AIJREAS VOLUME 8, ISSUE 10 (2023, OCT)aerfpublications2024-02-01T09:53:52+00:00
AIJREAS VOLUME 8, ISSUE 10 (2023, OCT) (ISSN-2455-6300) ONLINE
ANVESHANA’S INTERNATIONAL JOURNAL OF RESEARCH IN ENGINEERING AND APPLIED SCIENCES
1.
CONTEMPLATIONS ON EQUITY IN AI FOR INDIVIDUALS WITH DISABILITIES
Mannepuli Srujana & Dr. Vijay Pal Singh
Page 1-15
 | Paper TitleCONTEMPLATIONS ON EQUITY IN AI FOR INDIVIDUALS WITH DISABILITIESAbstractPeople with disabilities endure prejudice today. As
artificial intelligence solutions become more
important in decision-making and interaction, they
may positively or adversely effect the treatment of
individuals with disabilities in society. In a session
with participants with various impairments, we
discuss potential and hazards in four emerging AI
application areas: job, education, public safety,
and healthcare. In many cases, non-AI solutions
are already discriminatory, and adding AI risks
perpetuating these faults. We then address
disability-related fairness techniques across the AI
development lifecycle. AI systems\' effects on users
should be considered in their wider context. They
should allow users and affected parties to complain
about fairness and correct mistakes. A more
inclusive and resilient system should incorporate
disabled people when obtaining data to generate
models and testing. Finally, we recommend a
corpus of literature on human-centered design
procedures and ideologies to help AI and ML
developers develop algorithms that decrease
damage and improve disabled people\'s lives.
KEYWORDS : Accessibility, Inclusivity, Bias
mitigation, Assistive technologies
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2.
AN ANALYTICAL REVIEW OF CHALLENGES IN CLOUD COMPUTING
Tummapudi Sunil & Dr. Vijay Pal Singh
Page 16-24
 | Paper TitleAN ANALYTICAL REVIEW OF CHALLENGES IN CLOUD COMPUTINGAbstractIn the business world, cloud computing is the most promising implementation of utility computing at present due to its main advantages over traditional utility computing, including elasticity, which enables clients to dynamically scale up or down resources during execution time. However, despite being in its nascent phase, cloud computing continues to face challenges related to a lack of standardization. The primary obstacles to the adoption of cloud computing are security concerns. Therefore, apprehensive sectors, including government organizations (ministries), exhibit a reluctance to embrace cloud computing on account of the potential loss of sensitive data while it is hosted in the cloud; the lack of transparency regarding the security mechanisms employed by Cloud Service Providers (CSPs) to safeguard their data and applications; and the uncertainty surrounding data location. These factors collectively impede the adoption of the agile computing paradigm. The purpose of this study is to examine and categorize the challenges associated with the deployment of cloud computing, an area of significant interest that requires further investigation. KEYWORDS : Cloud Computing, Cloud Computing Issues
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3.
A COMPARATIVE STUDY OF FAULT DENSITY-BASED SOFTWARE RELIABILITY ESTIMATION TECHNIQUES UTILIZING FUZZY NETWORKS
Chintalapally Sandeep Kumar Dr. Regonda Nagaraju & Dr. Prasadu Peddi
Page 25-29
 | Paper TitleA COMPARATIVE STUDY OF FAULT DENSITY-BASED SOFTWARE RELIABILITY ESTIMATION TECHNIQUES UTILIZING FUZZY NETWORKSAbstractSoftware dependability pertains to the probability of software continually delivering correct service within a certain duration and under particular circumstances. Identifying commonly recurring problems throughout the development process is becoming more critical in many software industries. Identifying software defects in software project modules is a complex and intrinsically uncertain process. Although there are several intricate machine learning and deep learning models available for predicting problems, it is essential to create a simple model that integrates the knowledge of domain experts and successfully handles uncertainty in feature measurements. We have developed a software fault prediction model using the Mamdani Fuzzy Logic approach. This model has the capability to use conventional membership functions like triangle and trapezoidal, together with custom membership functions created by domain specialists, in order to produce accurate predictions about software faults. KEYWORDS : software fault prediction model using the Mamdani Fuzzy Logic approach
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4.
EXPERIMENTAL INVESTIGATION ON STRENGTH PROPERTIES OF LIGHT WEIGHT AGGREGATE CONCRETE WITH AGRICULTURAL WASTE ASH AS A REPLACEMENTS TO OPC
 | Paper TitleEXPERIMENTAL INVESTIGATION ON STRENGTH PROPERTIES OF LIGHT WEIGHT AGGREGATE CONCRETE WITH AGRICULTURAL WASTE ASH AS A REPLACEMENTS TO OPCAbstractIndia is a major maize-producing country, generating substantial quantities of maize cobs as agricultural waste. When these cobs are burned for energy or disposal, they produce maize cob ash (MCA), a byproduct that poses both environmental challenges and potential opportunities for sustainable construction materials.
The increasing demand for cement raises concerns due to its high energy consumption and carbon emissions. To mitigate these issues, researchers are exploring eco-friendly alternatives to conventional cementitious materials. MCA, rich in silica and other pozzolanic compounds, has emerged as a promising mineral admixture that can enhance concrete performance while addressing waste management challenges.
This study investigates the potential of MCA as a partial replacement for cement in the development of lightweight aggregate concrete. By incorporating Light Expanded Clay Aggregate (LECA) as a substitute for coarse aggregates and MCA as a supplementary cementitious material, the research aims to assess the effects of these modifications on the mechanical properties, durability, and overall performance of concrete through rigorous experimental analysis.
The findings of this study could contribute to sustainable construction practices by reducing cement consumption, promoting agricultural waste utilization, and enhancing the structural efficiency of lightweight concrete
KEYWORDS : Maize cob Ash, LECA, Mineral Admixture, Light Weight Agggregate
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