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Improved Energy Management Strategy for Prosumer Buildings with Renewable Energy Sources and Battery Energy Storage Systems

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 381-392页

作者： Pavitra Sharma Krishna Kumar Saini Hitesh Datt Mathur Puneet Mishra Department of Electrical&Electronics Engineering Birla Institute of Technology&SciencePilaniIndia IEEE

The concept of utilizing microgrids(MGs)to convert buildings into prosumers is gaining massive popularity because of its economic and environmental *** pro-sumer buildings consist of renewable energy sources and usually install battery energy storage systems(BESSs)to deal with the uncertain nature of renewable energy ***,because of the high capital investment of BESS and the limitation of available energy,there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of *** this regard,this paper proposes an improved energy management strategy(IEMS)for the prosumer building to minimize the operating cost of MG and degradation factor of ***,to estimate the practical operating life span of BESS,this paper utilizes a non-linear battery degradation *** addition,a flexible load shifting(FLS)scheme is also developed and integrated into the proposed strategy to further improve its *** proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic(PV)and BESS-powered AC-DC hybrid MG installed at a commercial ***,the scenario reduction technique is used to handle the uncertainty associated with generation and load *** validate the performance of the proposed strategy,the results of IEMS are compared with the well-established energy management *** simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of ***,FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS,thus making the operation of prosumer building more economical and efficient.

关键词： Prosumer building battery energy storage system(BESS) battery degradation factor demand response energy management microgrid solar photovoltaic(PV)system

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Bio-chelation for sustainable heavy metal remediation in municipal solid waste compost: A critical review of chelation technologies

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Environmental science and Pollution Research 2025年 1-25页

作者： Khare, Srishti Singhal, Anupam Rallapalli, Srinivas Mishra, Anant Department of Civil Engineering Birla Institute of Technology and Science Rajasthan Pilani India

Municipal solid waste (MSW) compost is a promising solution for sustainable urban waste management, widely used as a soil amendment and for carbon sequestration. However, heavy metals in MSW compost pose risks to ecosystems, food safety and human health. This review critically examines three decades of research (1994–2024) on heavy metal contamination in MSW compost and household hazardous waste (HHW), identifying gaps in managing these pollutants, particularly regarding hazardous waste co-disposal. It evaluates existing remediation strategies for heavy metal removal, with a focus on chemical-assisted leaching using chelating agents. Key treatment parameters—such as chelating agent concentration, pH, contact time, liquid/solid ratio, temperature and flow rate—are analysed in both batch and continuous modes. The study advocates for biodegradable chelating agents as an effective approach to enhancing MSW compost quality, with applications in landfill reclamation and agriculture. Emphasizing the need for eco-friendly heavy metal mitigation, the review underscores the importance of safe urban composting practices. The findings contribute to the circular economy and Sustainable Development Goals by promoting sustainable and safe MSW compost applications, fostering environmental protection and public health and guiding research and industry toward scalable, marketable remediation solutions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Municipal solid waste

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A threefold chaotic image encryption technique for efficacious image data protection

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International Journal of Computers and Applications 2024年第11期46卷 974-982页

作者： Anwar, Shamama Srivastava, Anshuman Department of Computer Science and Engineering Birla Institute of Technology Ranchi India

With the profound use of digital contents in education and social media, multimedia content have become a prevalent means of communication and with such rapid increase, information security is still a major concern. This paper provides a summary of the techniques of securing image data focusing primarily on encryption. This paper additionally also explores the contribution of chaos theory in the domain of image encryption and applies a threefold chaotic map sequence to design an image encryption technique. This chaos-based encryption technique renders characteristics like sensitivity, determinacy, and ergodicity, and hence provides a better trade-off between security and computational speed. The efficacy of proposed algorithm is also evaluated based on well-known statistical metrics like Number of Changing Pixel Rate (NPCR), Unified Average Changing Intensity (UACI), Information Entropy, local Entropy, Correlation analysis, key sensitivity analysis, and NIST Randomness test. Furthermore, the key sensitivity analysis is also discussed which is a major part of any encryption algorithm analysis. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Sensitivity analysis

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Hyperspectral image restoration using noise gradient and dual priors under mixed noise conditions

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CAAI Transactions on Intelligence technology 2025年第1期10卷 72-93页

作者： Hazique Aetesam Suman Kumar Maji V.B.Surya Prasath Computer Science and Engineering Birla Institute of Technology MesraBiharIndia Computer Science and Engineering Indian Institute of Technology PatnaBiharIndia Department of Computer Science University of CincinnatiCincinnatiOhioUSA

Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral *** in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and *** HSI restoration method for such a mixed noise model is ***,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise ***,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image ***,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is *** the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)*** the best of authors'knowledge,this joint optimisation framework is the first attempt in this *** handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is ***,an efficient split-Bregman approach is used to solve the resulting optimisation *** results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.

关键词： hyper-laplacian prior hyperspectral images image restoration mixed noise variational approach

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Machine and deep learning techniques for the prediction of diabetics: a review

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Multimedia Tools and Applications 2025年第18期84卷 19425-19549页

作者： Modak, Sandip Kumar Singh Jha, Vijay Kumar Department of Computer Science & Engineering Sarala Birla University Jharkhand Ranchi India Department of Computer Science & Engineering Birla Institute of Technology Mesra Ranchi India

Diabetes has become one of the significant reasons for public sickness and death in worldwide. By 2019, diabetes had affected more than 463 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 700 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multi factorial metabolic disease, its diagnostic criteria are difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of the medical diagnosis process. With the development of data mining, researchers find that machine learning and deep learning, playing an important role in diabetes prediction research. This paper is an in-depth study on the application of machine learning and deep learning techniques in the prediction of diabetics. In addition, this paper also discusses the different methodology used in machine and deep learning for prediction of diabetics since last two decades and examines the methods used, to explore their successes and failure. This review would help researchers and practitioners understand the current state-of-the-art methods and identify gaps in the literature. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： data mining

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Bayesian Optimized Random Forest Classifier for Improved Credit Card Fraud Detection: Overcoming Challenges and Limitations 8th

Bayesian Optimized Random Forest Classifier for Improved Cre...

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8th International Conference on Computers, Management and Mathematical sciences, ICCM 2022

作者： Rajesh, P.K. Shreyanth, S. Sarveshwaran, R. Nithin Chary, V. MTech in Data Science and Engineering Birla Institute of Technology and Science Rajasthan Pilani India

ISBN: (纸本)9783031511660

In the financial sector, credit card fraud is a common issue that costs both people and organizations a lot of money. The ability of machine learning algorithms to automatically identify patterns and anomalies from big datasets has made them a common method for fraud detection. In order to increase the detection of credit card fraud, we suggest an improved Bayesian random forest classifier in this article. By using Bayesian optimization to choose the ideal hyperparameters for the model, we handle the difficulties and restrictions of conventional random forest classifiers. Using an openly accessible dataset on credit card fraud, the proposed method is assessed and contrasted with other cutting-edge approaches. By obtaining an accuracy of 99.6% and an area under the curve (AUC) of 0.99, our findings demonstrate that the suggested optimized Bayesian random forest classifier outperforms conventional random forest and other benchmark methods. We also examine the significance of features in fraud identification in order to show the model’s interpretability. Finally, we discuss the suggested strategy’s drawbacks and potential future developments. Our study aids in the creation of reliable and effective fraud detection tools for the finance sector. The suggested approach can be used as a decision support tool for fraud analysts and detectives and can be applied to a variety of fraud detection issues beyond credit card fraud. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： data mining

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Network Security and Cryptography: Threats, Obstacles and Solutions-A Bibliometric Analysis

Recent Advances in Computer Science and Communications

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Recent Advances in Computer science and Communications 2025年第2期18卷 83-101页

作者： Singh, Purushottam Dutta, Sandip Pranav, Prashant Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi835215 India

Background: In the wake of escalating cyber threats and the indispensability of ro-bust network security mechanisms, it becomes crucial to understand the evolving landscape of cryptographic research. Recognizing the significant contributions and discerning emerging trends can guide future strategies and technological advancements. Our study endeavors to shed light on this through a bibliometric analysis of publications in the realms of Network Security and Cryptography. Method: To chronicle and synthesize the progression of research methodologies from their in-ception to the present day, we undertook a comprehensive Bibliometric Analysis of Network Security and Cryptography. Our data set was culled from the Clarivate Analytics Web of science database, encompassing 3,897 papers, 603 sources, and 7,886 authors from across the globe. Results: Our analysis revealed a marked upsurge in cryptographic research since 1992, with China standing out as a dominant contributor in terms of publications. Notably, while 'security' and 'cryptography' emerged as recurrent research themes, there's an observable downtrend in international collaborations. Our study also highlights pivotal topics shaping the network security domain, offering insights into the trajectories of research source growth, structural variabil-ities in research relevance, and prospective intellectual and collaborative avenues as guided by authorship patterns. Conclusion: Cryptographic research is on an upward trajectory, both in volume and signifi-cance. However, the tapering of international collaborations and an evident need to concentrate on emergent challenges, such as data privacy and innovative network attacks, emerge as nota-ble insights. This bibliometric review serves as a compass, directing researchers and academi-cians towards areas warranting heightened attention, thereby informing the roadmap for future investigative pursuits. © 2025 Bentham science Publishers.

关键词： Cyber attacks

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Convergence of various computer-aided systems for breast tumor diagnosis: a comparative insight

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Multimedia Tools and Applications 2025年第16期84卷 16709-16756页

作者： Singh, Saket Kumar Patnaik, K. Sridhar Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi835215 India

Breast Cancer, with an expected 42,780 deaths in the US alone in 2024, is one of the most prevalent types of cancer. The death toll due to breast cancer would be very high if it were to be totaled up globally. Early detection of breast cancer is the only way to decrease the mortality caused by it. In order to diagnose breast cancer, even the most competent and qualified pathologists and radiologists have to examine hundreds of high-resolution images, which is a massive burden on them. Compared to the number of cases, very few experts are available to manage this burden. Additionally, as humans are more prone to mistakes, the likelihood of finding false positive cases is also high. Numerous AI techniques, including machine learning and deep learning, are ideally suited to address these issues, inspiring many researchers to introduce novel computer-aided detection systems. In this study, we have comprehensively reviewed pre-existing literature aimed at developing computer-aided systems based on using machine learning, deep learning, and vision transformers to identify and classify breast cancer. We have discussed numerous imaging modalities for detecting breast cancer, along with the widely used data pre-processing approaches, machine learning and deep learning models, as well as ensemble learning methods suitable for the task. Popular datasets and their sources are also listed for future referencing. Finally, we have identified a few gaps and addressed potential future research directions with an intent of aiding researchers select approaches tailored to case-specific needs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Computer aided diagnosis

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A secure and privacy preserving model for healthcare applications based on blockchain-layered architecture

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International Journal of Computers and Applications 2024年第12期46卷 1206-1218页

作者： Sanober, Adla Anwar, Shamama Department of Computer Science and Engineering Birla Institute of Technology Ranchi Mesra India

Blockchain is one of the emerging technologies that are applied in various fields and its application in Healthcare 4.0 is crucial to handle the vast amount of health records that are growing continuously everyday. This paper explores the application of blockchain technology-based layered architecture integrated with Interplanetary File System (IPFS) storage to provide a secure and robust sharing platform for healthcare system. A multilayer security is provided in the proposed system including a 128-bit Random Deoxyribonucleic Acid (DNA) encoding sequence with a modified version of the 128-bit Advanced Encryption Standard (AES) key expansion symmetric key technique is being used before storing the records in the IPFS storage, Rivest–Shamir–Adleman (RSA) 2048 digital envelope is used for secure transmission of symmetric keys, RSA 1024 is used for digital signature, and Secure Hash Algorithm 256-bit (SHA-256) is used for hashing the record files. Comparative analysis with existing state-of-the-art in blockchain-based E-healthcare system is discussed. The performance of various assessment metrics is evaluated, including encryption and decryption time for EHR records, mining time, record retrieval time, smart contract execution time and network latency. Additionally, a reputation-based filtering mechanism specifically designed in the proposed model to detect and mitigate the various attacks. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Electronic health record

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Design and Enhancing Performance of Lead-Free Flexible Perovskite Solar Cells: A Numerical Approach

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第3期4卷 109-116页

作者： Grover, Akshit Tejaswi, Gopalchetty Neeraj Gupta, Navneet Birla Institute of Technology and Science Department of Electrical and Electronics Engineering Rajasthan Pilani333031 India

Lead-free perovskite materials have emerged as the most feasible option for photovoltaics to address the issue of toxicity and better performance. This study aims to design and enhance the performance of lead-free methylammonium (MA) and formamidinium (FA) compound (FAMASnGeI3)-based flexible perovskite solar cell (FPSC) by utilizing the solar cell capacitance simulator 1-D (SCAPS-1D). The optimization of various geometrical parameters and charge densities of the absorber and charge transport layers show the improvement in power conversion efficiency (PCE). We have obtained Voc of 0.87V, Jsc of 45.7 mA/cm2, fill factor (FF) of 75.3%, and PCE of 29.9%. The obtained results were compared with similar existing research based on various absorber layers. It was found that the selected absorber layer improves the performance of FPSCs. © 2022 IEEE.

关键词： Perovskite

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