47 research outputs found
An FCM-Based Dynamic Modelling of Integrated Project Delivery Implementation Challenges in Construction Projects
Question: What are the Integrated Project Delivery Implementation challenges in construction projects, their interrelationships and their effects on the project time, cost and quality?Purpose: The Purpose of this study is applying an efficient method to determine the most important challenges to IPD implementation in construction industry, and also to evaluate the interrelationships among these challenges and their effects on the project time, cost, and quality.Research Method: This study models available Integrated Project Delivery challengesusing a real case data, through applying Fuzzy Cognitive Mapping technique.Findings: Results show that contractual factors have the major influence compared with others. This shows the significance of paying attention to why project stakeholders must be integrated throughout the project life cycle since early contract documentation stage.Limitations/Implications: This study is limited to the caseselectedfrom Tehran of Iran.Value for authors: This study is significantdue to identifying, classifying and determining the intensity of effects of IPD implementation challenges on cost, time, and quality of construction projects. It results in planning, resolving the challenges, enhancing the quality of constructions and lastly saving the construction cost and time
A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring
The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society
Review of farmer-centered AI systems technologies in livestock operations
The assessment of livestock welfare aids in keeping an eye on the health, physiology, and environment of the animals in order to prevent deterioration, detect injuries, stress, and sustain productivity. Because it puts more consumer pressure on farming industries to change how animals are treated to make them more humane, it has also grown to be a significant marketing tactic. Common visual welfare procedures followed by experts and vets could be expensive, subjective, and need specialized staff. Recent developments in artificial intelligence (AI) integrated with farmers’ expertise have aided in the creation of novel and cutting-edge livestock biometrics technologies that extract important physiological data linked to animal welfare. A thorough examination of physiological, behavioral, and health variables highlights AI's ability to provide accurate, rapid, and impartial assessments. Farmer-focused strategy: an emphasis on the crucial role that farmers play in the skillful adoption and prudent application of AI and sensor technologies, as well as conversations about developing logical, practical, and affordable solutions that are specific to the needs of farmers
SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network
Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of morphological variety and indistinguishable characteristics across skin malignancies. Recently, deep learning models have been used in the field of image-based skin-lesion diagnosis and have demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep convolutional neural network termed SkinLesNet was built in this study. The dataset used in this study was extracted from the PAD-UFES-20 dataset and was augmented. The PAD-UFES-20-Modified dataset includes three common forms of skin lesions: seborrheic keratosis, nevus, and melanoma. To comprehensively assess SkinLesNet’s performance, its evaluation was expanded beyond the PAD-UFES-20-Modified dataset. Two additional datasets, HAM10000 and ISIC2017, were included, and SkinLesNet was compared to the widely used ResNet50 and VGG16 models. This broader evaluation confirmed SkinLesNet’s effectiveness, as it consistently outperformed both benchmarks across all datasets
Markowitz-based cardinality constrained portfolio selection using Asexual Reproduction Optimization (ARO)
The Markowitz-based portfolio selection turns to an NP-hard problem when considering cardinality constraints. In this case, existing exact solutions like quadratic programming may not be efficient to solve the problem. Many researchers, therefore, used heuristic and metaheuristic approaches in order to deal with the problem. This work presents Asexual Reproduction Optimization (ARO), a model free metaheuristic algorithm inspired by the asexual reproduction, in order to solve the portfolio optimization problem including cardinality constraint to ensure the investment in a given number of different assets and bounding constraint to limit the proportions of fund invested in each asset. This is the first time that this relatively new metaheuristic is in the field of portfolio optimization, and we show that ARO results in better quality solutions in comparison with some of the well-known metaheuristics stated in the literature. To validate our proposed algorithm, we measured the deviation of obtained results from the standard efficient frontier. We report our computational results on a set of publicly available benchmark test problems relating to five main market indices containing 31, 85, 89, 98, and 225 assets. These results are used in order to test the efficiency of our proposed method in comparison to other existing metaheuristic solutions. The experimental results indicate that ARO outperforms Genetic Algorithm(GA), Tabu Search (TS), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) in most of test problems. In terms of the obtained error, by using ARO, the average error of the aforementioned test problems is reduced by approximately 20 percent of the minimum average error calculated for the above-mentioned algorithms
Credit Card Fraud Detection Using Asexual Reproduction Optimization
As the number of credit card users has increased, detecting fraud in this
domain has become a vital issue. Previous literature has applied various
supervised and unsupervised machine learning methods to find an effective fraud
detection system. However, some of these methods require an enormous amount of
time to achieve reasonable accuracy. In this paper, an Asexual Reproduction
Optimization (ARO) approach was employed, which is a supervised method to
detect credit card fraud. ARO refers to a kind of production in which one
parent produces some offspring. By applying this method and sampling just from
the majority class, the effectiveness of the classification is increased. A
comparison to Artificial Immune Systems (AIS), which is one of the best methods
implemented on current datasets, has shown that the proposed method is able to
remarkably reduce the required training time and at the same time increase the
recall that is important in fraud detection problems. The obtained results show
that ARO achieves the best cost in a short time, and consequently, it can be
considered a real-time fraud detection system
Startup’s critical failure factors dynamic modeling using FCM
The emergence of startups and their influence on a country's economic growth has become a significant concern for governments. The failure of these ventures leads to substantial depletion of financial resources and workforce, resulting in detrimental effects on a country's economic climate. At various stages of a startup's lifecycle, numerous factors can affect the growth of a startup and lead to failure. Numerous scholars and authors have primarily directed their attention toward studying the successes of these ventures. Previous research review of critical failure factors (CFFs) reveals a dearth of research that comprehensively investigates the introduction of all failure factors and their interdependent influences. This study investigates and categorizes the failure factors across various stages of a startup's life cycle to provide a deeper insight into how they might interact and reinforce one another. Employing expert perspectives, the authors construct fuzzy cognitive maps (FCMs) to visualize the CFFs within entrepreneurial ventures and examine these factors' influence across the four growth stages of a venture. Our primary aim is to construct a model that captures the complexities and uncertainties surrounding startup failure, unveiling the concealed interconnections among CFFs. The FCMs model empowers entrepreneurs to anticipate potential failures under diverse scenarios based on the dynamic behavior of these factors. The proposed model equips entrepreneurs and decision-makers with a comprehensive understanding of the collective influence exerted by various factors on the failure of entrepreneurial ventures
BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches
Generating the hash values of short subsequences, called seeds, enables
quickly identifying similarities between genomic sequences by matching seeds
with a single lookup of their hash values. However, these hash values can be
used only for finding exact-matching seeds as the conventional hashing methods
assign distinct hash values for different seeds, including highly similar
seeds. Finding only exact-matching seeds causes either 1) increasing the use of
the costly sequence alignment or 2) limited sensitivity.
We introduce BLEND, the first efficient and accurate mechanism that can
identify both exact-matching and highly similar seeds with a single lookup of
their hash values, called fuzzy seeds matches. BLEND 1) utilizes a technique
called SimHash, that can generate the same hash value for similar sets, and 2)
provides the proper mechanisms for using seeds as sets with the SimHash
technique to find fuzzy seed matches efficiently.
We show the benefits of BLEND when used in read overlapping and read mapping.
For read overlapping, BLEND is faster by 2.6x-63.5x (on average 19.5x), has a
lower memory footprint by 0.9x-9.7x (on average 3.6x), and finds higher quality
overlaps leading to accurate de novo assemblies than the state-of-the-art tool,
minimap2. For read mapping, BLEND is faster by 0.7x-3.7x (on average 1.7x) than
minimap2. Source code is available at https://github.com/CMU-SAFARI/BLEND
SMASH: Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations
Important workloads, such as machine learning and graph analytics
applications, heavily involve sparse linear algebra operations. These
operations use sparse matrix compression as an effective means to avoid storing
zeros and performing unnecessary computation on zero elements. However,
compression techniques like Compressed Sparse Row (CSR) that are widely used
today introduce significant instruction overhead and expensive pointer-chasing
operations to discover the positions of the non-zero elements. In this paper,
we identify the discovery of the positions (i.e., indexing) of non-zero
elements as a key bottleneck in sparse matrix-based workloads, which greatly
reduces the benefits of compression. We propose SMASH, a hardware-software
cooperative mechanism that enables highly-efficient indexing and storage of
sparse matrices. The key idea of SMASH is to explicitly enable the hardware to
recognize and exploit sparsity in data. To this end, we devise a novel software
encoding based on a hierarchy of bitmaps. This encoding can be used to
efficiently compress any sparse matrix, regardless of the extent and structure
of sparsity. At the same time, the bitmap encoding can be directly interpreted
by the hardware. We design a lightweight hardware unit, the Bitmap Management
Unit (BMU), that buffers and scans the bitmap hierarchy to perform
highly-efficient indexing of sparse matrices. SMASH exposes an expressive and
rich ISA to communicate with the BMU, which enables its use in accelerating any
sparse matrix computation. We demonstrate the benefits of SMASH on four use
cases that include sparse matrix kernels and graph analytics applications