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Efficient Approach Of Memory Glow Method For
Find The Misplaced Items
Dr.L.Sathish Kumar, Dr.K.Thinakaran, Dr. K.Santhi, Dr.R.Pandi Selvam
Abstract: Memory can be characterized as the maintenance, memory and the enlistment of past occurring, Human memory is perplexing. Our mind needs to recall past happened understanding to support the live moments, for instance, the majority of the time we overlook a few things like pen drive, significant archives, watch and extra, however we didn't recollect after some time where we dropped that things. These sorts of overlook will make numerous issues in the live world. Such a large number of strategies are having on the planet for specifically this occurred and these sorts of strategies are utilized RFID and some remote procedures for found the lost things. Be that as it may, these all are gear cost are exceptionally high and furthermore need electronic hotspot for working and locate the lost things. These sorts of issues have been approved proficiently by MGM (Memory Glow Method) technique, in light of the fact that no need any electrical innovation for working this strategy and we possess to go through some energy for entering insights regarding the things to the FMIMGM (Find the Misplaced Items Using Memory Glow Method) framework. This framework has discovered the lost thing inside 0.01 sec normal time, achievement rate is more than 96 % and dissatisfaction level is 0.3.
Index Terms: Memory Glow Algorithm, Misplaced Item, Memory Glow Method, CTA Matrix. —————————— ——————————
1
INTRODUCTION
he outwardly diminished, out of impulse, tend to be sorted out with regards to monitoring significant things, for example, keys, wallets or cell phones. Notwithstanding, numerous dull things are not part of the equivalent demanding every day routine and may not be as intently pursued. Items may incorporate jars of washing items while they are washing their homes, a punch machine while they are working in their working environment or their tea cup in the early morning. Despite the fact that losing these things might not have a similar astuteness of direness as dropping a lot of table keys, it might have the option to in any case be time-taken and attempting to discover the things. Existing key-following framework, for example, in Sonic Key Finder (2000) has working by sounds are inclined to bogus positives and normally need the assistance of a far-located being to utilize them. There is nothing more wearisome than attempting to locate a lost thing. The strain to discover it goes increments exponentially when it is to some degree extremely fundamental like a home property records. Moreover, these frameworks are no need cell phone, separate establishment frameworks for all living arrangements where the client may have missed a thing. Over and finished with an iterative movement, we have pondered a portable application for rapidly and easily find and finding the things. The inspiration of this strategy is called FMIMGM (Find the Misplaced Items Using Memory Glow Method), is totally portable and framework. We need to include basic clues into the frameworks, what and where we have put things as it were. In this paper, we talk about with existing work in things discoverer frameworks and then secured for the convey points of interest in the proposition and usage of the FMIMGM strategy.
We at that point banter the appraisal of the strategy and results, convey implications we revealed in the plan of these classifications of frameworks, and afterward achieve.
2
RELATED
WORK
These systems for exhorting the issuing of finding lost items have been examined widely both commercially [2, 3, 4, 5] and in examination [6]. The most related system to ours is a commercial system is called The Locating system [7] from the Gloucester smart automation. This method uses an audio call out system for locating items marked around surroundings. One more method for finding items in the home is through the use of system vision and computational sensitivity [8]. This method uses cameras to find when items of awareness are unstable within a planetary and can thus statement the live location of any items within its arena of sight. This method is not presently cost in effect compared to other methods, but has the advantage of being able to passively track items. An existing Ma and Paradiso [9] present a resolution that uses a flash-light to actuate tags on items around some surroundings, producing an LED on the tag to spark. Other investigators have also discovered finding lost items for the universal people. Peters et al. [6] measured many handlers on the types of items that are commonly lost and also included a summary of various approaches used in finding items. They established a set of guiding principle for use in evolving solutions a method to help find lost items. Motivated by their work, we sought after to reveal which items entities with graphic impairments often misplaced, their approaches for finding or avoiding loss, and guiding principle for increasing a technological result. Participating plan study of RTLS [10] based on perspective mapping, conducted in two diagnosis home administrations. Somewhat than reconnoitering the technological opportunities, this review investigates the needs and needs from the perspective of the care expert. Human interface [11] is established on the robot, so that it can identify voice commands from customers. Then, an item recognition based on the deep knowledge Convolution Neural Networks (CNN) is implementing to detect the misplaced objects. Next the incisive result is pronounced in words to the user, in the interim the location of the objects is shown in the remote end. [12] A method to locate a private item of a user on dropping the personal object is delivered. Personification, the system T
_____________________________
• Dr. L. Sathish Kumar, Assistant Professor of School of Computer Science and Engineering, VIT Bhopal University, Bhopal, Madhya Pradesh. E-mail: [email protected].
• Dr.K.Thinakaran, Associate Professor, Dept. Comp. Sci. and Eng. Saveetha School of Engineetring, Chennai. E-Mail: [email protected].
• Dr. K.Santhi Professor, Dept. Comp. Sci. and Eng., Sreenivasa Institute of Technology and Management Studies,Chittoor E-Mail: santhiglorybai @gmail.com.
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comprises an object affixed on a surface of the individual item to audibly emit programmed sound for a prearranged amount of time when the receiver items receives a signal having a certain frequency unique to the receiver items. The methods further include a transmitter device by electronic coupled with a data dealing out method through a Universal Serial Interface (USB) harbor of the data processing method. A method ID3 algorithm [13] for diagnosis the disease based upon the user query in the process implementing of mobile phone and television because all category people can used easily find and predicted what kind of disease through television and mobile phones. The new techniques [14] would be successful to find out disease and subsequent disease in the base of evidence. The evidence is checked from the existing patient EMR report. The proposed method [15] extracts information, especially the information about diseases, using ICD-10 code which is supplemented with the statistical evidence. Here extraction of information is in the form of query combination of keywords. With the help of this method, we could generate predictions for the diseases listed in ICD-10. The predictions are based on standard data-sets from Universal Classification Irvin (UCI) repository, Word Health Organization, and EMR data-sets. To evaluate whether a [17] dichotic memory test would reveal deficits in short-term working-memory recall and long-term memory recall in a group of adult patients with ADHD. The method basically consists of an Android Application within itself with the help of GSM [18] and Bluetooth technology. The objective of project to produced common platform as users. There are many higher end cars provide features like performance Management, etc. But, these are only available with top end vehicles. A Middle-class man cannot afford to spend a lot of money on this kind of Cars just for these features. This system will provide the features to all types of cars. The methods gives interrupted analytical reports of cars performances. Thus, car lovers can plan the budget for a given amount time.
3 PROPOSED
FMIMGM
METHOD
The FMIMGM Find Misplaced Items Using Memory Glow Method has two main phases, namely input about will be placing item by the user and search misplaced item using MGM method.
First phase is input of the details about what and where items are placed, it has totally seven steps, there are the user have
to enter the details for items, next step 2 is that items were going to placed, third step is which date have been items placed, step 4 is what kind of types of item we have placed, and enter some description of about the items, step 6 is the user have to enter item placed location and their descriptions also and finally all details are stored to database. The second phase is finding the misplaced items and it has four steps there are first the user has to give input about misplaced items, if that user didn’t remember about anything they have another option step two is either or one from item name or type or keywords about the items. After that the MGM method has search location of item from database based on given information by user. Finally, the MGM method produced result location of misplaced item details. The following session is having be explain FMIMGM proposed method is how to find the misplaced items.
3.1 Inputs about Dropped Item
This phase, get all information about placed items from user. If a user wants to place an items like pen drive or some important document in the home.
Procedure 1: Inputs about Item Auxiliary Variables:
UIN ← User and Item Name
ID ← Keywords of Item & Description PN ← Item Place Name
FPN ← Final Item Place Name D ← Date of Item Placing T ← Item Type
TD ← Type Description
PND ← Item Place Name Description DB ← Database
Begin:
1. UIN ← UIN_text 2. T ← T_text 3. ID ← ID_text
4. If PN = ―Home‖ or PN = ―Shelf‖ or PN = ―Table‖ or PN = ―Files‖ or PN = ―Beside
5. Table‖ or PN = ―Teapoy‖ or PN =‖Beside Mirror‖ or PN =‖On Chair‖ or PN
6. =‖Beside Mirror‖ or or PN =‖Bed‖ or PN =‖Cupboard‖ or PN =‖Car‖ or PN =‖Bike‖
7. Then 8. FPN ← PN 9. End IF 10. D ← D_text 11. TD ← TD_text 12. PND ← PND_text
13. If T and ID and PN and FPN and D and TP and PND != Empty then
14. DB ← All Details and Validations 15. End If
End: Inputs about Item
The user should be entering first user name and item name to UIN. After that have to enter item type to T like if item name is pen drive than item type is electronics, item type description to TD, place name to PN and place description to PND. After that check whether all attributes are entered it will store in to database.
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Auxiliary Variables:
SKW ← Search Key Words
OSKW ← Optional Search Keywords (Name, Type, Description)
RL ← Result Location Begin:
1. SKW ← Keywords by user 2. For i <= UIN.count do 3. For j <= IN.count do 4. For k <= T.count do
5. For l <= PND.cout do 6. If SKW == UIN(i) then
7. RL ← UIN.PN 8. End If
9. If SKW == IN(j) then 10. RL ← IN.PN 11. End If
12. If SKW == T(k) then 13. RL ← T.PN 14. End If
15. If SKW == PND(l) then 16. RL ← PND.PN 17. End If
18. Next i
19. Next j
20. Next k
21. Next l End: Memory Glow Method
The user has two options for enter keywords, one is direct keywords another one is either or one of item name or item type or dropped place name or place description or related words of dropped item. These words are considering as keywords, this keyword stored in to SKW. After that the SKW will match with all attributes in the database, first the keywords are match with UIN, IN, T, PND also, if there is matched will get item location based on item name and stored into RL. Finally, the user can get right location of misplaced item location with help of MGM method.
4 EMPIRICAL
SURVEY
Overall, the proposed system successfully permitted the user to attach multiway searching misplaced items. The proposed system to justify the result using four users and it will be proving many differences in how our users used the system and the rate at which they missed items. from the table 1 Raja, he who was using FMIMGM on him smart phone, his reported the most of time use of the system. he reported using it 3-5 times per day and found it very easy to track her pen drive and his tea cup. He said he always knew where him cup, so when something was lost he could locate the object quickly and easily. She also reported that she ―lost‖ the item more because it was much easier to find. Four of our users reported experimenting with locating them on different items to see where it would be the most helpful. Hence, they put the search on their most repeatedly misplaced item and leave it there unless needed. The figure 2 Indicate of how many time (min) taken for searching and founded the missing items and figure 3 is show the ratio of success rate of the this proposed methodology, figure 4 is given the frustration level variation base on the misplaced items.
Overall, the proposed system FMIMGM system an increase in
TABLE1
ITEMS LOST BY USER AND AVERAGED THE TIME TO FIND AND FL
End User Name
Miss Placed Items
Times
No. of Remark
Time (Min.)
Best Case Rate
Frustratio n level
Raja Pen Drive 5 Yes 0.2 97.5 0.5
Mia Keys 3 Yes 0.2 97 0.2
Shankar Pen 6 Yes 0.05 99.6 0.3
Raja Tea cup 5 yes 0.2 97.78 0.5
Bala TV remote 11 Yes 0.5 98.7 0.2
Pandisel
vam Mobile 1 Yes 0.3 97.2 0.7
Pandisel vam
Head
Phone 3 Yes 0.2 97.3 0.1
Shankar Novel Book 1 Yes 0.05 98.7 0.2
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the success rate for finding misplaced items and a reduce in the time it took for users to find misplaced items if they were tracked by our system, with most users finding their object within 0.1 minutes. Moreover, the FMIMGM method decrease in the frustration level (FL) associated with finding lost items. This makes FMIMGM successful on the three parameters are time, success rate and FL.
4
CONCLUSION
The proposed technique proficiently found the lost things dependent on client things database. In FMIMGM strategy, MGM coordinating based discovered is proposed to establish the lost things by the client question. Here catchphrase is which helps in the pursuit things. The watchword is particularly client explicit. It is free watchwords given by the client. The proposed technique execution is assessed as far as Time, Success Rate, and Frustration Level. The proposed technique FMIMGM yields 0.21 min Average Time, 97.97 % Success Rate and 0.33 % Frustration Level.
5
REFERENCES
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[3]. FindIT.
http://www.ambitiousideas.com/findlostkeys.htm. 2006.
[4]. KeyRinger, http://www.keyringer.com. 2006.
[5]. Now you can find it! http://www.sharperimage.com. 2006.
[6]. Peters, R.E., R. Pak, G.D. Abowd, A.D. Fisk, W.A. Rogers. ―Finding Lost Objects: Informing the Design of Ubiquitous Computing Services for the Home.‖ Georgia Tech GVU Technical Report: GIT-GVU 04-01. 2004.
[7]. Locator, The. Gloucestor Smart House. Bath Institute of Biomedical Engineering. Available at http://www.bath.ac.uk/ bime/projects/ smart/index.htm. [8]. Nelson, Randal and Green, Isaac, ―Tracking Objects Using Recognition‖, in International Conference on Pattern Recognition (ICPR02), Quebec City, Quebec, August 2002, Volume 2, 1025-1039.
[9]. Ma, Hongshen, and Paradiso, J.A. ―The FindIT Flashlight: Responsive Tagging Based on Optically Triggered Microprocessor Wakeup,‖ in Proc. of the UbiComp 2002, Gotenborg, Sweden, October 2002, pp. 160-167.
[10]. ―Lost and misplaced items and assistive devices in nursing homes: Identifying problems and technological opportunities through participatory design research‖, Oude Weernink, Fontys University of Applied Sciences, Fontys EGT – Centre for Healthcare and Technology, Eindhoven, The Netherlands, DOI: 10.3233/TAD-170179, Technology and Disability, vol. 29, no. 3, pp. 129-140, 6 February 2018.
[11]. Retrieval of Misplaced Items Using a Mobile Robot via Visual Object Recognition, Qi Wang, College of electrical engineering, Zhejiang University, Hangzhou, 310027, P. R. China, DOI: 10.1109/CYBER.2017.8446158, IEEE Xplore: 27 August 2018.
[12].] ―System and method to locate misplaced personal items‖ , Sany Abed.
[13].] L.sathish Kumar and A.padmapriya. Article: Prediction for Common Disease using ID3 Algorithm in Mobile Phone and Television. International Journal of Computer Applications 50(4):30-33, July 2012. [14].L. Sathish Kumar and A. Padmapriya, "Evidence
based subsequent disease extraction from EMR Health Record by Grade Measure," 2016 Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, 2016, pp. 1-5. doi: 10.1109/GET.2016.7916771.
[15].L. Sathish Kumar & A. Padmapriya (2019) Information Extraction and Prediction Using Partial Keyword Combination and Blends Measure, IETE Journal of Research, 65:2, 164-171, DOI: 10.1080/03772063.2017.1409666.
[16].Kumar, L. Sathish and Padmapriya, A., Disease Information Extraction from Healthcare Records Using CTA Matrix (January 8, 2016). Australian Journal of Basic and Applied Sciences, 10(2) Special 2016, Pages: 141-149.
[17].N Dige, E Maahr, Reduced Capacity in a Dichotic Memory Test for Adult Patients With ADHD, September 25, 2009, Volume: 13 issue: 6, page(s): 677-683.
A. Kodre, K. Tikone, M. Sonawane, P. Jare and P. Shinde, "Smart and Efficient Personal Car Assistant System," 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, Palladam, India, 2018, pp. 12-17. doi: 10.1109/I-SMAC.2018.8653752.
AUTHORS BIOGRAPHY:
Dr. L. Sathish Kumar is Assistant Professor of School of Computer Science and Engineering, VIT University, Bhopal, Madhya Pradesh. He has been 7 years of teaching experience in subjects related to Computer Science and Applications. He received his Ph.D in Computer Science from Alagappa University in 2017. He has authored 26 more research papers in international journal, conferences including SCI indexed, IEEE, ACM and Springer publications. He have been published 5 academic books entitle ―Desktop Publishing‖, ―Desktop Publishing Second Edition‖, ―Artificial Intelligence‖, ―Data Communication and Networking‖ and ―Java Programming‖. His areas of interest include Data Mining, Artificial Intelligence and Image Processing. He received Best Young Faculty Award from Novel Academy. Pondicherry.
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Dr. K SANTHI, Professor in the Department of Computer Science and Engineering at Sreenivasa Institute of Technology and Management Studies, Chittoor. Her fields of interests are Computer network security, Wireless Networks, Wireless Sensor Network etc. She has published 10 research papers in National/International conferences and 6 in International/national Journals. She is a member of IRED, ICSES, CSTA and IAENG. received her Ph.D in the Faculty of Information and Communication Engineering, Anna University Chennai, India in 2018. She received her MCA and M.Phil degree in 2002 and 2007 respectively. She received M.E (Computer Science and Engineering) degree in 2010. Presently she is working as Associate.
