Chaotic Function Based Data Hiding Approach at Least Significant Bit Positions
Authors: Dilip Kumar Mishra, P.G.Scholar, Sriram Yadav, A.P. & Head
Abstract: With the increase in the digital media transfer and modification of image is very easy. So one major issue of proprietorship is raised, as copying and transferring is very soft practice. Here this paper has resolve proprietorship problem by embedding the digital data with encryption. In this work embedding of data is done by applying the Arnold’s Cat Map algorithm for randomization of pixel values. Then robustness is provided by using the AES algorithm. Finally using spatial technique embedding of digital data is done in encrypted image. Embedding in LSB portion of the pixel this research work is robust against various attacks. Experiment is done on real data-set image. Evaluation parameter values shows that this research work has maintain the SNR, PSNR values with high robustness of the data.
A Survey on: Social Feature Based Service Rating Prediction Techniques
Authors: Madhu Rajput, P.G.Scholar, Amit Thakur, A.P.
Abstract: The Internet has made it possible to discover opinions of others on a wide range of subjects, through social media websites, such as review sites, wikis, and through online social networks. Some of website provide user rating for different product or services but they do not recommend any user to purchase. This paper focus on elaborating the user rating behavior of particular kind of services. Here techniques developed by various researchers are discussed with there requirements. In social network some inherent features are also detailed with the help of which prediction percentage may be increase.
A Survey on Different Features and Techniques for Web Service Prediction
Authors: Sonika Baisakhiya, P.G.Scholar, A.P. Jayshree Boaddh, Prof. Durgesh Wadbude.
Abstract: – Quality of Service (QoS) assurance is an important factor of service recommendation. The web services which are never been used before by users have some indefinite QoS values for that service, and hence the accurate prediction of indefinite QoS values is important for the successful consumption of Web service-dependent applications. Collaborative filtering is the technique which is broadly accepted in the prediction of indefinite QoS values as it is significant for predicting missing values. Though, collaborative filtering derived from the processing of subjective data. In this paper, we describe various collaborative filtering by QoS rating techniques applied to web service mining and addresses various collaborative filtering problems.
Internet of Things Based Smart Home Automation
Authors: Harshal S.Bhosale, Mrs. V. RPalundarkar, Priyesh S. Surve, Rahul B. Biswas
Abstract: – Home Automation is conveniences installed and designed to perform chore in your living place. Smart homes are often referred to as intelligent homes as they perform services that become part of our life. Many of the automated systems that silently perform their jobs unnoticed this is automation at its best. We live in an exciting time where more and more everyday items “things” are becoming smart! “Things” have sensors and can communicate to other “things” and can provide control to more “things”. The Internet of Things, IoT, is upon us in a huge way and people are rapidly inventing new gadgets that enhance our lives. The price of micro controllers with the ability to talk over a network keeps dropping and developers can now tinker and build things inexpensively. IoT based home automation can be achieved by using low cost ESP8266 ESPino ESP-12 WiFi Module, AVR Controller and Relay’s.
Review Article of Basic ADC Design and Issue of Old Algorithm
Authors: M. Tech. Madhusudan Singh Solanki, Associate Prof. Priyanshu Pandey
Abstract: –Analog to-digital converters (ADCs) are key design blocks and are at present embraced in numerous application fields to enhance computerized frameworks, which accomplish better exhibitions with regard the analog arrangements. With the quick progression of CMOS manufacture innovation, more signal processing capacities are actualized in the computerized space for a lower cost, bring down power utilization, higher yield, and higher re-configurability. Across the board use gives awesome significance to the design exercises, with these days to a great extent adds to the generation cost in coordinated circuit gadgets. This has as of late generated an extraordinary interest for low-control, low-voltage ADCs that can be acknowledged in a standard deep submicron CMOS innovation. Different cases of ADC applications can be found in information securing frameworks, estimation frameworks and digital correspondence frameworks likewise imaging, instrumentation frameworks. Subsequently, this work need to considered every one of the parameters and enhancing the related execution may fundamentally decrease the modern cost of an ADC producing process and enhanced the determination and configuration extraordinarily control utilization . This paper displays a 4 bit Pipeline ADC with low power dissipation executed in <0.18μm CMOS innovation with a power supply of 1.2V.
A Survey on Various Techniques and Characteristics of Text Document Fetching
Authors: M. Tech. Scholar Rajeev Kumar, Prof. Durgesh Wadbude, A.P. Jayshree Boaddh
Abstract: –Search engines are the major breakthrough on the web for retrieving the information. But List of retrieved documents contains a high percentage of duplicated and near document result. So there is the need to improve the performance of search results. In this paper text document retrieval study is done with various techniques of fetching with there implementations. Here different features for the text document retrieval is explained in detailed with there requirements as feature vary as per text analysis. Paper has brief different evaluation parameters for the study and comparison of relevant documents techniques.
An Rob-frugal Based Data Distribution Approach By Utilizing Frequent Pattern Tree Rules
Authors: M. Tech. Scholar Ankit Saini, A.P. Jayshree Boaddh, Prof. Durgesh Wadbude
Abstract: –– Information sharing among the organizations is a general movement in a few zones like business advancement and showcasing. As portion of the sensitive rules that should be kept private might be revealed and such revelation of sensitive patterns may impacts the benefits of the organization that possess the information. Consequently the principles which are sensitive must be covered before sharing the information. In this paper to provide secure data sharing sensitive rules are perturbed first which was found by frequent pattern tree. Here sensitive set of rules are perturbed by substitution with rob-frugal mixing. This type of substitution reduces the risk and increase the utility of the dataset as compared to other methods. Analysis is done on genuine dataset. Results demonstrates that proposed work is better as contrast with different past methodologies on the premise of assessment parameters.
A Implementation of High Speed On-Chip Monitoring Circuit By Using SAR Based ADC Design
Authors: M. Tech. Madhusudan Singh Solanki, Associate Prof. Priyanshu Pandey
Abstract: –– An 16-bit pipelined analog-to digital converter (ADC) is designed in this thesis. The pipelined architecture realizes the high-speed and high-resolution. To reduce some complexities of flash ADC pipeline ADC is used. The calibration schemes of pipelined ADC limit absolute and relative accuracy. Deviations in residue amplifier gain results due to low intrinsic gain of transistors, and mismatching between all the capacitors of capacitance 1pF result in both deviations in residue amplifier gain and DAC nonlinearity in a pipelined ADC. To image the world, a low-power CMOS image sensor array is required in the vision processor. The image sensor array is typically formed through photo diodes and analog to digital converter (ADC). To achieve low power acquisition, a low-power mid-resolution ADC is necessary. Digital correction allows also to use very low power dynamic comparators. The multiplying D/A converters (MDACs) utilize a modified folded dynamic amplifier .In this enables to the use of dynamic amplifiers in a pipeline ADC. In addition, the dynamic amplifier offers both clock scalability and high-speed operation even with a scaled supply voltage. Using the above techniques, a 16 bit prototype ADC achieves a conversion rate of 160 MS/s with a supply voltage of 0.55 V. Therefore, the combination of interpolated pipeline architecture and dynamic residue amplifiers demonstrates the feasibility of ultra-low voltage high-speed analog circuit design. To implement the whole system a low-power and small size capacitance value sensing readout circuit is required. Also, it has to be integrated together with the back-end low-power current-mode ADC on the same chip. The low-power current-mode ADC has been designed and fabricated with TSMC 0.18um CMOS technology. In the simulation result, the power consumption for 6-bit ADC was 6 W, with a power supply of 0.65 V.
The Impact Of AI-based Workload Schedulers On Energy-efficient Data Centers
Authors: Arjun Prasad
Abstract: Artificial intelligence (AI) has emerged as a transformative force across numerous technological domains, with its impact acutely felt in the design and operation of modern data centers. As the demand for cloud services, big data analytics, and internet-based applications surges, data centers have grown exponentially in size and complexity, concurrently escalating their energy consumption. Addressing energy efficiency within these large-scale computing infrastructures is paramount not only from an operational cost perspective but also for environmental sustainability. AI-based workload schedulers have been increasingly adopted as innovative solutions to optimize resource utilization and curtail energy wastage. These intelligent schedulers leverage machine learning algorithms, predictive analytics, and real-time monitoring to dynamically allocate workloads based on energy profiles, cooling capacities, and computing requirements. The integration of AI fosters adaptive scheduling strategies that can respond to fluctuating workloads, minimize idle hardware, and optimize server usage, thereby enhancing energy efficiency. This article comprehensively explores the multifaceted impact of AI-driven workload scheduling on the operation of energy-efficient data centers. It delves into state-of-the-art AI scheduling techniques, mechanisms for workload prediction, energy consumption modeling, and the synergies between hardware infrastructure and intelligent scheduling systems. Furthermore, the article discusses challenges such as scalability, algorithmic complexity, and integration with existing data center management frameworks. By synthesizing contemporary research findings and industry practices, this work aims to provide a detailed understanding of how AI can revolutionize energy management in data centers, ultimately contributing to reduced carbon footprints and sustainable growth in the digital era.
DOI: http://doi.org/10.5281/zenodo.17777795
The Impact Of AI On Predictive Performance Tuning In Cloud Computing Environments
Authors: Ashok Kumar
Abstract: Artificial Intelligence (AI) has revolutionized predictive performance tuning in cloud computing environments, offering significant advancements in resource allocation, fault detection, and autonomic optimization. In an era marked by increasing computational complexity, unpredictable traffic patterns, and heightened demands for availability, integrating AI into cloud operations enables proactive identification and mitigation of latency, bottlenecks, and system inefficiencies. This abstract provides a concise overview of how AI-driven techniques—such as machine learning models, deep neural networks, and reinforcement learning algorithms—have become indispensable for predictive analytics, facilitating dynamic resource scaling, workload balancing, and anomaly detection. AI systems leverage vast datasets generated by cloud infrastructures to uncover hidden patterns, optimize service level agreements (SLAs), and deliver high-performance computing with reduced costs and improved reliability. Challenges remain, especially regarding model interpretability, real-time adaptability, and ethical deployment. Nevertheless, the synergistic evolution of AI and cloud computing stands poised to redefine best practices in predictive performance tuning, fostering new paradigms of automation, resilience, and intelligence in the digital ecosystem.
DOI: http://doi.org/10.5281/zenodo.17777827
The Role Of AI And Automation In Adaptive Unix And Linux System Governance
Authors: Ramesh L. Subedi
Abstract: The integration of Artificial Intelligence (AI) and automation into Unix and Linux system governance has revolutionized how system administrators manage, monitor, and optimize infrastructure. These traditionally command-driven systems are now empowered with intelligent tools capable of adaptive learning, self-optimization, and proactive management. AI enhances performance monitoring, predictive maintenance, anomaly detection, and resource allocation, while automation streamlines repetitive administrative tasks, reducing human error and improving efficiency. Together, they establish a self-sustaining governance model that adapts dynamically to workloads and evolving cybersecurity challenges. This review explores the foundational concepts of AI integration, automation frameworks, and adaptive governance mechanisms within Unix and Linux environments. Furthermore, it examines their impact on performance, scalability, and compliance, alongside discussing real-world implementations and future perspectives. As enterprises shift towards AI-driven operations, the adaptive governance of Unix and Linux systems emerges as a vital frontier, blending intelligence with reliability to build resilient, autonomous computing infrastructures.
DOI: http://doi.org/10.5281/zenodo.17840389
Architectural And Workload-Driven Optimization Of SQL Server For High-Performance Enterprise Systems
Authors: Hema Latha Boddupally
Abstract: Enterprise applications increasingly depend on relational database systems to process large volumes of both transactional and analytical workloads while meeting stringent performance, scalability, and availability requirements. Microsoft SQL Server, widely adopted across industries such as finance, healthcare, retail, and manufacturing, provides a comprehensive set of optimization mechanisms that span query processing, cost-based optimization, indexing strategies, storage and I/O layout, and automated tuning facilities. Despite the maturity of these capabilities, many enterprise deployments suffer from suboptimal configurations, poorly designed schemas, stale statistics, and ineffective maintenance practices, which collectively lead to latency bottlenecks, excessive disk and memory utilization, and limited scalability under peak loads. This article presents a systematic study of SQL Server optimization techniques for high-performance enterprise workloads, focusing on core areas including query execution architecture, index and statistics design, table partitioning strategies, and operational maintenance practices such as monitoring and automation. By synthesizing foundational academic research, Microsoft technical whitepapers, and widely adopted industry case studies published between 2000 and 2016, the paper distills practical, experience-driven guidance for designing, tuning, and sustaining SQL Server systems that can reliably operate under high concurrency, large data volumes, and evolving business demands.
DOI: http://doi.org/10.5281/zenodo.18042490