Content Based Automated File Organization Using Machine Learning Approaches

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Computers, Materials & Continua
DOI: 10.32604/cmc.2022.029400
Article
Content Based Automated File Organization Using Machine
Learning Approaches
Syed Ali Raza1,2, Sagheer Abbas1, Taher M. Ghazal3,4, Muhammad Adnan Khan5,6,
Munir Ahmad1and Hussam Al Hamadi7,*
1School of Computer Science, National College of Business Administration & Economics, Lahore, 54000, Pakistan
2Department of Computer Science, GC University Lahore, Pakistan
3Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia,
Bangi, Selangor, Malaysia
4School of Information Technology, Skyline University College, University City Sharjah, Sharjah, 1797, UAE
5Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University Lahore Campus,
Lahore, 54000, Pakistan
6Department of Software, Pattern Recognition and Machine Learning Lab, Gachon University,
Seongnam, 13120, Gyeonggido, Korea
7Cyber-Physical Systems, Khalifa University, Abu Dhabi, 127788, UAE
*Corresponding Author: Hussam Al Hamadi. Email: hussam.alhamadi@ku.ac.ae
Received: 02 March 2022; Accepted: 07 April 2022
Abstract: In the world of big data, it’s quite a task to organize differ-
ent files based on their similarities. Dealing with heterogeneous data and
keeping a record of every single file stored in any folder is one of the
biggest problems encountered by almost every computer user. Much of file
management related tasks will be solved if the files on any operating system
are somehow categorized according to their similarities. Then, the browsing
process can be performed quickly and easily. This research aims to design a
system to automatically organize files based on their similarities in terms of
content. The proposed methodology is based on a novel strategy that employs
the charactaristics of both supervised and unsupervised machine learning
approaches for learning categories of digital files stored on any computer
system. The results demonstrate that the proposed architecture can effectively
and efficiently address the file organization challenges using real-world user
files. The results suggest that the proposed system has great potential to
automatically categorize almost all of the user files based on their content. The
proposed system is completely automated and does not require any human
effort in managing the files and the task of file organization become more
efficient as the number of files grows.
Keywords: File organization; natural language processing; machine learning

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1Introduction
Classification of documents such as customer feedback, product reviews and e-mails etc., is
conventionally considered as an essential task in various fields like information retrieval, extraction
and recognition etc. [1]. Initial classes for the documents can be known or unknown initially. Yet,
the classification task can abridge the task of information processing and may also escalate the
performance of information processing systems [2]. Different machine learning approaches like
clustering, classification and optimization have been utilized previously for this purpose [3]. Generally,
these approaches use either textual content or the document’s overall structure to categorize them
into different classes [4]. However, most of the research in document classification is limited to the
classification or clustering of plain text documents. Yet, very little concentration has been given to
the automatic organization of different user files stored on computer systems based on their content.
Previously multiple file organizers and file managers are available for different operating systems,
but they do not manage files in an automated way or categorize files based on file extensions. This
research paper addresses the file organization problem by considering the content of the user file and
categorizing textual files of different extensions on any computer system.
Most of the clustering algorithms [5,6] available so far are data-centric, which means that they
are not ideal in recognizing labeled clusters [5]. These algorithms are not adoptable for grouping
files on computer systems [6] since they generally group documents into non-labeled clusters [7].
Optimization algorithms, for example, genetic algorithm, Ant Bee and particle swarm optimization,
have also been used to identify optimal cluster count [8]. The fitness function in such approaches
is to find the semantic similarity of the documents in a given cluster [9,10]. These approaches are
limited to pre-defined forms and are not generalizable to domain-independent documents [11–13]. A
few attempts have also been made for document clustering based on ontologies [14–17]. A significant
limitation of these systems is that as soon as the ontology size expands, it becomes difficult and
time-consuming to assign clusters to new documents. The observed computational complexities of
all of these benchmarking models are nonlinear [18]. And cluster count and cluster optimality are
questionable due to the curse dimensionality reduction by semantic relevance [10,19]. These models
are least significant to define optimal cluster count for document set with fewer divergence [7]. Since
the complexity of the traditional evolutionary strategies like Genetic Algorithm (GA), the process
complexity is observed as nonlinear [10]. A few attempts have been made to fine-learning through
supervised training after the unsupervised pre-training [20,21]. Likewise, supervised pre-training has
been shown supportive in different image processing problems [22,23]. Ross et al. [24] observed that
supervised pre-training on extensive scarce data with fine-tuning on minor problem-specific datasets
improves classification results.
The purpose of this research is to minimize the human efforts to the point where a user doesn’t have
to worry about first analyzing the document and then storing it into the folder of similar documents.
Furthermore, addressing the issues faced in document clustering and providing the solution is also a
significant focus of this research. The organization and management of digital documents and folders
and their non-digital counterparts have remained the focus of study for a long time. However, current
file organizers and file managers available for different operating systems either do not manage files in
an automated way or categorize files based on file extensions [9]. Dinneen et al. [12] reported that file
management simulates relatively unsupported activity that invites attention from different disciplines
with wide significance for topics across computer sciences.
Another significant aspect of file organization and categorization is how the file categories or
folders are named, as generating descriptive and meaningful names for both files and folders aids users

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find, apprehend and organize files [13]. On the other hand, this task is also tricky, particularly if the
user wants to generate concise and unique names [25]. Users usually tend to demonstrate substantial
creativity in file naming [14]. However, the file naming patterns are recognizable such as files are named
to display the document they represent, their purpose, or a relevant creation date or deadline [15]but
may also contain characters to expedite sorting of the files. This research addresses the file organization
problem by considering the content of the file document and using it to categorize textual files of
different extensions on any computer system. The system will work as a file manager. When the user
adds a document, it will create folder categories, generate names of the folder, and categorize files
into relevant folders automatically. The proposed system will help to reduce the efforts in managing
documents in an automated and intelligent manner.
2Materials and Methods
The task of automatic file organization is one of the most critical and time-consuming issues.
Most non-technical users often do not store files in separate folders. Eventually, main folders like
desktop, document or downloads in the Windows operating system appear to bear hundreds of files.
As the number of files in any particular folder increases, it becomes more challenging to manage
these files. The proposed system handles all these issues and is the first attempt towards an automatic
file management system for operating systems. The proposed architecture consists of two different
modules, named global and localize modules, as shown in Fig. 1. The global module is responsible
for the overall learning of categories and category labels. At the same time, the local organizer
classifies and stores the files in relevant folders based on the similarity of the files. Global module
passes the trained model of neural network and the parameter settings for data preprocessing, feature
extraction and truncated singular value decompositions. K-Means clustering approach is used for
initial clustering of files which is fine-tuned with Adam optimizer to predict final classes. Once the
number of documents in any folder increases to a certain threshold, the local organizer calls the global
organizer, which may restructure the folders and rearrange the files into relevant folders.
2.1 Global Module
The global module is subdivided into Preprocessing module, Feature Extraction Module, Dimen-
sionality Reduction Module, Clustering Module, Analysis Module, Topic Extraction Module and
Neural Network training module.
2.1.1 Preprocessing Module
Most of the files in the computer system are usually classified as unstructured documents;
therefore, almost all of the files must be pre-processed before the actual clustering begins. The pre-
processing of the files includes multiple steps such as Filtering, Stop Word Removal, Tokenization
and Stemming of document words.
Filtering: The task of filtering is to clean the data for all links, punctuation marks, digits and
unnecessary white spaces.
Stop Word Removal: The task of this sub-module is to remove stop words that don’t hold
any valuable meanings and increase the length of the document unnecessarily from a text analysis
perspective. Natural Language ToolKit (NLTK) [16] library has been used in this research to eliminate
all stop words.

1930 CMC, 2022, vol.73, no.1
Figure 1: Proposed architecture for automatic file organization system
Tokenization: Once stop words are removed from the document, tokens are assigned to all
remaining words to analyse the text better. The tokenization task is also done by using the NLTK
library.
Stemming: Stem provides the root word of every token to find important terms used in the
document. Snowball stemming algorithm [26] is one of the most popular stemming approaches for
English Language documents used in this research to stem all tokens. Once stemming is done, a Stem
Token Dictionary (STD) is maintained for topic extraction modules. Each entry in this dictionary
holds a stem with all corresponding tokens.
2.1.2 Feature Extraction Module
Once pre-processing of the files is done, the essential tokens from the files are extracted. For
this purpose, it is necessary to present all files in a suitable form. The vector space model is one of
the most common approaches representing documents as a vector space [20]. To find the similarity
between different files frequency of the words is used. In this research, each file Fi is located in an
n-dimensional vector space where all n dimensions are extracted using the Term Frequency Inverse
Document Frequency (TFIDF) vectorizer. It means that all components of a vector reflect a term
within the given file. The value of each component is determined by the degree of association among
the corresponding file and associated term. The TFIDF value can be calculated as mentioned in
Eq. (1).

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TFIDF =tf (t)∗idf (t)(1)
Tf (t) is the number of times the term t appears in a file divided by the total number of terms
in the file, and idf(t) is the loge of a total number of files divided by the total number of files with
term t. Term frequency finds out the frequency for each unique word and divides it with a number of
words in that file, making each file having a unit length, resulting in no biasness towards any file. If
the value of TFIDF is higher, it means that the associative term is rare, and if the value of TFIDF is
smaller than the weight, it means that the associative term is more common. A minimum threshold
mindf and maximum threshold maxdf are being used to check if any term appears in the number of
documents more remarkable than the maximum threshold or lower than the min threshold, then that
term is pruned. The threshold value for mindf is 2, and the maxdf is 0.5. The algorithm for the feature
extraction module is mentioned below in Algorithm 1.
The output of the TFIDF vectorizer with the threshold as mentioned above values for mindf and
maxdf is a TFIDF matrix of shape (18846, 32329) where 18846 are the no. of documents 32329 are the
features extracted by TFIDF vectorizer.
2.1.3 Dimensionality Reduction Module
The files on a user computer system may contain very high dimensional data, and it becomes
a formidable problem while applying statistical or machine reasoning methods on such data. For
instance, the feature extraction module in this research identified 32329 features from the given dataset,
which is a very high number. It is challenging to train a clustering or classification algorithm for this
number of input variables. Therefore, it is necessary to reduce extracted features before a clustering
or classification algorithm can be successfully applied to the dataset [27]. There can be two possible
ways to perform dimensionality reduction. One way is to keep the most appropriate variables from the
original dataset. The second way is to exploit the redundancy of the features in different ways and find
a smaller set of new variables. In the second approach, a smaller set of new variables is identified. Each
set is the combination of the input variables containing the same information as the input variables.
In this research, V ×f document-term matrix D is very important where V is not equal to f, and
it is implausible that D is symmetric. The first task in dimensionality reduction is to find Singular
Value Decomposition (SVD). For the decomposition D, let X be the V ×V matrix where columns
of the matrix are orthogonal eigenvectors of DDT, and Y be the f ×f matrix where columns are the
orthogonal eigenvectors of DTD. These X and Y represent the left singular vector and right singular
vector, respectively. Denote by DT the transpose of a matrix D is given in Eq. (2).
D=μXT(2)

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where the eigenvalues of DDTare the same as the eigenvalues of DTD, the orthonormal columns X,
Y can be represented as Eqs. (3) and (4).
XTX=I(3)
YTY=I(4)
The next step is to find the Truncated SVD, which is the subtraction of low-rank approximation
from SVD. Although the SVD can solve this approximation given a V ×f matrix X and a positive
integer k, it is essential to find a V ×f matrix Xk of rank as a maximum k to minimise the matrix
difference, as computed in Eq. (5).
A=X−Xk(5)
The value of A is the measure of inconsistency between the matrixes Xk and X. To find the
Truncated SVD, this inconsistency needs to be minimized while limiting Xk to have ranked at most k
as mentioned in Eq. (6).
min z|rank (z)=k||X−Z||F=||X−Xk||F(6)
2.1.4 K-Means Clustering
The k-means clustering algorithm [21] is widely used in document clustering problems and is
acknowledged as an efficient approach to clustering large data sets [19]. The K in this algorithm is
a pre-defined or user-defined constant, and the objective of the algorithm is to create a partition of
data based on features into k clusters. K-means algorithm performs well on linear time complexity
for the varying number of documents and especially gives better performance on a large number of
documents [28]. As it is clear from the previous discussion, thousands of user-generated files are stored
on any computer system; therefore, the K-Means algorithm can provide better and efficient initial
clusters [29]. The main idea of K-Means is to define centroids for all clusters. These centroids are
represented by k and are formed so that all features in a cluster are closely related to the centroid in
terms of similarity function. This similarity of features can be measured through various methods,
including cosine similarity and Euclidean distance, to all objects in that cluster [30]. The algorithm’s
output is a set of K cluster centroids where each k has a corresponding label L. The clusters are updated
after μkthe set of centroids is obtained, to hold the points nearest to each centroid. The centroids are
recomputed as the mean of all cluster points on μk. This process is repeated unless no change in the
centroids and the assignments of clusters is observed.
2.1.5 Silhouette Analysis
The major problem with K-Means clustering is that the number of centroids is defined before
the clustering begins [31]. In file categorization, it becomes almost impossible to predict the number
of folders/categories before the assignment of clusters. Therefore, silhouette analysis is used in this
research to identify the quality and separation distance between the resulting clusters. Silhouette
analysis results in a range of [−1, 1] where coefficients of the clusters near positive 1 specify that
the current cluster is at a reasonable distance from its neighboring clusters. If the value returned is
0, it specifies that the current cluster is close to the boundary and lies precisely on the boundary
between two neighboring clusters. A negative value specifies that current objects have been allocated
to the wrong cluster. One way to examine the impression mentioned above is by observing the Average
Silhouette Coefficient (ASC) evolution for each cluster. If the value of ASC is high, it specifies that

CMC, 2022, vol.73, no.1 1933
the clustering is good, whereas a small ASC value indicates that it is uncertain to which cluster these
elements belong.
Furthermore, a negative ASC value specifies that the elements in consideration do not belong to
a relevant cluster. The Silhouette Coefficient is calculated using the average distance between clusters
distance represented by ‘a’ and the average distance with the nearest cluster represented by ‘b’ for each
sample. The Silhouette Coefficient for a sample can be calculated using Eq. (7).
S=(b−a)
max (a,b)(7)
The number of clusters ‘K’ is selected with having the highest ASC, as mentioned in Eq. (8).
ASC =Savg (X,Labels)(8)
Once ASC is computed for all clusters, the next step is to use a threshold value as a cut-off point
for selecting the total number of clusters as computed through Eq. (9).
θASC =



minASC +maxASC
2



(9)
where minASC represents the minimum value of all corresponding ASC and maxASC is the value of
maximum ASC among all corresponding ASC. Every ASC score is compared with the θASC value and
the value of K is set till the point where the condition ASC score >θ
ASC remains true.
2.1.6 Topic Extraction
Topic extraction is an essential step for the overall working of the proposed system. Therefore,
in this research, a new strategy for topic extraction is proposed, as mentioned in Algorithm 2. First
of all, top terms per cluster are selected through the centroid of the cluster. Then, since dimensions
have been reduced for training purposes, all features are inverse transformed to get the original feature
map. Then, the top 10 terms from each TFIDF vectorizer are selected, and for each term, the lemma
is obtained using the NLTK library. Once the lemma is obtained, it is replaced with its hypernym to
get the most generic term. Finally, the most frequent hypernym is selected as the topic of the cluster—
Tab. 1 give a glimpse of extracted topics from the given dataset.
Table 1: Examples of extracted labels using proposed algorithm of topic extraction
Cluster Terms (Stems) Topic
5 gun control crime law weapon firearm cimin kill would peopl instrument
(Continued)

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Table 1: Continued
Cluster Terms (Stems) Topic
9 christian religion believ belief atheist god say faith bibl peopl establishment
13 drug test caus day effect also one doctor medic patient medical_practitioner
16 card driver video monitor vga use window bus color mode video_display
19 drive disk scsi hard floppi ide mb use control problem work
20 jesus god Christian church sin bibl Christ word one say sacred_writing
24 space orbit nasa launch shuttl mission satellit cost would earth location
2.1.7 Adaptive Moment Estimation
Adaptive Moment Estimation (ADAM) [32] optimizer is selected in this research because ADAM
is direct to implement, is efficient in terms of computational cost, and requires very little memory.
Furthermore, ADAM is an invariant to diagonal rescaling of the gradients and is suitable for problems
containing large datasets or have a large number of parameters. The method is also suitable for
non-static goals and problems with very noisy and/or sparse gradients. As the files stored on the
computer system are sparse and typically contain many parameters and data, ADAM can provide
better results than other gradients’ variants. ADAM computes individual adaptive learning rates for
different parameters from approximations of first and second moments of the gradients where mt and
vt are approximations of the mean and non-centred variance of gradients, respectively, as computed
in Eqs. (10) and (11).
mt=β1mt−1+(1−β1)gt(10)
vt=βtvt+(1−β2)g2
t(11)
As mt and vt are initiated as vectors of 0’s, the values of mt and vt are biased towards 0, mainly
when the rate of decay is low and at the time of initial steps. However, these biases are counteracted
through bias-corrected approximations of a first and second moment, as mentioned in Eqs. (12)
and (13).
ˆ
mt=mt
1−βt
1
(12)
ˆ
vt=vt
1−βt
2
(13)
These bias-corrected approximations are then used for parameters updating, which yields the
update rule as mentioned in Eq. (14).
θt+1=θt−η
√
vt+
mt(14)
2.2 Localize Module
The global module acts as a background process while the user interacts with the localize
module. The localize module works as a user interface and is responsible for receiving files from the
user, preprocessing the files, extracting features, and reducing dimensions for better analysis. After
dimensionality reduction, localize module classify the received file based on the model and parameter

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settings received through the global module. The preprocessing, feature extraction, and dimensionality
reduction of file in localize module is similar to the global module.
3Results and Discussion
The results of the proposed methodology are evaluated in this section. First of all, the experimental
setup is explained, followed by a comparative account of the results generated from different levels of
training of the proposed system. Then, the results are discussedwith the deployment of the system with
actual user-generated files. It is a known fact that every individual can have files of different nature and
counts stored on their computer system; therefore, it is complicated to generate a benchmark dataset
for file categorization problems. For experimental purposes, initial training of K-Means clustering and
ADAM optimizer is done on 20 Newsgroup data set [33], which have been used widely for applications
related to text analysis. 18846 subset documents were selected from the dataset on various topics as
listed in Tab. 2. Each subset consists of randomly chosen documents from various newsgroups. All the
documents header and main titles were removed to remove biasness in topic extraction and clustering.
Table 2: Statistics on experimental data
Sr. No. Topics Total No. of Docs. Sr. No. Topics Total No. of Docs.
1 alt.atheism 799 11 rec.sport.hockey 999
2 comp.graphics 973 12 sci.crypt 991
3 comp.os.ms-
windows.misc
985 13 sci.electronics 984
4 comp.sys.ibm 982 14 sci.med 990
5 comp.sys.mac 963 15 sci.space 987
6 comp.windows.x 988 16 soc.religion 997
7 misc.forsale 975 17 talk.politics.guns 910
8 rec.autos 990 18 talk.politics.mideast 940
9 rec.motorcycles 996 19 talk.politics 775
10 rec.sport.baseball 994 20 talk.religion 628
After preprocessing and feature extraction task is completed, this K-Means algorithm was trained
on this dataset with no. clusters ranging from 2–40. For analysis of the cluster quality, ASC for each
K-Means result was computed, as shown in Tab. 3. The values in Tab. 3 expresses the ASC concerning
several clusters generated by the K-Means Algorithm. The cutoff point is computed using Eq. (4),
which is 0.003. Therefore, the number of clusters selected for K-Means was set to 26 because the value
of cluster 27 decreases from the computed threshold.
Table 3: Average silhouette coefficients with respect to number of clusters generated by k-means
algorithm
Clusters234567891011121314
ASC 0.0092 0.0066 0.0066 0.0068 0.0078 0.0075 0.0075 0.0067 0.007 0.0056 0.0055 0.0061 0.0059
Clusters 15 16 17 18 19 20 21 22 23 24 25 26 27
ASC 0.0051 0.0101 0.0046 0.0061 0.0098 0.0075 0.0065 0.0048 0.0046 0.0095 0.0068 0.0097 −0.0065
(Continued)

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Table 3: Continued
Clusters234567891011121314
Clusters 28 29 30 31 32 33 34 35 36 37 38 39 40
ASC 0.0082 0.0072 −0.0164 −0.0122 −0.0087 −0.0115 0.0006 −0.0111 −0.0109 0.0021 −0.0086 −0.0078 −0.0091
Once the number of clusters was identified dimensionality reduction algorithm was applied
through truncated SVD. Truncated SVD takes n components and transforms the feature matrix
such that for 100 <=no. of components <=1000 and then ASC is computed for each value of n
components for 2 <=K<=26.
It can be observed from Tab. 4 that the ASC decreases as the dimensions of feature vectors
increases. Therefore, the best K is selected based on the highest value of ASC, which is found with
100 components with the highest value of 0.074 for K =26. The plot of ASC for different dimensions
of the feature vector is shown in Fig. 2, which depicts that ASC is inversely proportional to the feature
dimensions. Therefore, the value of ASC decreases when the feature dimensions are increased.
Table 4: Different dimensions with respect to the number of clusters generated by the K-means
algorithm
No of clusters
Dim.234567891011121314
100 0.031 0.033 0.034 0.037 0.037 0.04 0.042 0.044 0.047 0.051 0.054 0.053 0.056
200 0.019 0.014 0.023 0.023 0.025 0.027 0.028 0.028 0.031 0.033 0.031 0.035 0.037
300 0.015 0.012 0.018 0.017 0.019 0.02 0.021 0.022 0.024 0.023 0.025 0.027 0.025
400 0.012 0.01 0.013 0.014 0.016 0.018 0.018 0.019 0.02 0.021 0.023 0.021 0.023
500 0.011 0.009 0.012 0.011 0.014 0.014 0.017 0.016 0.019 0.017 0.021 0.02 0.021
600 0.01 0.009 0.011 0.012 0.013 0.014 0.013 0.016 0.014 0.015 0.014 0.017 0.016
700 0.01 0.009 0.011 0.011 0.012 0.013 0.012 0.015 0.015 0.015 0.017 0.013 0.016
800 0.01 0.008 0.01 0.01 0.012 0.013 0.014 0.014 0.015 0.011 0.016 0.012 0.013
900 0.009 0.008 0.01 0.01 0.011 0.012 0.013 0.013 0.012 0.015 0.012 0.014 0.013
1000 0.009 0.008 0.009 0.01 0.011 0.012 0.012 0.011 0.013 0.014 0.012 0.013 0.012
No of clusters
Dim. 15 16 17 18 19 20 21 22 23 24 25 26
100 0.059 0.062 0.062 0.063 0.064 0.066 0.065 0.067 0.067 0.072 0.07 0.074
200 0.038 0.041 0.039 0.04 0.041 0.041 0.041 0.045 0.043 0.045 0.046 0.048
300 0.029 0.028 0.031 0.029 0.032 0.033 0.033 0.032 0.034 0.033 0.036 0.038
400 0.022 0.025 0.022 0.025 0.024 0.028 0.025 0.009 0.026 0.03 0.03 0.025
500 0.019 0.021 0.021 0.017 0.022 0.023 0.024 0.023 0.023 0.022 0.021 0.025
600 0.017 0.019 0.018 0.02 0.021 0.02 0.021 0.019 0.02 0.021 0.019 0.021
700 0.015 0.016 0.019 0.017 0.017 0.018 0.018 0.018 0.017 0.017 0.02 0.02
800 0.016 0.015 0.014 0.017 0.015 0.015 0.017 0.015 0.016 0.015 0.017 0.01
900 0.013 0.014 0.015 0.016 0.015 0.015 0.016 0.014 0.017 0.014 0.019 0.016
1000 0.016 0.013 0.014 0.013 0.015 0.014 0.013 0.015 0.016 0.015 0.016 0.015

CMC, 2022, vol.73, no.1 1937
Figure 2: Plot for ASC for different dimensions of the feature vector
Once the clusters were obtained, the neural network was trained with 26 output classes and 100
input neurons. Different settings for hidden layers were used for 10 <=neurons <=100 with 1 Hidden
layer. The training results are mentioned in Tab. 5. It can be observed from the table that the training
loss is smallest when the number of neurons is 40 in 1 hidden layer. The training loss starts increasing
when the number of neurons is more than 40 in the hidden layer. The best result obtained with 40
neurons is training loss is 0.008, and validation loss is 0.181.
Table 5: Training and validation loss with different number of neurons in first hidden layer with ADAM
optimizer
Neuros in hidden layer Samples 10 20 30 40 50 60 70 80 90 100
Training 13192 0.214 0.0024 0.01 0.008 0.004 0.003 0.002 0.002 0.001 0.002
Validation 5654 0.315 0.182 0.187 0.181 0.194 0.212 0.237 0.229 0.24 0.24
Tab. 6 provides the result of accuracy with the same network settings. Again, it can be observed
that accuracy for the neurons is best with 40 neurons in the first hidden layer where loss is negligible
primarily, i.e., 0.999 and the validation accuracy is 0.946.
Table 6: Training and validation accuracy with different number of neurons in first hidden layer with
ADAM optimizer
Neuros in hidden layer Samples 10 20 30 40 50 60 70 80 90 100
Training 13192 0.996 0.998 0.998 0.998 0.998 0.998 0.996 0.997 0.994 0.998
Validation 5654 0.888 0.912 0.926 0.924 0.928 0.921 0.926 0.922 0.923 0.924
Based on the results of hidden layer 1, the neural network was trained again with two hidden layers
where the neurons in the first hidden layer were 40 and the number of neurons in the second hidden
layer was 10 <=neurons <=100. Tab. 7 shows that the training loss is almost negligible when the
number of neurons in the second hidden layer is 30, and the loss starts increasing when the number of
neurons is more than 30 in the 2nd hidden layer. The best result achieved with 40 neurons in hidden
layer 1 and 30 neurons in hidden layer 2 shows the 0.008 training loss and 0.378 validation loss. Tab. 8
provides accuracy with the same network settings. The accuracy for the neurons is best with 40 and

1938 CMC, 2022, vol.73, no.1
30 neurons in the first and second hidden layer, respectively, where training accuracy is 0.998, and the
validation accuracy is 0.926.
Table 7: Training and validation loss with 40 neurons in first hidden layer and different number of
neurons in second hidden layer with ADAM optimizer
Category
name
Accomplish Consider Location Medical
practitioner
Modify Move Take
No. of files 6 40 8 13 21 101 15
Table 8: Training and validation accuracy with 40 neurons in first hidden layer and different number
of neurons in second hidden layer with ADAM optimizer
Neuros in hidden layer Samples 10 20 30 40 50 60 70 80 90 100
Training 13192 0.924 0.998 0.999 0.999 0.999 0.999 1.0 1.0 1.0 0.999
Validation 5654 0.891 0.942 0.942 0.949 0.948 0.948 0.946 0.945 0.94 0.946
Once the training was completed, the proposed system was tested by adding multiple files to the
system. Around 204 files of various extensions such as .pdf, .docx, .txt, .pptx on different topics were
provided to the system and results were obtained as mentioned in Tab. 9.
Table 9: Category name and user files stored by the proposed system after training
Neuros in hidden layer Samples 10 20 30 40 50 60 70 80 90 100
Training 13192 0.022 0.008 0.008 0.006 0.007 0.006 0.013 0.012 0.015 0.007
Validation 5654 0.567 0.454 0.378 0.384 0.401 0.461 0.405 0.454 0.471 0.455
It was observed that six files were stored in the folder “Accomplish”, where the content of these
files included different results of students. Around 40 files were moved in the folder “consider” by the
proposed system. Most of these files included a list of students admitted to institutes or other notices
for different events. The folder “location” received eight files, and the content of these files was related
to maps and geographical details about different places. The folder “medical practitioner” received
13 files, and most of the files included content related to psychology, communication skills or related
the field of medical. Around 21 files were moved in folder “modify”, where most of the novels were
moved to this folder, including adventure stories or science fiction. Around 101 files were moved in
folder “move”, and all of the files were related to programming, course books of computer science and
different articles and research papers on artificial intelligence. Finally, 15 files were moved in folder
“take”, where most of the files in this folder included content related to programming related slides
or code.
These results depict that although the names of different categories are not appropriate due to
initial training with dataset of news articles, the number of files stored in different systems is different.
However, most of the files having similar content are stored in the same folder. This limitation of
having inappropriate names can be dealt with few pieces of training with user-generated files. It is

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evident that every user may have files of different natures, so predicting category names is almost
impossible. Still, when users start uploading the files in the system, it can automatically learn and
update according to the users’ requirements. It is important to note that all of this categorization
process is entirely automated, which means that users only have to provide files to the system. It is the
system’s responsibility to recognize the content of the file and manage files accordingly.
Another experiment was conducted with 222 files, including 204 files used in the previous
experiment and both global and localize modules were executed. The results obtained after this
experiment was exciting. The first thing observed was that the proposed system computed the threshold
ASC value for K was three as computed using Eq. (9) for Tab. 10 .
Table 10: Average silhouette coefficients with respect to number of clusters generated by k-means
algorithm on original feature space dimensions (222 ×24573)
No. clusters 2 3 4 5 6 7 8 9 10
ASC 0.05 0.05 0.016 0.048 0.046 0.043 0.069 0.013 0.017
Based on Tab. 10, the proposed system selected 3 clusters and executed the dimensionality
reduction module for several components 100 to 222. However, the number of components for this
module cannot exceed the number of documents given for training. As a result, the best value obtained
was 0.07 in 100 dimensions with 3 clusters, as mentioned in Tab. 11.
Table 11: Different dimensions with respect to the number of clusters generated by the k-means
algorithm
No. clusters
Dimension 2 3
100 0.067 0.07
200 0.05 0.05
222 0.05 0.05
The next task was to train a neural network for three output classes. The neural network achieved
the best performance with ten neurons in both the first and second hidden layers. However, the
maximum accuracy achieved with the validation set was 70% which is very low compared to the
first experiment. This depreciation of accuracy was mainly because of the smaller dataset size and
the diversity of documents in the given dataset. The 222 files were user-generated files on various
topics like course books, novels, programming code, student’s marks sheets etc. After training, the
proposed system categorized these files into three different folders, as mentioned in Tab. 1 2 . Seventeen
files were moved in the folder “consume”. In comparison, most of these files had content like a
list of students admitted in institutes and students marks sheets etc. 181 files were moved in folder
“information” where most of the files in this folder included content related to books on psychology,
communication skills, course books of Computer Science particularly books and research papers on
artificial intelligence and novels. Twenty-four files were moved in folder “quantity”, where all of the
files in this folder were programming related slides or code.

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Table 12: Category name and user files stored by the proposed system after training
Consume Information Quantity
17 180 24
4Conclusion
In the current digital world, where most of the data available has been digitalized and is available
in different files, managing these heterogeneous files is becoming a tedious task. Most computer
users create, copy or download tens of files daily and as the number of files on a system grows, it
becomes more difficult to arrange these files. The motivation behind this research is to design a system
for categorizing files based on their similarities consequently. This research is the first step towards
automatic file management based on the content to the best of our knowledge. The results suggest that
the proposed system has great potential to automatically categorize almost all of the user files based
on their content. The proposed system is completely automated and does not require any human effort
in managing the files. As soon as the number of files grows, the predictions of labels and categorization
tasks become more efficient. One of the significant limitations of the proposed system is that it can
only categorize text-based files. The future versions of this system will incorporate an image, audio
and visual files for categorization.
Acknowledgement: Thanks to our families & colleagues who supported us morally.
Funding Statement: The authors received no specific funding for this study.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the
present study.
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