Kumar Pareek Parameshachari B. D Madhumala R B Sowmya S R Department of Computer Applications (BCA) Dr NSAM First Grade College (Affiliated to Bangalore City University) Bengaluru Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore Department of Computer Science & Engineering Nitte Meenakshi Institute of Technology Bengaluru Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore

ISBN: (纸本)9781665493970

Image processing has become more sophisticated over the course of the last several years. In this study, we are presenting a model that, based on a convolution network, can identify the status of the rice leaf, namely whether it is a healthy leaf or one that has been afflicted by any illness. Where rice is widely regarded as the primary source of sustenance not just in India but also in other areas of the globe. The agriculture may get infected with the illness and the crops may be affected by the disease for some of the causes listed above. When it may be more difficult to discover the kind of illness, and where farmers may not have the information to find the specific type of disease that an infected leaf was afflicted with. In order to do this, we are use the VGG16 network, which is an architecture based on a convolution neural network (CNN). This is done so that the crop disease can be readily identified, and so that correct findings can be obtained.

关键词： Deep learning Convolution Image processing Transfer learning Neural networks Crops Predictive models

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IoT-Based Diagnosis and Recommendation System for Chronic Diseases Using Patient Health Records

IoT-Based Diagnosis and Recommendation System for Chronic Di...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： S. K. UmaMaheswaran Durgaprasad Gangodkar V. Samatha I. S. Chakrapani Dharamvir Dharam Buddhi Department of Mathematics Sri Sai Ram Engineering College Chennai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Computer Applications DKW Govt. College for Women (A) Nellore AP India Dept.of Zoology PRR&VS Govt College AP Dept. of MCA The Oxford College of Engineering Bangalore Uttaranchal Institute of Technology Uttaranchal University

The proliferation of IoT applications, notably in the sphere of health care, has led to discussion of patient health records utilising data gathered from IoT-connected devices. Biological data from patients' medical records is mined for health analysis and diagnosis. Certain types of illness, known as chronic illnesses, are completely quiet yet devastating if left untreated. Recent years have seen an uptick in interest from academics in the utilization of patient health information data for the pre-emptive identification of chronic diseases. On the other side, healthcare and medical assistance have benefited enormously from the implementation of recommender systems that use machine learning techniques. Using an Internet of Things device, this research implemented a medical recommendation system to aid in the early diagnosis and management of chronic conditions. The current technique made use of the dataset of digitised patient health records that is housed in the Physio Net data repository. The current dataset contains patient health records that have been documented in accordance with the diseases that have been identified and the doctor's diagnosis. The recommended technique uses K-nearest neighbour classification to identify the kind of ailment before using collaborative filtering to select the optimal course of treatment for patients. The outcomes of using the suggested methodology show that this technique, which is based on using patient symptom similarity, produces better results than previous methods and has a high precision in diagnosing and forecasting chronic illnesses. After determining the kind of disease using the closest neighbour classification method, the collaborative filtering strategy is utilised to select the optimal course of therapy for patients. The outcomes of using the suggested methodology show that this technique, which is based on using patient symptom similarity, produces better results than previous methods and achieves better accuracy

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Enhanced security for the authentication of digital signature from the key generated by the CSTRNG method 3

Enhanced security for the authentication of digital signatur...

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3rd International Conference on Smart Systems and Inventive Technology, ICSSIT 2020

作者： Prasad, B.V.V.S. Mandapati, Sridhar Haritha, B. Begum, Md.Jareena Computer Science Engg. Chebrolu Engineering College Guntur India R. V. R J. C COLLEGE of ENGINEERING Department of Computer Applications Guntur India Computer Science Engg. Bapatla Women's Engineering College Bapatla India

ISBN: (纸本)9781728158211

Security is the most critical feature of the present e-world. Many applications are based on random number generation. In the present situation, it is very difficult to hide the data. So the key-based encryption process is suggested to keep the files secure. The protection of the data depends on the key. Key generation is more important for all cryptography applications, which is entirely dependent on randomness. As long as many designers have been able to develop many Random Number Generator-based keys. Also work is going on to make hacker data more stable. CSTRNG can be suggested in this work. One of the most commonly used technologies in the marketing field is the production of digital signatures. The generation of digital signatures can be generated using many keys that have the property of RNG. The most stable RNG is the Cryptographically Secured True Random Number Generator (CSTRNG). This could generate more unexpected numbers. Work has been built on the Xilinx ISE simulator. © 2020 IEEE.

关键词： Cryptography

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Solution of multi-term fractional order delay differential equations using homotopy analysis method

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AIP Conference Proceedings 2023年第1期2457卷

作者： Mohammed S. Ismael Fadhel S. Fadhel Ali H. Fayadh Department of Mathematics and Computer Applications College of Science Al-Nahrain University Baghdad Iraq

In this paper, the homotopy analysis method have been utilized for solving non-linear delay differential equations of multi-term fractional order, and the fractional derivative is expressed in the Caputo sense. In this approach, the solutions are found in the form of convergent power series components are easily computed. Some illustrative examples are simulated using computer programs written in Mathcad 15 computer software and then a comparison between the exact solution and the approximate results are presented to show the reliability and the accuracy of the proposed method.

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An AI-based Approach to Train a Language Model on a Wide Range Corpus Using BERT

An AI-based Approach to Train a Language Model on a Wide Ran...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Aryan Bhutda Gopal Sakarkar Nilesh Shelke Ketan Paithankar Riddhi Panchal Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Department of Computer Science and Applications Dr. Vishwanath Karad MIT World Peace University Pune India Co-Founder & CTO Konverge.AI Pvt. Ltd. Nagpur M.H India

In 2018, Google developed and released BERT (Bidirectional Encoder Representation from Transformers) for Natural Language Processing (NLP). The English language models in BERT are two. To help close this data gap, a range of techniques have been developed for pre-training general purpose language representation models on the huge amount of unannotated content available on the web. Unlike when BERT is trained on these datasets from scratch, the model must first be pre-trained on small amounts of data. Once a measurable result is obtained, it can then be applied to tasks like NLP sentiment analysis and question answering, which causes to a notable rise in accuracy. The purpose of this research is to train the language model and conduct a descriptive study of BERT in various applications. For the implementation, throughout the task we are taking a famous textbook of medicine "Harrison’s Principle of Internal Medicine" into consideration. The research has made advantage of high configuration computation, specifically the Nvidia Tesla DGX-V100 GPU.

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FEBSRA: Fuzzy Trust Based Energy Aware Balanced Secure Routing Algorithm for Secured Communications in WSNs

Research Square

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Research Square 2021年

作者： Anitha, R. Bapu, B.R. Tapas Kuppusamy, P.G. Partheeban, N. Sasikumar, A.N. Department of Computer Applications S. A. Engineering College India Department of Electronics & Communication Engineering S. A. Engineering College India Computer Science and Engineering Galgotias university India Computer Science and Engineering Panimalar Engineering College Chennai India

Energy efficiency is playing major role in the design of a sensor network for improving the network lifetime and the trust is also important for providing the security to the data communication process. Delay is also major challenge today due to the enormous volume of network users. For overcoming all these issues, many researchers have been developed energy efficient security mechanisms for fulfilling the requirements. Even though, they are not able to satisfy the current requirements and users in terms of energy consumption, delay and security. For this purpose, this paper propose a new algorithm called Fuzzy Trust Based Energy Aware Balanced Secure Routing Algorithm (FEBSRA) which is able to provide the effective delay constrained secured routing algorithm which uses the fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 both inclusive for making final decision over sensor nodes with the consideration of number of hops between the source and destination nodes, energy level of the nodes and the trust scores. Moreover, a new trust model is also introduced new formulae for calculating the trust scores with the consideration of energy level of the communication delay which is calculated by using number of hops used for the specific communication. The experimental results of the proposed secured routing algorithm demonstrated that the performance in terms of energy consumption, less delay and high throughput with security is better when compared to the existing systems. © 2021, CC BY.

关键词： Fuzzy logic

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Infinite-Q guided modes radiate in the continuum

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Physical Review B 2023年第11期107卷 115415-115415页

作者： Kaili Sun Heng Wei Weijin Chen Yang Chen Yangjian Cai Cheng-Wei Qiu Zhanghua Han Shandong Provincial Key Laboratory of Optics and Photonic Devices Center of Light Manipulation and Applications School of Physics and Electronics Shandong Normal University Jinan 250358 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore 117583 Chinese Academy of Sciences Key Laboratory of Mechanical Behavior and Design of Materials Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei China

Guided modes in photonic structures, with broadband infinite-quality factors, are inaccessible from the far field due to the momentum mismatch. They become leaky modes in the continuum when the mismatch is compensated for by introducing periodic perturbations to form photonic structures. However, the quality factors (Q factors) of such leaky modes deteriorate significantly in most regions of the k-space except at a few discrete high-symmetry points. It is an intriguing question as to whether guided modes can hop above the light cone and yet maintain high Q. Here, we propose a double-band-folding strategy to achieve high-Q leaky modes in compound lattices, exemplified with a one-dimensional grating and a two-dimensional zigzag array of dielectric disks. The Q factor of those leaky modes can be made ultrahigh at arbitrarily any incident angles, showing that such modes do not originate from bound states in the continuum (BICs) above the light cone. Our findings provide unique insight for elucidating the relations between guided modes, BICs, quasi-BICs, and radiation. They further provide a generalized recipe for numerous optical applications such as all-dielectric sensing, lasing, and nonlinear generation with multiple inputs.

关键词： Light propagation, transmission & absorption Metasurfaces Nanophotonics Photonic crystals

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Optimized Predictive Storage Resource Allocation Through Neural Forecasting for Cloud Environments

Optimized Predictive Storage Resource Allocation Through Neu...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Sridevi Ponmalar. P Adithya Pothan Raj V S D Sundarsingh Jebaseelan Gayatri Phade V. Srithar Harikrishnan. R Department of Computational Intelligence School of computing Kattankulathur Campus SRM Institute of Science and Technology Chennai Dept. of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Dept of EEE Sathyabama Institute of Science and Technology Chennai 119 Sandip Institute of Technology & Research Centre Nashik Department of Computer Applications PeriyarManiammai Institute of Science & Technology Thanjavur Symbiosis Institute of Technology Pune Campus Symbiosis International Deemed University Pune

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The demand for efficient and scalable management of storage resources is growing enormously with the rapid growth in data-intensive applications within the cloud environment. Optimized PSRANF is the state-of-the-art approach towards dynamic storage resource allocation, based on the forecast of future demands in storage. PSRANF utilizes state-of-the-art neural forecasting with Long Short-Term Memory (LSTM) networks and Transformer designs to analyze the pattern of historical storage usage for highly accurate future need predictions. By proactively adjusting storage capacity to these predictions, PSRANF strikes an optimal balance between performance and cost by reserving just-in-time resources. It ensures that there is no risk of over-provisioning, which would be too costly. In the same breath, the risk of under-provisioning, which leads to performance bottlenecks, is avoided. What is more, the system will apply prioritization, placing the data of higher priority or frequently accessed data into faster storage tiers of a higher class of performance and less important classes of data into cost-effective storage. The adaptive feedback loop continuously refines the forecasting model, which, over time, improves accuracy and allows the system to handle changes in workload patterns. PSRANF combines neural forecasting with resource allocation in such a way that is cost-effective; thus, it is very well placed for cloud environments where the demand for storage is dynamic in nature and requires intelligent data-driven approaches. The AWS implementation of PSRANF has turned out to be quite a helpful tool in optimizing the storage resource allocation process. While it is able to predict 98.5% of the storage needs correctly by achieving a cost saving of up to 18.7 % , managing data from 97.8% to 99.2%, and resources from 95.4%, it's robust for improvements in cloud storage management and operational efficiency.

关键词： Cloud computing Costs Accuracy Storage management Predictive models Dynamic scheduling Resource management Forecasting Optimization Long short term memory

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