INCREASING THE ROBUSTNESS OF CLASSIFICATION ALGORITHMS TO QUANTIFY LEAKS THROUGH OPTIMIZATION

Leaks in water pipeline networks have cost billions of dollars each year. Robust leak quantification (to detect and to localize) methods are needed to minimize the lost. We quantify leaks by classifying their locations using machine learning algorithms, namely Support Vector Machine and C4.5. The algorithms are chosen due to their high performance in classification. We simulate leaks at different positions at different sizes and use the data to train the algorithms. We tune the algorithm by optimizing the algorithms' parameters in the training process. Then, we tested the algorithms' models against real observation data. We also experimented with noisy data, due to sensor inaccuracies, that often happen in real situations. Lastly, we compared the two algorithms to investigate how accurate and robust they localize leaks with noisy data. We found that C4.5 is more robust against noisy data than SVM

EMERGENCY PROCESSES HANDLING IN URBAN AREA USING MODIFIED DIJKSTRA METHOD

Emergency Aid has a very vital role in saving the patient's life. The emergency process involves two stages, namely the pre-hospital and the hospital stage. Striving for the entire emergency process is to have the fastest response time. The initial part of emergency treatment (pre-hospital) is determining the shortest and fastest route to the hospital. In addition, the availability of the targeted hospital must also be considered. We modified Dijkstra's Algorithm to produce the shortest route and the fastest time by considering the availability of the targeted hospital to support the handling of the emergency process. The modification made to the Dijkstra algorithm replaces the weight of Dijkstra's distance with a quantity representing the congestion rate and distance. Besides, the event time is estimated to determine the status of the intended hospital. As a result, Dijkstra's modification method can produce a more efficient and faster route

Optimization of Access Point Positioning on Wi-Fi Networks Using the K-Means Clustering Method

Uneven distribution is a common problem in setting up access points where some areas have many signals colliding with each other and others have no signals at all (blank spots). As a result, proper access point positioning on the WI-FI network is required to optimize the number of access points used and the signal strength received while maintaining the same coverage area's functionality. In this study, signal strength measurement is used to obtain the estimated distance using the Received Signal Strength Indicator (RSSI) method. The server analyzes using the K-Means Clustering algorithm to cluster the observation area. The output of this classification is the mapping of dense regions (traffic) and loose regions to determine the coverage areas of each access point. This approach aims to optimize the placement of access points in terms of their number and specifications

BONE GRAFT SUBSTITUTES WITH BONE CURETTAGE FOR THE TREATMENT OF FIBROUS DYSPLASIA OF RIGHT TIBIA

Fibrous dysplasia (FD) is benign condition, with an ability to affect any bone in the body and has a wide spectrum of severity. It is a chronic condition of the skeleton where a portion of a bone develops abnormally. We report and discuss a case of six year old male patient suffering from fibrous dysplasia of right tibia, its diagnosis, and management including surgical intervention.  Bone graft substitute with bone curettage was performed after thorough excision of bone tumour and bone wash. Procedure was carried out under general anaesthesia and c-arm guidance.  Significant improvement was seen clinically and patient walked eight weeks after surgery, and was able to carry out routine activities normally with a significant improvement in quality of life. This was the latest surgical procedure available for the treatment of FD.Key words: Bone curettage, bone graft substitute with bone curettage, Fibrous dysplasia, quality of lif

Perancangan Audiometer Tutur Kata Berbasis Android dengan Penerapan Voice Recognition dalam Pelaksanaan Tes Pendengaran Pekerja

Sering terjadinya peningkatan kasus kecelakaan kerja, memberitahukan pentingnya penerapan K3 (Keamanan, Kesehatan dan Keselamatan kerja) pada perusahaan yang bertujuan menekan angka terjadinya kecelakaan di dalam bekerja. Salah satu penerapan K3 yakni diberlakukannya tes kesehatan. Tes kesehatan dilakukan untuk menghitung faktor resiko demi keselamatan dan efektifitas dalam pekerjaan yang digunakan sebagai pengukur untuk mendapatkan pekerja yang berkualitas dan sehat. Salah satu tes kesehatan yang dilakukan adalah tes pendengaran. Tes pendengaran merupakan prosedur yang dilakukan untuk memeriksa kemampuan mendengar seseorang. Salah satu tes pendengaran adalah Tes audiometri nada tutur. Tes pendengaran audiometri nada tutur menggunakan kata-kata terpilih yang telah dibakukan dan dikaliberasi, untuk mengukur beberapa aspek kemampuan pendengaran pekerja yang dilakukan secara konvensional.Namun dalam pengadaan tes secara konvensional menggunakan hardware audiometer masih kurang efesien untuk dilakukan selama proses tahap penerimaan calon peserta, hal ini dikarenakan perhitungan hasil tes keseluruhan masih dibuat secara manual oleh penyedia tes, sehingga diperlukan pengawas dari perusahaan untuk menghindari manipulasi data yang bisa diubah oleh penyedia dan peserta tes. Keterbatasan alat yang dimiliki oleh penyedia tes juga membuat pelaksaanaan tes menjadi kurang efisien dalam waktu dikarenakan harga audiomer yang terjangkau mahal. Melalui rancangan sistem audiometer nada tutur berbasis android yang menerapkan voice recognition diharapkan dapat membantu dalam pelaksaan tes pendengaran untuk pekerja. Selain itu, diharapkan dapat meminimalisir biaya yang dilakukan perusahaan untuk membeli alat audiometer atau menggunakan jasa ketiga dari pihak yang memiliki audiometer. Sistem audiometer nada tutur ini diberi nama dengan AudioTest Pro

A Video Streaming Application Using Mobile Media Application Programming Interface

Recently, the development of mobile phone technology is growing rapidly. These developments led to the emerging of a multimedia mobile phone that supports Wireless Local Area Network (WLAN). However, the use of WLAN technology on mobile phones to access the streaming video is very rarely employed, while the current S60 Symbian operating system as a multimedia mobile phone is very reliable in handling a video. This study presents the making of a video streaming application in mobile phone via a WLAN connection using JSR 135 technology or the Mobile Media API (MMAPI). MMAPI is used to control the process of video streaming and its features. The application uses the two protocols; RTSP and HTTP. Experiment results show that the use of MMAPI on Symbian 60 based mobile phones to do video streaming is feasible and has a good reliability as 0% packet loss on connection. In addition, the times required to play multimedia files are not affected by the size of video streaming files

An Enhanced Dynamic Signature Verification using the X and Y Histogram Features

Dynamic signature verification by using histogram features is a well-known signature forgery detection technique due to its high performance. However, this technique is often limited to angular histograms derived from vectors containing two adjacent points. We propose additional new features from the X and Y histograms to overcome the limitation.  Our experiments indicate that our technique produced Under Curve Area AUC values 0.80 to detect skilled forgery and 0.91 for random forgery. Our method performed best when the verification system uses 12 of the most dominant features.  This setup produced AUC values of 0.80 to detect skilled forgery and 0.93 for random forgery. These results outperformed the original technique when the X and Y histogram features are not used that produced AUC values of 0.78 to detect skilled forgery and 0.90 for random forgery

Deteksi Suhu Melalui Citra Termal Wajah Menggunakan Deep Learning

Dalam masa pandemi kasus penularan virus CORONA masih tetap bertambah dari hari ke hari. Salah satu gejala yang umum dialami oleh pasien COVID-19 adalah demam. Hal yang umum dilakukan untuk mengukur suhu di masa pandemi adalah menggunakan termometer non kontak. Deep Learning bisa digunakan untuk mendeteksi wajah dan membantu mendeteksi suhu maksimal wajah dari gambar termal. Tujuan Penelitian ini adalah membuat aplikasi pendeteksi suhu pada citra termal menggunakan pendekatan Deep Learning. Dalam Penelitian ini dilatih sebuah model deteksi SSD-MobileNet untuk mendeteksi area wajah dari citra termal. Setelah terdeteksi, data suhu diekstrak dari area wajah tersebut. Dalam pelaksanaan penelitian ini digunakan dataset citra termal Tuft Face Database, IRDatabase, dan citra termal yang diambil menggunakan Flir One. Dari hasil uji coba didapatkan hasil mean average precision deteksi wajah sebesar 0,95 dengan threshold dari evaluasi model untuk IoU 0,75 sebesar 0,95 dan mean absolute error deteksi suhu sebesar 1,51

AN ENHANCED SQL INJECTION DETECTION USING ENSEMBLE METHOD

SQL injection is a cybercrime that attacks websites. This issue is still a challenging issue in the realm of security that must be resolved. These attacks are very costly financially, which count millions of dollars each year. Due to large data leaks, the losses also impact the world economy, which averages nearly $50 per year, and most of them are caused by SQL injection. In a study of 300,000 attacks worldwide in any given month, 24.6% were SQL injection. Therefore, implementing a strategy to protect against web application attacks is essential and not easy because we have to protect user privacy and enterprise data. This study proposes an enhanced SQL injection detection using the voting classifier method based on several machine learning algorithms. The proposed classifier could achieve the highest accuracy from this research in 97.07%

COMMUNICATIVE AND PROFICIENT DATA ENTRÉE MANAGE FOR MULTI RIGHTS CLOUD

Cipher text-Policy ABE (Clubpenguin-ABE) is resourcefully designed meant for access control concerning encrypted data. It's one among the primary appropriate technologies meant for controlling of understanding access within cloud storage systems, because it offers the data owner additional direct control above access policies. The revocable multi-authority CPABE can be a capable technique, which may be functional in almost any distant storage systems furthermore to online social systems. We submit a revocable plan of multiauthority Clubpenguin-ABE structure that may support capable attribute revocation. Our plan doesn't necessitate server to obtain completely reliable, since key update is enforced by every attribute authority not server. Even though the server isn't semi-reliable in a number of scenarios, our schemes could assurance backward security and additionally forecasted structure is well-organized and incurs less computation outlay, that's safe and accomplishes backward security furthermore to forward security. Within our novel attribute revocation method, only ciphertexts which are associated with revoked attribute must be modernized