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A system for secure collaborative computation and method thereof /
Patent Number: 202121003561, Applicant: Dr. Samiksha Shukla.
The present invention discloses a system for Secure Collaborative Computation and method thereof. The present invention discloses a computation system conjugated with a processor and, the processor is to: provide input data by one or more computer system using an input device and further recognizing the input data. -
A system for secure collaborative computation and method thereof /
Patent Number: 202121003561, Applicant: Dr. Samiksha Shukla.
The present invention discloses a system for Secure Collaborative Computation and method thereof. The present invention discloses a computation system conjugated with a processor and, the processor is to: provide input data by one or more computer system using an input device and further recognizing the input data. -
A system for simulation of collision resistant secure sum protocol and method thereof /
Patent Number: 202021055655, Applicant: Dr. Samiksha Shukla.
The present invention discloses a system for simulation of collision resistant secure sum protocol and method thereof. The present invention discloses a simulation apparatus, system and method thereof having a computation system conjugated with a processor and a Trusted Third Party (TTP) system provided on a computation server system, in which computing, by the Trusted Third Party (TTP) system having an initiator, and via the processor, for number of party, packets per party and anonymizers. -
A system for simulation of collision resistent secure sum protocol and method thereof /
Patent Number: 202021055655, Applicant: Dr. Samiksha Shukla.
The present invention discloses a system for simulation of collision resistant secure sum protocol and method thereof. The present invention discloses a simulation apparatus, system and method thereof having a computation system conjugated with a processor and a Trusted Third Party (TTP) system provided on a computation server system, in which computing, by the Trusted Third Party (TTP) system having an initiator, and via the processor, for number of party, packets per party and anonymizers. -
A system for water utility management and conservation in community households /
Patent Number: 202241035724, Applicant: Dr.Vivek S.
A control device for managing water flow and consumption is provided. The device include an overhead tank fill control module, a submerged pump module, a ground level pump module, a ground level storage tank fill module, an inlet flow meter module, an outlet flow module and a flow control valve module; wherein the central control device communicates with an overhead tank fill control device through the overhead tank fill control module, with a submerged pump or ground level pump through the submerged pump module or ground level pump module. -
A Systematic Approach for Enhancing the Curriculum Development based on the Gap Analysis to Meets the Standards of Accreditation
The article focuses on the identification of gaps in course outcomes and program outcomes in an outcome-based education. The identified gaps help in the redesigning of the curriculum as per the standard of accreditation, new education policy of India and industry-academia related gap. The gaps are identified on the targets and the attained value of course outcome of a particular course. The course outcomes are in turn related to program outcomes and on this basis, we could be able to identify the program outcomes which are not attained. This helps us in the formulation of new curriculum or redesigning of existing curriculum. In this research paper we will discuss how to calculate the attainment of any particular course, attainment of program outcomes, identification of gaps and the suggest the action plan to fill this gap through the revision of curriculum. 2026 IEEE. -
A Systematic Approach for Predicting Cybersecurity Attacks in IoT using CNN-LSTM with HABCABO
IoT has transformed how devices work together. Now, billions of connected devices may share data across smart homes, energy systems, and environmental monitoring. In Internet of Things ecosystems, rapid IoT expansion has made them very vulnerable, which makes them easy targets for cyberattacks. Hackers can break into IoT devices that don't have enough protection to stop services, steal data, and invade privacy. This paper shows how to use deep learning using CNNs and LSTM networks and the HABCABO optimization algorithm to deal with these new dangers. After careful sequencing, scaling, and noise reduction, filter-based feature selection uses statistical methods to keep the most important information. To get the best detection, the CNN-LSTM model is trained with features that are carefully regulated. The suggested model is more accurate than CNN and LSTM approaches, with an accuracy rate of 98.04 %. These results show that the model can find and stop IoT cybersecurity threats. In conclusion, CNN-LSTM and HABCABO are strong and smart ways to make sure that IoT infrastructure is safe and reliable right now. 2025 IEEE. -
A systematic literature network analysis approach to assess the topology of modern-era supply chain risk management research
Over the past decade, there has been a significant increase in research on supply chain risk management (SCRM). This review uses a systematic literature network analysis to provide an overview of the SCRM research landscape, with emphasis on optimisation approaches, mathematical modelling tools, and the identification of seminal studies and relevant keywords used in SCRM research. However, there are few quantitative models that represent the relationship between supply chain surplus, sustainability, and resilience in SCRM literature. The study has limitations since it only sources from a single database, and more clarity is needed on the effectiveness of optimisation in SCRM, which can be further evaluated through case studies and empirical studies. Copyright 2025 Inderscience Enterprises Ltd. -
A Systematic Literature Review on Image Preprocessing and Feature Extraction Techniques in Precision Agriculture
Revolutions in information technology have been helping agriculturists to increase the productivity of the cultivation. Many techniques exist for farming, but precision agriculture (PAg) is one technique that has gained popularity and has become a valuable tool for agriculture. Nowadays, farmers find it difficult to get expert advice regarding crops on time. As a solution, image processing techniques (IPTs) embedded PAg applications are developed to support farmers for the benefit of agriculture. In recent years, IPT has contributed a lot to provide a significant solution in PAg. This systematic review provides an understanding on preprocessing and feature extraction in PAg applications along with limitations. Preprocessing and feature extraction are the major steps of any application using IPTs. This study gives an overall view of the different preprocessing, feature extraction, and classification methods proposed by the researchers for PAg. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Systematic Literature Review Unveiling the Societal Impact of 5G/6G Technologies on Bridging the Digital Divide and Enabling Social Transformation
This study examines the multifaceted societal impact of 5G and 6G mobile communication technologies. Employing a systematic literature review (SLR) of recent scholarly literature (2022-2024), A comprehensive search strategy was implemented across reputable academic databases (Scopus, Web of Science, IEEE Xplore) to identify relevant publications. Selected articles underwent rigorous critical appraisal and thematic analysis to synthesize key findings and recurring themes. The review reveals the potential of 5G/6G to bridge the digital divide, particularly in underserved regions, through strategic infrastructure development. Social transformation can be achieved by fostering equitable access to communication services and deploying transformative applications in critical sectors like agriculture and healthcare. However, challenges persist regarding infrastructure limitations, equitable access, and sustainable development. Effective policy frameworks and robust managerial strategies for responsible deployment are crucial to maximize the positive impact of 5G/6G technologies. 2025 by IGI Global Scientific Publishing. All rights reserved. -
A SYSTEMATIC RESEARCH REVIEW AND META-ANALYSIS OF ENVIRONMENTAL SCIENCES AND MANAGEMENT MODELS
This research advances the comprehension of the processes behind individuals' environmentally friendly behavior using a comprehensive approach. A questionnaire addressed intrapersonal, motivational, relationships, and educational aspects, with environmental science as the primary catalyst for green behavior within a complete theoretical structure.The method is the CADMIACA approach, which is founded on Comprehensive Action Determining Modeling (CADM), together with various Motivational and Interpersonal (MI) theories and the Activity Competence Algorithm(ACA). This framework encompasses various control factors relevant to comprehensively characterizing the factors influencing environmentally friendly behavior, including climate change, energy conservation, recycling, sustainable buying, and contamination.The findings were gathered in the A Coru metropolitan region to experimentally evaluate the causal relationships among the parameters that formed the framework utilizing Structural Equation Modeling (SEM). Findings show that environmentalscience serves as an effective instrument for fostering eco-friendly behavior among residents. The extensive CADMIACA model aligns well with the information since all components incorporated in the framework (intrapersonal, inspiring, social, and institutional) are pivotal in shaping green conduct.Environmental instruction and intrapersonal variables emerged as the primary predictors of green conduct, but social and motivational variables were less prevalent in influencing such behavior. The findings suggest that human conduct plays a vital role in environmental protection. 2025, Rotherham Academic Press Ltd. All rights reserved. -
A Systematic Review and Meta-Analysis of Pneumonia Diagnosis Using Machine Learning Techniques
Pneumonia is an infection that results in inflammation of the lungs and, if not identified in time, can be life-threatening. The most frequent method of diagnosing pneumonia is chest X-rays; the pictures are scrutinized closely. Pneumonia is still a global health burden. The accurate and timely diagnosis is difficult, especially in low-resource settings. X-rays have served as a primary key for the identification of pneumonia for many years. However, with the recent advancement in artificial intelligence technologies, especially deep learning and machine learning, there's now a potential to automatically detect and classify pneumonia using chest x-ray images. This review examines the research from 2019 to 2024 to understand the current trends and future direction in various deep learning and machine learning models. These encompass the convolutional neural networks, transfer learning methods, combined network designs, explainable AI models, and the use of radiomics with conventional machine learning techniques. However, the three significant challenges remain differences in the data, an imbalance between the classes, and a limited ability to apply these methods in real clinical settings. Based on the review, this paper suggests more future research on machine learning techniques for detecting pneumonia. In this work, a new system is also introduced to improve both case identification and the clinical diagnosis process. The proposed model was evaluated using the Key Parameter Indicator (KPI) as a feature and was compared with an earlier model. Finally, recommendations are provided for future research on trustworthiness, clinical usefulness, and multi-modal AI systems. 2025 IEEE. -
A Systematic Review and Modern Approaches for Bio Signal Based BCI's
Bio-signals play a very critical role in modern medicine, especially for paralyzed patients. The development of Brain-Computer Interface (BCI) systems, which allows direct brain-to-external device contact, is made possible by these signals, creating new ways for medical intervention and rehabilitation. Using these bio-signals, modern medicine has made great strides toward developing intelligent devices that enhance the quality of life for people who are paralyzed. These include improved mobility, supported communication devices, and environmental control. In this survey, we thoroughly assess the latest BCI-based smart devices and their medical applications. By finding and examining current methods, technologies, and practical uses, we seek to showcase the effectiveness and potential of brain-computer interfaces (BCIs) in providing new ways to treat paralysis. Currently, bio-signal-based control systems have been continuously used rapidly in biomedical devices and assistive robots to enhance and improve the quality of life for disabled and elderly individuals. Among these, electromyograph (EMG), electroencephalography (EEG), and electrooculography (EOG) bio-signals are highly used for improving modern technologies. The primary objectives of this paper are to detail the techniques used in Brain-computer interface-based smart devices and their applications, used in healthcare. Additionally, this paper provides an overview of applications controlled through these bio-signals and discusses the research challenges in developing these control systems. 2025 IEEE. -
A Systematic Review of AI Privileges to Combat Widen Threat of Flavivirus
In order to prevent the extraordinary spread of sickness caused by Flavivirus, the healthcare business as well as public health are working tirelessly. Individual lives have been affected, but mosquito-infested public locations have made a considerable influence on the general publics health. Site adaptability, climate change, and inadequate healthcare services and surveillance all contribute to the spread of the virus. The potential dangers of this virus, on the other hand, have been uncovered through extensive and ongoing research in the healthcare business. Modern healthcare facilities may benefit from the reasoning capabilities and ever-evolving analysis techniques provided by artificial intelligence. More conclusive findings have been demonstrated in the realm of AI applications in healthcare domains such as cancer, neurology, and cardiology. A number of research works have justified the use of AI-oriented algorithms for intelligently handling unstructured and huge healthcare data. When it comes to using artificial intelligence (AI) to identify, forecast, diagnose, and treat disease using data from public health and biological databases, the current effort aims to undertake an extensive examination. There may be issues in integrating assistive technology into the current healthcare system, as well. Because of this review, we hope that by merging AI research with clinical and public health specialists, critical knowledge may be extracted from data in order to unchain the relevant information of Flavivirus disease from its chains. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Systematic Review of Challenges and Techniques of Privacy-Preserving Machine Learning
Machine learning (ML) techniques are the backbone of Prediction and Recommendation systems, widely used across banking, medicine, and finance domains. ML techniques effectiveness depends mainly on the amount, distribution, and variety of training data that requires varied participants to contribute data. However, its challenging to combine data from multiple sources due to privacy and security concerns, competitive advantages, and data sovereignty. Therefore, ML techniques must preserve privacy when they aggregate, train, and eventually serve inferences. This survey establishes the meaning of privacy in ML, classifies current privacy threats, and describes state-of-the-art mitigation techniques named Privacy-Preserving Machine Learning (PPML) techniques. The paper compares existing PPML techniques based on relevant parameters, thereby presenting gaps in the existing literature and proposing probable future research drifts. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Systematic Review of Challenges, Tools, and Myths of Big Data Ingestion
Each sector of the digital world generates enormous data as human life continues to transform. Areas like data analytics, data science, knowledge discovery in databases (KDD), machine learning, and artificial intelligence depend on highly distributed data which requires appropriate storage in a data lake. Collecting the data from different heterogeneous sources and creating a single lake of data is called data ingestion. Ironically, data ingestion has been treated as a less important stage in data analysis because it is considered a minor first step. There are several misconceptions in the data and analytics domain about data ingestion. The survey employed in this research presents a list of significant challenges faced by information technology (IT) industries during data ingestion. The available frameworks are compared in terms of standard parameters that are set against the existing challenges and myths. The findings from the comparison are compiled in a tabular format for easy reference. The paper places emphasis on the significance of data ingestion and attempts to present it as a major activity on the big data platform. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Systematic Review of Fish-Based Biomaterial on Wound Healing and Anti-Inflammatory Processes
Objective: To conduct a systematic literature review to study the effects of fish-based biomaterials on wound healing in both in vivo and in vitro animal models. Approach: This review covers the study reported in different articles between 2016 and August 2022 concentrating mainly on the cytotoxicity evaluation of different fish-based biomaterials on inflammation, reepithelialization and wound healing. Significance: This review shows considerable amount of research work carried out with fish-based biomaterials and collagen for treating burn wounds. Surprisingly there are only a few commercial products developed so far in this particular regard for surgical purpose and therefore, there is a way out and need for developing medical support product from fish-based biomaterials to treat and cure wounds. Recent Advances: Three-dimensional skin bioprinting technique is a large-scale solution for severe burn wounds that requires collagen as a raw material for printing, wherein fish collagen can be used in place of bovine and porcine, as it is biocompatible, promotes cell proliferation, adhesion, and migration, and degrades enzymatically. In the recent times, there are a few fish-based surgical products that have been formulated by Kerecis in United States. Critical Issues: The different fish-based biomaterial products are all mere supplements taken in orally as food or supplements till date and there is no proper proven medications that has been formulated so far in the field of wound healing and inflammation based on fish biomaterials except the surgical products that can be finger counted. Future Directions: Fish-based biomaterials are known for the medicinal properties that are used throughout the world and further investigations should be carried out to understand the actual physiochemical properties of its derivatives for the discovery of novel products and drugs. Copyright 2024 by Mary Ann Liebert, Inc. -
A Systematic Review of Green Apparel Manufacturing
The purpose of this paper is to conduct a systematic review of the literature on green manufacturing practices in the apparel industry to map green practices across various apparel manufacturing departments. The review includes academic journal articles that were retrieved between March 2013 and March 2023 from several different databases. As part of a comprehensive literature assessment, content analysis was applied to 138 publications that were published in peer-reviewed journals over ten years. Green practices in garment manufacturing process are covered, including product design, raw material procurement, fabric spreading, cutting, sewing and assembly, washing, printing and embroidery, finishing, and packing. The review of eco-friendly production practices at each phase of the production process shows the variety and complexity of green practices in apparel production companies. However, there is a lack of research on the conditions of developing countries, where the majority of apparel production takes place, as well as on the methods used in the manufacture of garments. The study is distinct in that it focuses solely on the garment manufacturing industry, and will not include textiles because the production processes for textiles and clothing are fundamentally different. This study assists managers in building a companys sustainability competency by outlining best practices at various phases of production. It also provides scholars with a uniform representation of environmentally sustainable practices to spur additional scholarly investigation. 2023, Kauno Technologijos Universitetas. All rights reserved. -
A Systematic Review of Mental Health and Productivity Challenges and Navigating Turbulence in Academia
This study examines academic mental health and productivity through a systematic and thematic literature review of research published from 2019 to 2025, with a focus on the impact of COVID-19. Searches in Scopus, Ebsco, ProQuest, Science Direct, and Google Scholar identified key challenges, including stress, burnout, and work-life imbalances, which were exacerbated by the pandemic. Systemic barriers such as inadequate funding, fragmented support, and rigid institutional policies further disrupted academic productivity. Vulnerable groups, including academic mothers, doctoral candidates, minority students, and early career researchers, faced disproportionate burdens. Additionally, academic writing productivity declined, and excessive media use negatively impacted attention spans and mental health. While interventions such as peer support systems, flexible work policies, and co-designed mental health initiatives have been proposed, existing approaches remain fragmented and insufficient for long-term solutions and targeted interventions to improve academic mental health and productivity. 2026 by IGI Global Scientific Publishing. All rights reserved. -
A Systematic Review of Various Advancements Implementation in the Field of Crop (Plant) Production
An essential component of agricultural output is pest management, especially in fertigation-based farming. Although fertigation systems in Malaysia are beneficial for irrigation and fertilization, they frequently don't have effective pest control techniques. Because pests usually live beneath crop leaves, hand spraying is difficult and labor-intensive. Insect pests have the power to seriously harm, weaken, or even kill agricultural plants, which can lead to lower yields, worse-quality goods, and unsalable outcomes. Furthermore, insects may still cause harm to processed or stored items after harvest. Therefore, creating an autonomous pesticide sprayer specifically designed for chilli fertigation systems is the main goal of this research. The main goal is to create a sprayer arm that is flexible enough to reach under crop leaves. The goal of this project is to build an autonomous, unmanned pesticide sprayer. The goal of autonomous operation is to reduce the amount of dangerous pesticides that people are exposed to, especially in enclosed spaces like greenhouses. In addition, the sprayer arm's adaptability to different agricultural circumstances makes it a valuable tool in both greenhouse and outdoor settings. It is expected that the successful adoption of the autonomous pesticide sprayer would completely transform fertigation-based farming's approach to pest management. 2024 IEEE.